Quantum Techniques in Machine Learning

Contribution List

269. Quantum machine learning advantages beyond hardness of evaluation

Riccardo Molteni (Leiden University)

07/12/2026, 11:00

Talk

Contributed talks

Recent years have seen rigorous proofs of quantum advantages in machine learning, particularly when data is labeled by cryptographic or inherently quantum functions. These results typically rely on the infeasibility of classical polynomial-sized circuits to evaluate the true labeling function. While broad in scope, these results however reveal little about advantages stemming from the actual...

96. Efficient Learning of Structured Quantum Circuits via Pauli Dimensionality and Sparsity

Sabee Grewal (IBM Quantum, Columbia University)

07/12/2026, 11:20

Talk

Contributed talks

We study the problem of efficiently learning an unknown $n$-qubit unitary channel in diamond distance given query access. We present a general framework showing that if Pauli operators remain low-complexity under conjugation by a unitary, then the unitary can be learned efficiently. This framework yields polynomial-time algorithms for a wide range of circuit classes, including $O(\log \log...

268. Towards robust quantum advantage in sequence learning with a single reliable gate

Jonathan Lu (Massachusetts Institute of Technology)

07/12/2026, 11:40

Talk

Contributed talks

While most quantum algorithms either offer sampling advantages with zero fault tolerance or computational advantages with universal fault tolerance, early fault-tolerant quantum devices occupy an intermediate regime in which substantial but not fully universal error-corrected computation is possible. This contrast motivates the question of whether quantum-classical separations can be achieved...

104. Distributed Quantum Property Testing with Communication Constraints

Mr Chirag Wadhwa (University of Edinburgh)

07/12/2026, 12:00

Talk

Contributed talks

We introduce a framework for distributed quantum inference under communication constraints. In our model, $m$ distributed nodes each receive one copy of an unknown $d$-dimensional quantum state $\rho$, before communicating via a constrained communication channel with a central node, which aims to infer some property of $\rho$. This framework generalizes the classical distributed inference...

207. Provable and scalable quantum Gaussian processes for quantum learning

Jonas Jäger

07/12/2026, 12:20

Talk

Contributed talks

Quantum machine learning has become increasingly popular, but despite tremendous efforts, some of its central issues like trainability, scalability, poor local minima, interpretability, and reliable generalization from limited quantum data still remain. This motivates a different organizing principle for quantum learning, where the model is not chosen as a flexible ansatz, but is instead...

155. Covert Quantum Learning: Privately and Verifiably Learning from Quantum Data

Abhishek Anand (Caltech), Ari Karchmer (Morgan Stanley), Matthias C Caro (University of Warwick), Saachi Mutreja (Columbia University)

07/12/2026, 14:30

Talk

Contributed talks

Quantum learning from remotely accessed quantum compute and data must address two key challenges: verifying the correctness of data (verifiability) and ensuring the privacy of the learner’s conclusions (covertness). Inspired by the classical covert (verifiable) learning model of Canetti and Karchmer (TCC 2021), we propose the first models of covert verifiable learning in quantum learning...

59. Cloning is as Hard as Learning for Stabilizer States

Nikhil Bansal (Department of Computer Science, University of Warwick), Dr Matthias C. Caro (Department of Computer Science, University of Warwick), Dr Gaurav Mahajan (Department of Computer Science, Yale University)

07/12/2026, 14:50

Talk

Contributed talks

The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation errors, cloning an arbitrary unknown pure state requires as many initial copies as needed to fully learn the state. Rather than arbitrary unknown states, modern quantum learning theory often considers structured classes of states and exploits such...

280. Learning Arbitrary Lindbladians with Quantum Error Correction

Mr Petr Ivashkov (ETH Zürich)

07/12/2026, 15:40

Talk

Contributed talks

We study ansatz-free Lindbladian learning, the problem of reconstructing the generator of an open quantum system without prior knowledge of its Hamiltonian or dissipator structures. This problem exhibits two distinct information-theoretic precision limits: Hamiltonian components unmasked by dissipation are Heisenberg-limited, while the remaining Lindbladian components are subject to the...

251. Learning scalable entanglement distribution policies

Christos Chalkias (Leiden University)

07/12/2026, 16:10

Talk

Contributed talks

A quantum internet must distribute entanglement across imperfect repeaters, where the order in which each node waits, swaps, or purifies its links sets how fast and how reliably it is delivered. Choosing that order is a sequential decision problem: the dominant "swap-as-soon-as-possible'' (swap-asap) heuristic is optimal only for perfect hardware, and learned policies so far are tied to a...

272. Decoded Quantum Interferometry is Obstructed by Topological Structure

Eric Anschuetz (Caltech), Jonathan Lu (Massachusetts Institute of Technology)

08/12/2026, 09:40

Talk

Contributed talks

We study the performance of Decoded Quantum Interferometry (DQI) on typical instances of MAX-$k$-XOR-SAT. We prove that if the decoding step of DQI corrects up to the maximum threshold of the best known computationally efficient decoders (e.g. belief propagation), then DQI is obstructed by a topological feature of the near-optimal space of solutions known as the overlap gap property (OGP). We...

118. Canonical quantization of neurons

Mark Wilde (Cornell University)

08/12/2026, 10:10

Talk

Contributed talks

Canonical quantization provides a systematic procedure for constructing
quantum models from classical Hamiltonians. Here, we apply this principle to a fundamental computational primitive of machine learning: the neuron. Specifically, by viewing a neuron as a composition of an energy function and an activation function, we quantize this model by replacing the energy function with a quantum...

102. Complexity of Normalized Persistence Problems for Topological Data Analysis and Local Hamiltonians

Dominic Lowe (Imperial College London), Ryu Hayakawa (Kyoto University)

08/12/2026, 11:00

Talk

Contributed talks

Topological data analysis (TDA) is a machine learning technique that uses topology to extract patterns from data and has shown the potential to exhibit quantum advantage. A key concept in TDA is persistent homology, which measures the robustness of topological information at different lengthscales. In this paper, we introduce and study the problem of normalized persistence, a practically...

197. Quantum principal component analysis without eigenvector recovery

Yewei Yuan (Shanghai Jiao Tong University)

08/12/2026, 11:20

Talk

Contributed talks

Principal component analysis (PCA) is traditionally implemented through a covariance or kernel matrix, leading-eigenvector extraction, and hard rank-k projection. These steps can be computationally costly in high-dimensional and quantum-data settings, sensitive to small eigengaps, and unnecessary when downstream tasks only require principal-subspace scores. Such score-based objectives are...

202. On the Query Complexity of Quantum Mean Estimation for Multivariate Heavy-Tailed Distributions

Bin Luo (The Chinese University of Hong Kong)

08/12/2026, 11:40

Talk

Contributed talks

We study quantum mean estimation for multivariate heavy-tailed distributions. Specifically, we consider random vectors whose $p$-th central moment is bounded for some $p\in(1,2]$; this includes the bounded-variance case when $p=2$, while allowing infinite variance when $p<2$. We first propose a quantum heavy-tailed mean estimator ($\texttt{QHTME}$), for estimating the mean of random variables...

18. An unbounded quantum memory advantage for reinforcement learning world models

Dr Hailan Ma (Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore and Centre for Quantum Technologies), Dr Josep Lumbreras (Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University and Centre for Quantum Technologies, Singapore)

08/12/2026, 12:00

Talk

Contributed talks

World models allow reinforcement-learning agents to evaluate actions in simulation before acting. For planning, however, reproducing typical simulated trajectories is not enough: the model must sample futures conditioned on a specified history and candidate action. This distinction is critical in safety settings, where a rare history, such as a pedestrian suddenly entering the road, can...

309. Representation theory for passive linear optics: anti-concentration, barren plateaus and classical simulability

Hela Mhiri (LIP6, EPFL)

08/12/2026, 12:20

Talk

Contributed talks

Photonic quantum systems are a promising platform for near-term quantum technologies, combining low noise, high-fidelity linear-optical operations, and scalability to large numbers of optical modes. In this work, we develop the first general framework for computing second moments of particle-number-preserving bosonic observables under Haar-random passive linear-optical transformations....

80. Private training in quantum machine learning

Tigran Sedrakyan (LIP6)

08/12/2026, 14:50

Talk

Contributed talks

With the emergence of machine learning (ML) models trained on large datasets containing potentially sensitive data, a major question in AI safety is how to make learning private with respect to the training data. Similar to classical machine learning, quantum machine learning (QML) models are not devoid of privacy vulnerabilities. Differential privacy (DP) is a standard tool for training ML...

88. Quantum Convolutional Neural Network for Phase Recognition in Two Dimensions

Leon C. Sander (Friedrich-Alexander-Universität Erlangen-Nürnberg)

09/12/2026, 09:40

Talk

Contributed talks

Quantum convolutional neural networks (QCNNs) are quantum circuits for characterizing complex quantum states. They have been proposed for recognizing quantum phases of matter at low sampling cost and have been designed for condensed matter systems in one dimension. Here we construct a QCNN that can perform phase recognition in two dimensions and correctly identify the phase transition from a...

90. Automated near-term quantum algorithm discovery for molecular ground states

Konstantinos Meichanetzidis (Quantinuum)

09/12/2026, 10:00

Talk

Contributed talks

Abstract submission for a talk.

318. Hybrid quantum-classical neural network for sample-efficient recognition of topological phases

Markus K. Hoffmann (Friedrich-Alexander Universität Erlangen-Nürnberg)

09/12/2026, 10:20

Talk

Contributed talks

With increasing maturity of quantum computers, standard methods for characterizing global properties of their output quantum states via direct measurements and classical post-processing are becoming increasingly impractical due to large measurement costs. Here, we introduce, numerically benchmark and experimentally realize a hybrid quantum-classical neural network, consisting of a shallow...

201. Learning to Reconstruct Wigner Functions in Phase Space

Ms Xinyu Tang (Shanghai Jiaotong University), Yi-hsin Lin

09/12/2026, 11:10

Talk

Contributed talks

Wigner function learning is a central tool for characterizing continuous variable quantum systems. A fundamental challenge in this setting is to infer a continuous phase-space function from sparse pointwise measurement data, a task that becomes increasingly demanding as the effective dimension enlarges. Here, we develop a general machine learning framework to reconstruct Wigner functions...

230. Harnessing imperfect quantum data through domain adaptation

Kosuke Ito (Toyota Motor Corporation)

09/12/2026, 11:30

Talk

Contributed talks

Classical supervised learning from quantum measurements can extract useful physical information. However, regimes of interest often lack reliable labels and involve imperfect state preparation, while labels may be available only in easier regimes with different distributions. Can unlabeled measurement data from imperfectly prepared states be combined with these labeled data to learn an...

216. One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

Mr Sergio García Heredia (aQa, LION, Leiden University, Leiden, The Netherlands)

09/12/2026, 11:50

Talk

Contributed talks

Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods. Yet their optimization remains comparatively...

253. Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model

Joao F. Doriguello (HUN-REN Alfréd Rényi Institute of Mathematics)

09/12/2026, 12:10

Talk

Contributed talks

Reinforcement learning is a subfield of machine learning that studies how an agent interacts with an environment in order to extract as large a reward as possible. A standard approach to study such interaction is through Markov Decision Processes (MDPs) and the choice for an optimal policy --- a function that tells the agent which action to take. In this work, we study two types of MDPs ---...

184. Machine-learned syndrome post-selection for reliable quantum error correction

Askery Canabarro (Technology Innovation Institute)

09/12/2026, 14:30

Talk

Contributed talks

Quantum error correction can be enhanced by rejecting runs that are likely to fail logically, but the most informative confidence metrics often require costly decoder-level likelihood information. We introduce a decoder-agnostic post-selection method that learns an abort score directly from syndrome data. A supervised classifier is trained only to distinguish syndromes sampled from low- and...

312. Multi-Agent Reinforcement Learning for Context-Aware Real-Time Error Suppression

Arthur Strauss (National University of Singapore), Mr Lukas Voss (Ulm University)

09/12/2026, 14:50

Talk

Contributed talks

Quantum processors are limited by coherent errors that depend on the circuit context in which each gate operates, a dependence that conventional calibration protocols do not capture because they characterize gates in isolation. We present CARTES (Context-Aware RealTime Error Suppression), a framework that applies model-free multiagent reinforcement learning (MARL) to adapt pulse-level gate...

42. Feed-Forward-Free Logical Two-Qubit Randomized Benchmarking with Steane QEC and Residual Machine-Learning Decoders

Jan Balewski (NERSC/LBNL, USA)

09/12/2026, 15:40

Talk

Contributed talks

Machine-learning residual decoders improve Steane QEC in a feed-forward-free logical two-qubit randomized benchmarking (RB) experiment on neutral-atom platforms such as QuEra, where mid-circuit conditional gates are unavailable. Two logical qubits are encoded in Steane [[7,1,3]] blocks and initialized via the Goto fault-tolerant procedure. Logical two-qubit Clifford RB is applied...

113. AQER: A Scalable and Efficient Data Loader for Digital Quantum Computers

Kaining Zhang (Nanyang Technological University)

09/12/2026, 16:00

Talk

Contributed talks

Digital quantum computers promise computational capabilities beyond classical systems, but scarce qubits and limited coherence make data loading a persistent bottleneck. Approximate quantum loaders (AQLs) address this bottleneck by trading preparation fidelity for circuit complexity. We develop AQER, an AQL method that constructs loading circuits through maximal entanglement reduction. We...

32. Breaking concentration barriers for quantum extreme learning on digital quantum processors

Timothée Dao (IBM Research Europe - Zurich), Francesco Tacchino (IBM Research Europe - Zurich)

09/12/2026, 16:20

Talk

Contributed talks

We present a large-scale experimental realization of quantum extreme learning machines (QELMs) on superconducting quantum processors, scaling up to 124 qubits and circuits with more than 5,000 two-qubit gates. While quantum reservoir approaches offer enhanced expressivity via high-dimensional Hilbert spaces, their scalability can be hindered by hardware noise and concentration phenomena that...

219. Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings

Prof. Nir Weinberger (Technion-Israel Institute of Technology), Sreejith Sreekumar (L2S, CNRS, CentraleSupelec, University Paris-Saclay)

09/12/2026, 16:40

Talk

Contributed talks

Maximum likelihood prediction (MLP) is a core task at the heart of modern large language models. Here, we study a quantum version of this task for a simplified data model consisting of independent and identically distributed samples, as a first step. The quantum maximum likelihood predictor (QMLP) is obtained by embedding of empirical probability distributions into quantum states and...

61. Fragmentation is Efficiently Learnable by Quantum Neural Networks

Mikhail Mints (Caltech)

10/12/2026, 09:40

Talk

Contributed talks

In certain classes of physical quantum systems, the exponentially large state space "fragments" into many low-dimensional, dynamically disconnected subspaces. We introduce a learning problem known as fragment classification, where given a quantum state input, one is interested in classifying to which subspace the state belongs. We prove that solving this learning problem is efficient on a...

212. Training Variational Quantum Algorithms Is NP-hard, Even Locally

Triscia Mundo (Singapore University of Technology and Design)

10/12/2026, 10:10

Talk

Contributed talks

It is well known that globally optimizing the training objectives of Variational Quantum Algorithms (VQAs) is $\mathsf{NP}$-hard. This motivates the use of local optimization methods in practice, raising the question of whether finding locally optimal parameters is computationally easier. We show that it is not: finding a local optimum for VQA training is also $\mathsf{NP}$-hard. Our proof...

126. Warm Starts, Cold States: Exploiting Adiabaticity for Variational Ground-States

Ricard Puig (École Polytechnique Fédérale de Lausanne (EPFL))

10/12/2026, 11:00

Talk

Contributed talks

This submission is based on [arXiv:2602.06137]

Reliable preparation of many-body ground states is an essential task in quantum computing, with applications spanning areas from chemistry and materials modeling to quantum optimization and benchmarking. A variety of approaches have been proposed to tackle this problem, including variational methods. However, variational training often struggle...

160. Challenges in Barren Plateau Mitigation with Dynamic Parameterized Quantum Circuits

Sumeet Shirgure (University of Central Florida)

10/12/2026, 11:20

Talk

Contributed talks

Variational quantum algorithms (VQAs) are a promising paradigm for quantum advantage, yet their trainability is severely hampered by barren plateaus (BPs). Several works have proposed using dynamic parameterized quantum circuits (DPQCs) which intersperse unitary layers with parameterized CPTP maps (e.g. engineered dissipation, feedforward gadgets, or periodic resets), as a potential route...

279. Lower bounding the MaxCut of high girth 3-regular graphs using the QAOA

Benjamin Villalonga (Google Quantum AI)

10/12/2026, 11:40

Talk

Contributed talks

We study MaxCut on 3-regular graphs of minimum girth $g$ for various $g$'s. We obtain new lower bounds on the maximum cut achievable in such graphs by analyzing the Quantum Approximate Optimization Algorithm (QAOA). For $g\geq16$, at depth $p \geq 7$, the QAOA improves on previously known lower bounds. Our bounds are established through classical numerical analysis of the QAOA's expected...

152. Reconciling Theory and Practice: Quadratic Acceleration of Variational Quantum Algorithms with Rotosolve

Sayantan Pramanik (TATA Consultancy Services, Indian Institute of Science)

10/12/2026, 12:00

Talk

Contributed talks

The Rotosolve algorithm leverages explicit knowledge about the objective function to perform exact minimisation along one coordinate at a time. Recently, we established its convergence, and derived explicit rates which match those of gradient-based methods. However, since Rotosolve uses additional information about the objective function, it is expected to outperform generic gradient-based...

45. QAOA-MaxCut has barren plateaus for almost all graphs

Dr Rui Mao (School of Computer Science and Technology, University of Chinese Academy of Sciences), Dr Pei Yuan (Tencent Quantum Lab)

10/12/2026, 12:20

Talk

Contributed talks

See attached PDF file for extended abstract.

281. IQP Born Machines under Data-dependent and Agnostic Initializations

Supanut Thanasilp (Chulalongkorn University)

10/12/2026, 14:30

Talk

Contributed talks

Instantaneous quantum polynomial-time (IQP) circuits provide a natural framework for quantum generative modeling and are believed to be classically hard to sample from in certain regimes. We study the optimization landscape of IQP-based quantum circuit Born machines trained with Maximum Mean Discrepancy (MMD) losses constructed from low-body Pauli-$Z$ correlators. We prove that random...

239. Classically trainable quantum generative modeling with Boson and Fermion Sampling

Zoltán Kolarovszki (HUN-REN WIgner Research Centre for Physics), Zoltán Zimborás (University of Helsinki)

10/12/2026, 14:50

Talk

Contributed talks

Sampling-based quantum advantage schemes currently serve as the leading experimental demonstrations of computational tasks deemed intractable for classical devices. However, their practical significance remains a subject of intense debate as these tasks are synthetically designed to maximize classical hardness rather than to solve meaningful real-world problems. In this contribution, we...

224. Instantaneous Quantum Polynomial Circuit Born Machines: Trainability, Non-Simulability, Expressivity and Applications

Kevin Shen

10/12/2026, 15:40

Talk

Contributed talks

Instantaneous Quantum Polynomial Quantum Circuit Born Machines (IQP-QCBM) are emerging quantum generative models that have attracted increasing attention. IQP-QCBMs represent probability distributions over bitstrings through Born-rule measurements of parameterized IQP circuits. IQP-QCBMs are distinguished from many other quantum machine learning (QML) models in two ways: First, the models are...

303. Classically trained, quantum-deployed generative models: universality and kernel-adaptive training

Andrii Kurkin (Leiden Universiteit)

10/12/2026, 16:00

Talk

Contributed talks

A fundamental bottleneck holds back quantum machine learning: heuristic quantum methods cannot yet be meaningfully tested, since current hardware cannot run them at interesting scales. The train-classically, deploy-quantumly paradigm offers a seemingly impossible guarantee: the training objective is evaluated efficiently on a classical computer, while sampling from the trained model may remain...

183. Spectral Born machines: Classically trainable quantum generative models for discrete data

Joseph Bowles (Xanadu)

10/12/2026, 16:20

Talk

Contributed talks

We present \emph{spectral Born machines}, a class of quantum generative models that results from viewing and generalizing the class of IQP Born machines through the lens of group Fourier analysis. These quantum models exploit the quantum Fourier transform in order to induce smoothness priors that make them naturally suited to learning integer-structured data, while remaining classically hard...

158. Quantum thermodynamics and semidefinite optimization: Boltzmann, Fermi-Dirac, and Bose-Einstein frameworks

Michele Minervini (Cornell University)

10/12/2026, 16:40

Talk

Contributed talks

We show how quantum thermodynamics offers a unifying interpretation for a wide class of semidefinite programs (SDPs) that arise in quantum information. Three SDP variable constraints, namely trace-one density operators, operator-bounded measurements, and the unbounded positive semidefinite cone, admit thermodynamic regularizations associated with Boltzmann, Fermi-Dirac, and Bose-Einstein...

140. Hamiltonian learning via quantum Zeno effect

Egle Pagliaro (ICFO - The Institute of Photonic Sciences)

11/12/2026, 09:00

Talk

Contributed talks

Determining the Hamiltonian of a quantum system is essential for understanding its dynamics and validating its behavior. Hamiltonian learning provides a data-driven approach to reconstruct the generator of the dynamics from measurements on the evolved system, enabling the characterization and learning of quantum devices from experimental data. Among its applications, it is particularly...

137. Reversing Unknown Hamiltonian Evolutions through Algebraic Structure

Jizhe Lai (The Hong Kong University of Science and Technology (Guangzhou)), Mingrui Jing (The Hong Kong University of Science and Technology (Guangzhou)), Erdong Huang (The Hong Kong University of Science and Technology (Guangzhou))

11/12/2026, 09:30

Talk

Contributed talks

Coherent inverse access is needed in amplitude amplification, time-reversal tests, and scrambling probes, but exact black-box inversion of an unknown $d$-dimensional unitary has quadratic query cost. We ask what changes when the oracle is a Hamiltonian evolution $U(x)=\exp(i\sum_j x_j H_j)$ with known generators and unknown couplings. In a compiled-structure query model, fixed gates may depend...

259. Imposing Constraints on Driver Hamiltonians and Mixing Operators: From Theory to Practical Implementation

Hannes Leipold (Fujitsu Research of America)

11/12/2026, 09:50

Talk

Contributed talks

We present a general algebraic framework for constructing constraint preserving driver Hamiltonians and mixing operators, a central challenge in constrained quantum optimization. We characterize the operators that preserve embedded classical constraints, establish the computational complexity of finding them, and then develop practical algorithms for constructing them when locality bounds are...

322. Learning k-body Hamiltonians via sublinear-time sparse recovery

Franz J. Schreiber (Freie Universität Berlin)

11/12/2026, 10:10

Talk

Contributed talks

We present a protocol for learning an unknown $n$-qubit Hamiltonian
supported on Pauli strings of weight at most $k$, with what is, to our
knowledge, the first instance-optimal $\ell_2/\ell_2$ recovery guarantee
that is simultaneously efficient in total evolution time, measurement
count, and classical decoding time, all scaling sublinearly in the
$O(n^k)$-dimensional ambient space. The...

97. Nearly Time-Optimal Pure State Tomography with Pauli Measurements

Sabee Grewal (IBM Quantum, Columbia University)

11/12/2026, 11:00

Talk

Contributed talks

We give an algorithm for pure state tomography with near-optimal copy and time complexity using only single-qubit measurements. Specifically, given $\widetilde{O}(2^n/\epsilon)$ copies of an unknown $n$-qubit pure state $|\psi\rangle$, the algorithm performs only \textit{nonadaptive Pauli measurements}, runs in time $\widetilde{O}(2^n/\epsilon)$, and outputs $|\widehat{\psi}\rangle$ with...

218. A hidden bottleneck in classical and quantum linear reservoir computing

Francesco Arzani (INRIA Paris, ENS-PSL)

11/12/2026, 11:20

Talk

Contributed talks

We identify a hidden bottleneck in the information processing capacity of linear reservoir computers. When the measured features evolve linearly in the reservoir and the output is formed by linear readout with bias, we show that the capacity available at any fixed delay is limited by what is already present in the preprocessed input. Linear reservoir dynamics can therefore redistribute...

25. Efficient Characterization of Coherent and Correlated Low-Degree Noise in Layers of Gates

Marianna Crupi (Max Planck Institute of Quantum Optics)

11/12/2026, 11:40

Talk

Contributed talks

Quantum process tomography provides complete information about the dynamics of a quantum system, but its practical use is severely limited by the exponential resources required to characterize generic quantum channels. This challenge is particularly relevant for the characterization of noise in quantum devices, where understanding coherent and correlated errors is essential for benchmarking,...

112. Measurements as a design principle for quantum reservoirs: enhancing and enabling online temporal learning

Giacomo Franceschetto (ICFO - The Institute of Photonic Sciences)

11/12/2026, 12:00

Talk

Contributed talks

Quantum reservoir computing offers a near-term route to temporal learning by using fixed quantum dynamics as a processor and training only a classical readout. However, moving from classical to quantum reservoirs introduces two central difficulties: the need for an input-update mechanism that provides nonlinearity and fading memory, and the tension between extracting observables and preserving...

227. Kicked-Ising Online Quantum Reservoirs: Operating Regimes and Hardware Tests

Ahmet Ege Yilmaz

11/12/2026, 12:20

Talk

Contributed talks

Quantum reservoir computing processes temporal data with fixed quantum dynamics and a trained classical readout. We convert the hardware-tested kicked-Ising circuit family used in dual-unitary chaos simulations and large-scale quantum extreme learning machines into an online quantum reservoir. Unlike feedforward QELM feature maps, the circuit is recurrent. A visible register is measured and...

47. Optimal randomized measurements for a family of non-linear quantum properties

Yifan Tang

11/12/2026, 12:40

Talk

Contributed talks

Quantum learning encounters fundamental challenges when estimating non-linear properties, owing to the inherent linearity of quantum mechanics. Although recent advances in single-copy randomized measurement protocols have achieved optimal sample complexity for specific tasks like state purity estimation, generalizing these protocols to estimate broader classes of non-linear properties without...

298. Temporal information processing on a 4,500-qubit quantum annealer

Mr Antonio Sannia (Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC; Theoretical Division, Los Alamos National Laboratory;USRA Research Institute for Advanced Computer Science (RIACS))

11/12/2026, 14:10

Talk

Contributed talks

Quantum machine learning could uncover statistical structure beyond the reach of classical models, but realizing this potential requires scaling quantum models to large, expressive regimes while still reading out useful information at acceptable cost. Most approaches rely on optimizing many quantum parameters, a process hampered by expensive training loops and vanishing gradients. Here we...

267. Sampling from Thermal Quantum States via Pauli Propagation

Michele Minervini (Cornell University)

11/12/2026, 14:30

Talk

Contributed talks

Pauli propagation is a powerful framework for simulating quantum systems by tracking the evolution of Pauli operators, yet its scope has so far been limited to estimating expectation values. We extend it to sampling from quantum states, introducing an algorithm that draws bitstrings directly from truncated Pauli expansions. While the very truncation that makes Pauli propagation efficient...

288. Quantum simulation in the Heisenberg picture: Algorithms and Learning Separations

Shao Hen Chiew (École Polytechnique Fédérale de Lausanne (EPFL))

11/12/2026, 14:50

Talk

Contributed talks

In the first work of this joint submission (Ref. 18), we introduce a general framework for quantum simulation in the Heisenberg picture using quantum computers. Central to our approach are the vectorization and transfer matrix formalisms, which we exploit to encode Heisenberg operators $O(t)= e^{iHt} O e^{-iHt}$ as quantum states $|{O(t)}\rangle\!\rangle$ in a structure-preserving manner. As a...

114. Stochastic Pauli-Path Simulator for Large-Scale Quantum Optimization

Dr Kaining Zhang (Nanyang Technological University)

11/12/2026, 15:10

Talk

Contributed talks

Pauli-based simulation offers a promising route to large-scale classical simulation of quantum circuits in structured regimes, but existing truncation-based Pauli simulators are mainly designed for forward expectation estimation. When embedded in gradient-driven quantum optimization, these truncations can produce systematically biased gradients and distorted optimization trajectories. We...

220. A Basis-Dependent Kernel for Learning Quantum Phases of Matter

Clemens Lindner (University of Jyväskylä)

Poster

The existence and unraveling of different phases of matter for various configurations of the same system constitutes a central task in quantum many body physics. Recent developments addressed this problem from data, by studying the differences between states, e. g. through fidelity measures, and inferring phase transitions from them. We introduce the IPR-kernel, a kernel method built for...

99. A Compass on the Quantum State Sphere: The Hopf Ansatz for Arbitrary Pure-State Optimization

Ruge Lin

Poster

Optimizing arbitrary quantum state vectors is like navigating the unit sphere in Hilbert space: beyond target reachability, optimization asks for coordinates, local distance information, and measurable directions. We introduce the Hopf ansatz, a binary-tree circuit for arbitrary normalized real and complex state vectors. Internal angles steer probability between subtrees, leaf phases carry the...

203. A continuous-variable photonic memristor as a reinforcement learner

Dean Brand

Poster

Abstract attached.

58. A Givens-Exchange Ansatz for Molecular Variational Eigensolvers

Dr Azadeh Alavi (RMIT University), Prof. Muhammad Usman

Poster

Variational quantum eigensolvers require ansatzes that are expressive, trainable, and implementable on constrained quantum hardware. Quantum architecture search can discover compact circuits, but the learned topology is obtained only after an additional search process. We present a deterministic Givens-exchange ansatz for molecular VQE that removes this outer architecture-search loop while...

30. A Hybrid E91-based QKD Protocol with PNS-Resistance and Location-Based Authentication

HAMZA ABAAKIL (Mohammed V University of Rabat)

Poster

We propose a hybrid quantum key distribution protocol based on the E91
entanglement scheme that combines sifting-less reverse reconciliation with
quantum position verification (QPV). The protocol removes both the classical
sifting step and the need for pre-shared authentication keys, enabling a more
self-contained QKD architecture. We derive a composable security bound
ε
total ≈ 2 ×...

315. A Hybrid Quantum-Classical Approach to Unsteady MHD Nanofluid Flow: Benchmarking HHL and VQLS Linear Solvers

Serai Mosala (Nelson Mandela University)

Poster

This work presents a hybrid quantum-classical framework for solving the linear systems arising from discretised unsteady magnetohydrodynamic (MHD) nanofluid Couette flow equations. The classical component employs a Bivariate Spectral Quasilinearisation Method (BI-SQLM) and Crank-Nicolson finite difference scheme to solve the coupled momentum and energy equations with partial slip and...

20. A hybrid quantum-classical particle-in-cell method for plasma simulations

Pratibha Raghupati Hegde (International Institute of Information Technology, Hyderabad)

Variational QML

Poster

We present a hybrid quantum-classical electrostatic Particle-in-Cell (PIC) method, where the electrostatic field Poisson solver is implemented on a quantum computer simulator using a hybrid classical-quantum Neural Network (HNN) using data-driven and physics-informed learning approaches. The HNN is trained on classical PIC simulation results and executed via a PennyLane quantum simulator. The...

306. A journey through Flatland: What does the antiflatness of a spectrum teach us?

Barbara Jasser (Scuola Superiore Meridionale)

Poster

Our work provides a resource-theoretic framework for asking when quantum states are easy or hard to learn because of the spectral organization of their entanglement and magic resources. We introduce antiflatness as a characterization of the entanglement spectrum. While standard entanglement measures, such as the von Neumann entropy or the purity, only capture average properties of the...

154. A Mathematical Investigation of Quantum Kernel Methods for Non-Linear Classification

Lindokuhle Ncini (Sol Plaatje University)

Poster

Kernel methods are fundamental to supervised machine learning because they enable the classification of non-linearly separable data through implicit mappings into higher-dimensional feature spaces. Recent advances in quantum machine learning have introduced quantum kernel methods, in which classical data are encoded into quantum states using parameterised quantum feature maps and similarity...

51. A Narrow but Real Regime: Shot-Budget-Matched Quantum Reservoir Forecasting that Classical Reservoirs Miss

Tushar Pandey

Poster

We ask a shot-budget-matched question: at a fixed M shots per timestep, is there a regime where quantum reservoir computing (QRC) beats classical reservoirs sharing the same budget, for a mechanistic reason? A quantum reservoir maps a k-body input observable onto a local Z probe whose shot variance is ~1/M regardless of body count, while a classical shadow estimator pays $3^k/M$; this predicts...

295. A noise robust approach for solving the state hidden subgroup problem on cyclic groups with a quantum computer

Adrián Pérez-Salinas (ETH Zürich - UPM), Jose Carrasco (Free University Berlin), Jens Eisert (Freie Universität Berlin)

Poster

Quantum computers can solve certain problems out of the reach of their classical counterparts. Among these problems, the (state) hidden subgroup problem -- StateHSP -- has been extensively studied and lies at the core of examples of quantum-classical separations. Solving the StateHSP requires unveiling the structure of a group, and most methods are highly sensitive to noise. In this work, we...

193. A quantum-inspired spiking variational autoencoder for robust anomaly detection at the LHC

Dean Brand

Poster

Abstract attached.

246. A qubit-efficient solver for the Low Autocorrelation Binary Sequences problem

Marco Sciorilli (Technology Innovation Institure)

Poster

Pauli Correlation Encoding (PCE) is a qubit-efficient variational approach to combinatorial optimizations. It offers a polynomial reduction in qubit count and a super-polynomial suppression of barren plateaus. We extend the PCE-based framework to the Low Autocorrelation Binary Sequences (LABS) problem, a notoriously hard benchmarking problem for classical and quantum solvers. We simulate the...

213. A trainability-neutral occupation-entropy regularizer against mode collapse in quantum generative models

Jian Feng Kong (Institute of Advanced Intelligence and Computing, A*STAR)

Poster

Quantum generative models face two well-known obstacles: barren plateau which affects the trainability of the model; and mode collapse is the failure of a trained model to reproduce all of the target distribution's modes. The two are sometimes linked, because the low-resolution cost functions used to avoid barren plateaus are also the ones least able to tell which modes carry probability. In...

138. A Transferable Machine Learning Approach to Predict Quantum Circuit Parameters for Electronic Structure Problems

Jonas Motyl (University of Augsburg)

Poster

The individual optimization of quantum circuit parameters remains one of the main practical bottlenecks in variational quantum eigensolver (VQE) calculations for electronic structure problems. To address this challenge, several machine learning approaches have been proposed. However, most existing methods focus on learning parameters tailored to individual molecules, either for a single...

83. Active learning for data-efficient modelling of quantum phase spaces

Jacob Swain (Quantinuum)

Poster

Neural networks provide a powerful framework for learning quantum states directly from measurement data across extended regions of parameter space. However, acquiring the data required to train these models can represent a significant cost. In this work, we introduce an active learning strategy for improving data acquisition for training conditional generative models. Based on a...

167. Adaptive directional gradients for parameterised quantum circuits

Brian Coyle (University of Edinburgh @ Fujitsu)

Poster

Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale. In this work, we propose a framework of forward gradient estimators for PQCs, based on the forward mode of automatic...

240. Addressing Quantum Many-Body Problems via Analog Devices

Vinícius Mohr (ETH Zurich)

Poster

Neural quantum states are among the most expressive variational ansätze for quantum many-body problems, yet evaluating them incurs a digital computational cost that grows with network size. We propose realizing the ansatz directly in analog hardware, using experimentally demonstrated devices as variational wavefunctions within the variational Monte Carlo (VMC) framework. We study two...

16. Advancing Differentiable Quantum Architecture Search via Topological Constraints, Gate Memory and Surrogate Neural Networks

David Kral (University of Southampton)

Poster

Variational quantum algorithms are widely used in quantum computing but rely heavily on manually designed circuit architectures. Quantum Architecture Search (QAS) addresses this bottleneck by automating the circuit design process using classical machine learning. In this work, we propose novel QAS approaches that advance differentiable QAS by integrating architectural constraints, sequential...

36. Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms

Abhishek Sadhu (University of Birmingham)

Poster

Distributed quantum algorithms offer a promising pathway to scale variational quantum algorithms beyond the constraints of noisy intermediate-scale quantum hardware. However, existing approaches implicitly assume a trusted entanglement-sharing layer across quantum processors. We show that this assumption introduces a fundamental vulnerability: adversarial perturbations of shared entanglement...

43. Adversarial robustness in rotationally equivariant quantum machine learning

Maureen Krumtuenger (University of Melbourne)

Poster

Group-equivariant quantum models impose symmetry constraints that can improve trainability, but their effect on adversarial robustness remains unclear. We address this question through a feature-level analysis of equivariant quantum models under adversarial transfer attacks. For models with invariant readout, predictions depend only on the group-twirled input, separating accessible invariant...

324. Algebraic and Topological Limits of Unitary-Input Data Re-Uploading

Mahtab Yaghubi rad (Leiden University)

Poster

Data re-uploading circuits are usually analyzed for scalar data encoded through commuting generators, where the model output is a truncated Fourier series. We study the non-Abelian counterpart in which the uploaded datum is an unknown unitary matrix U ∈ U(N), queried repeatedly between trainable unitary layers and read out through a Born probability.

We formulate the correct...

284. AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL

Daniele Lizzio Bosco

Internal review assignment R029

Poster

Clifford circuits play a foundational role in quantum computing, particularly due to their importance in quantum error correction and fault-tolerant logical synthesis.
While these circuits can be efficiently simulated and represented as symplectic matrices, standard synthesis methods, such as the Aaronson-Gottesman algorithm, often yield sub-optimal circuits with excessively high gate...

291. An efficient state preparation for the Pauli correlation encoding

Lucas Borges (TII)

Poster

Pauli correlation encoding (PCE) compresses many classical variables into expectation values of Pauli strings on fewer qubits. This compression has been used as a variational representation for combinatorial optimization, but a complementary primitive is needed to make the encoding bidirectional: given classical data or a classical solution, one would like to upload it directly into the...

236. Ansatz-Free Learning of Lindbladian Dynamics In Situ

Mr Petr Ivashkov (ETH Zürich)

Poster

Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust simulation protocols, and developing tailored error-correction methods. Under Markovian noise/dissipation, a natural characterization approach is to identify the full Lindbladian generator that gives rise to both...

172. Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods

Elies Gil-Fuster (Freie Universitaet Berlin, Heinrich Hertz Institute)

Poster

Decoded Quantum Interferometry (DQI) is a recently proposed quantum optimization framework that prepares a probability distribution biased toward high-quality solutions, with claims of quantum advantage for the Optimal Polynomial Intersection (OPI) problem. While previous work has primarily focused on the theoretical complexity of DQI, comparatively little is known about its empirical...

198. Augmenting Imaginary-Time Evolution with Local Geometric Information

Carlos Benavides-Riveros (IQM Quantum Computers)

Poster

Imaginary-time evolution (ITE) underpins a broad family of quantum algorithms for quantum simulation, optimization, and ground-state preparation. Yet all existing ITE methods are fundamentally limited by asymptotic convergence, with the energy error decaying only exponentially in imaginary time. We introduce augmented imaginary-time evolution (AITE), a generalized framework that extends...

249. Automated Circuit Design of Quantum Convolutional Neural Networks for Medical Image Classification

Dhiya Dharampal (Stellenbosch University)

Poster

One key area where machine learning has shown particular promise is through the use of Convolutional Neural Networks (CNNs) in medical image analysis and recognition. In light of this, the advancements of quantum machine learning have introduced a variety of quantum ansatze for classical CNNs, called quantum convolutional neural networks (QCNNs), some of which are capable of capturing global...

307. Autonomous quantum machine learning via Riemannian gradient-based optimization

Kasidit Srimahajariyapong

Poster

Quantum machine learning models typically rely on repeated quantum measurements and classical optimization during training, making the quantum-to-classical information-extraction process a fundamental bottleneck for scalability. We propose a framework for autonomous quantum machine learning based on Riemannian gradient optimization, in which, after encoding the training data, the optimization...

264. Bandwidth, Not Circuit Scaling, Drives Quantum-Kernel Recovery in Cross-Quality Face Verification

Rohan Ramakrishna (KLASS Engineering & Solutions Pte Ltd)

Internal review assignment R020

Poster

Quantum fidelity kernels are often judged by circuit architecture, but their first test is simpler: does the encoding create the right similarity geometry for the task? We answer this question in cross-quality face verification using fixed AdaFace embeddings and the official XQLFW fold structure. A default four-qubit ZZ fidelity kernel at the unscaled input bandwidth is near chance....

186. Benchmarking Analog Quantum Reservoir Computing on Neutral-Atom Platforms for Chaotic Time-Series Forecasting

Simone Angelozzi (University of Sputhampton), Dr Simone Gasperini

Poster

Forecasting chaotic dynamical systems is challenging due to sensitivity to initial conditions and long-term dependencies. We benchmark analog Quantum Reservoir Computing (QRC) on the Pasqal neutral-atom platform against classical forecasters (MLP, LSTM, CRC) using Mackey-Glass, Hénon, and Lorenz-63 datasets from ReservoirPy. Temporal inputs are encoded as continuous detuning waveforms driving...

150. Benchmarking Optimisers Across Variational Quantum Algorithms: Noise, Scale, and a Barren-Plateau-Motivated Early Stopping Analysis

Keiasha Gounden (NITheCS)

Poster

Variational quantum algorithms (VQAs) — including VQE, QAOA, and VQLS — share
a classical optimisation problem: minimising a parameterised cost function over
barren plateaus, noisy high-dimensional loss landscapes. While prior work has
benchmarked optimisers within individual VQA families, systematic comparison
across task families, noise models, and qubit counts remains limited. This...

325. Benchmarking Quantum Autoencoders for Time Series Anomaly Detection

Lourens van Niekerk (Forschungszentrum Jülich)

Poster

Anomaly detection (AD) is useful in medical assessment, fraud
detection, earth observation, and many more areas. These applications usually rely on deep time-series (TS) data,
in which anomalies could be complex and difficult to detect. Autoencoders have proven effective at TSAD, yet a systematic,
rigorous benchmark of quantum autoencoders is lacking. We are conducting a comprehensive study...

70. Beyond Black Boxes: Gate-Level Oracles for Quantum Advantage

Gabriel Escrig (Complutense University of Madrid)

Poster

While quantum algorithms promise significant speedups for combinatorial optimization, abstract black-box oracles often hide massive compilation overheads that threaten fault-tolerant viability. Drawing on our recent publications [1, 2], this work bridges the gap between algorithmic theory and physical implementation by presenting a fully explicit, gate-level synthesis framework for exact...

40. Bipartition Mutual Information Dynamics in Hybrid Quantum Neural Networks: An Exact Information-Theoretic Analysis

Neeshu Rathi (Indian Institute of Technology Delhi)

Poster

Understanding how variational quantum circuits encode and reorganize information during gradient-based training remains a fundamental open problem in quantum machine learning. We present an exact mutual information analysis of a 5-qubit Hybrid Quantum Neural Network (HQNN) trained on binary classification datasets, tracking Von Neumann bipartition mutual information I(A:B) across all unique...

64. Black-box Interactive Proofs for Quantum Many-vs-one Problems

Abdelkhalig Elfatih (African Institute for Mathematical Sciences (AIMS) South Africa)

Poster

Several quantum testing and learning tasks exhibit a striking
asymmetry: an algorithm that jointly measures multiple states is exponentially more powerful than one restricted to
single-copy measurements. Purity testing requires $\Omega(\sqrt{d})$
copies incoherently but only $\mathcal{O}(1)$ coherently; Pauli
shadow tomography on $n$ qubits requires $\Omega(2^n)$ incoherently
but only...

195. Bowtie VarQTE: A Resource-Efficient Quantum State Preparation Primitive

marc drudis (IBM, EPFL)

Poster

The preparation of quantum states is a fundamental requirement for many quantum algorithms. A native route to preparing physically structured states is based on short-time simulation of dynamical processes, such as real or imaginary time evolution. This work presents a resource-efficient framework for the approximation thereof with bowtie-VarQTE which uses classical simulation where possible...

237. Breaking $1/\varepsilon$ Barrier in Quantum Zero-Sum Games: Generalizing Metric Subregularity for Spectraplexes

Emmanouil Vasileios Vlatakis Gkaragkounis (University of Wisconsin-Madison), Pucheng Xiong (University of Wisconsin Madison)

Poster

This work revisits the conjectured $1/\epsilon$ barrier for gradient-based methods in quantum zero-sum games and shows that the correct picture is geometric rather than universal. Our main technical contribution is a spectraplex analogue of Hoffman-type error bounds, formulated through the SP--MS condition, which converts small duality gap into Frobenius-distance control to the Nash set. This...

170. Cautious optimism for quantum kernels and deep parametrized quantum circuits

Elies Gil-Fuster (Freie Universitaet Berlin, Heinrich Hertz Institute)

Poster

The double descent phenomenon challenges traditional statistical learning theory by showing that increasing model size beyond the interpolation threshold need not worsen performance on unseen data and can, in some regimes, improve it. While this behavior has been widely studied in classical machine learning, its role in quantum machine learning remains less understood. In this work, we...

175. Circuit Harmonic Matrices

Kyle Campbell (University of Edinburgh)

Poster

This extended abstract summarises the full paper "Circuit Harmonic Matrices: A Spectral Framework for Quantum Machine Learning". Parametrised quantum circuits are central to near-term QML, but architectural choices such as encoder, ansatz, entangling pattern, observable, and input state are often difficult to assess before training. We introduce the circuit harmonic matrix $C$, a...

319. Circuit-aware gate calibration via grouped channel direct fidelity estimation

Júlia Barberà Rodríguez (ICFO (Institute of Photonic Sciences))

Poster

We improve the existing direct fidelity estimation protocol for quantum channels by introducing a grouped, context-preserving variant tailored to circuit-level gate calibration. The method groups input-output Pauli pairs whose input and output operators are simultaneously qubit-wise commuting, so that a single preparation-measurement configuration estimates multiple Pauli-transfer...

182. Classical and Quantum Heuristics for the Binary Paint Shop Problem

V Vijendran (A*STAR QInC and CQT, NUS)

Poster

The Binary Paint Shop Problem (BPSP) is an $\mathsf{APX}$-hard optimisation problem in automotive manufacturing: given a sequence of $2n$ cars, comprising $n$ distinct models each appearing twice, the task is to decide which of two colours to paint each car so that the two occurrences of each model are painted differently, while minimising consecutive colour swaps. The key metric is the paint...

91. Classical Neural Networks in Quantum Constant Depth

Alessandro Luongo (Center for Quantum Technologies), Prof. Ansis Rosmanis (Quantinuum)

Poster

How quantum computing and neural network architectures can be meaningfully integrated is a central question in quantum artificial intelligence. Current approaches mainly follow two paradigms: variational quantum circuits, and quantum algorithms for the linear-algebraic primitives of classical neural networks. Here we propose a third route, grounded in the complexity-theoretic analysis of...

277. Classical Shadows over Symmetric Spaces

Rebecca Chang

Poster

Efficiently learning expectation values of unknown quantum states via classical shadows has become an important primitive in both theoretical and experimental aspects of quantum computation. Typically, classical shadow protocols involve randomised measurements induced by sampling uniformly randomly from a compact group, a situation which is now quite well understood. In this work we go beyond...

168. Classically compiling large-scale quantum circuits for approximate ground and thermal states

Cenk Tüysüz (CERN)

Poster

Preparing thermal and ground states of many-body Hamiltonians is a central task for quantum simulation. Algorithms with provable guarantees target fault-tolerant devices, while variational methods such as variational quantum imaginary time evolution (VarQITE) suit near-term hardware but require measuring a number of expectation values that grows with the ansatz size, with shot costs amplified...

39. Clinical Data Classification with Quantum Kernel Alignment

Florian Heininger

Poster

Quantum kernel methods are among the most promising near-term applications of quantum machine learning, with the quantum feature map controlling a model's ability to capture meaningful correlations from little labelled data. We present a systematic approach that aligns a quantum-inspired feature map to highly imbalanced and scarce clinical datasets through kernel--target alignment (KTA)...

301. Coherent Quantum Learning: Training via Coherent Evolution

Barry Sanders (University of Calgary), Ignacio B. Acedo (Quantum Mads), Javier Gonzalez-Conde (Quantum Mads), Lirandë Pira (NUS), Pablo Rodriguez-Grasa (University of the Basque Country)

Poster

We introduce coherent quantum learning, a training framework for parameterized quantum models in which the model parameters are quantum degrees of freedom evolved under a Hamiltonian that encodes the loss function. Current quantum machine learning retains classical optimization: parameters are updated by a classical outer loop using gradient estimates from measurements, and quantum coherence...

69. Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems

Armando Angrisani, NIKITA GUSEYNOV (Shanghai Jiao Tong University), Zoe Holmes

Poster

We develop coherent-state propagation, a computational framework for simulating bosonic systems. We focus on bosonic circuits composed of displaced linear optics augmented by Kerr nonlinearities, a universal model of bosonic quantum computation that is also physically motivated by driven Bose-Hubbard dynamics. The method works in the Schrodinger picture, representing the evolving state as a...

98. Comparing Persistent and Bottleneck Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection

Donovan Slabbert (Stellenbosch University)

Variational QML

Poster

Quantum convolutional neural networks (QCNNs) provide a framework for quantum representation learning through alternating convolution and pooling operations. Although many QCNN architectures remain efficiently classically simulable, they provide a framework for studying quantum representation learning on near-term quantum devices. While quantum autoencoders (QAEs) have been proposed for...

76. Deep Learning Approaches to Quantum Error Mitigation

Ifan Williams (Quantinuum)

Poster

Quantum error mitigation (QEM) is a near-term route to reducing bias in estimates extracted from noisy quantum processing units (QPUs). We present a systematic study of machine learning (ML)-guided QEM: deep learning models that mitigate full output probability distributions from measured quantum circuits. The task is deliberately stringent: an improved computational-basis distribution can be...

130. Deployable Quantum Machine Learning by Design: Hardware-Aware and Adversarially Robust Neural Architecture Search for Quantum Circuits

Dr Walid El Maouaki (New York University Abu Dhabi)

Poster

Quantum machine learning (QML) models are often designed mainly to maximize accuracy, but accuracy alone is not enough for deployment on near-term quantum hardware. In practice, quantum circuits must be transpiled to match backend constraints such as qubit connectivity and native gate sets. This can increase circuit depth, two-qubit gate count, SWAP overhead, and noise accumulation. In...

326. Designing Minimalistic Quantum Reservoir Computing in Practice

Grecia Castelazo (UCSB)

Poster

In this work, we explore the potential of existing quantum systems such as cold atoms in optical
cavities or quantum dots to perform complex computational tasks through the Quantum Reservoir
Computing (QRC) paradigm under some of the key constraints of the Noisy Intermediate Scale
Quantum (NISQ) era, such as: moderate hardware size, and non-negligible levels of noise. QRC
is a relatively...

313. Designing quantum circuits to enhance optical machine learning

Nikita Azevedo (University of the Witwatersrand)

Poster

Optical neural networks offer a path toward direct, light-speed inference. These networks have a range of functionalities, including handwritten-digit recognition, optical field phase imaging, and quantum state generation. However, their physical synthesis is computationally demanding: iterative, taxing simulations of light propagation dominate the training cycle. Our research question is then...

178. Diagnosing Quantum Reservoirs at scale based on expressivity and coverage

Laia Domingo (Computer Vision Center, Universitat Autònoma de Barcelona)

Poster

Quantum reservoirs (QRs) are a hardware-friendly route to quantum machine learning on near-term devices, but their performance depends critically on the choice of fixed reservoir dynamics. Existing diagnostics often rely on model-specific assumptions or require access to the full quantum state, unitary, or output distribution, limiting their scalability. We introduce a two-level diagnostic...

78. Differentiable Quantum Residual Networks without Post-Selection

Amena Khatun (CSIRO)

Poster

Residual learning transformed the training of deep classical neural networks, yet its adoption in quantum machine learning has remained limited because existing quantum residual architectures rely on post-selection, which prevents fully differentiable optimisation and makes execution increasingly inefficient as circuit depth grows. We introduce a new quantum machine learning architecture, the...

148. Distributed quantum architecture search using multi-agent reinforcement learning

Mikhail Sergeev (Skolkovo Institute of Science and Technology, Moscow, Russia)

Poster

Quantum architecture search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms. The framework finds a well-suited problem-specific structure of a variational ansatz. Among possible implementations of QAS the reinforcement learning (RL) stands out as one of the most promising. Current RL approaches are single-agent-based and show poor scalability...

151. Double Descent in Parameterized Quantum Circuits

Francesco Scala (University of Basel)

Poster

We present the first analytical and numerical characterization of double descent in parameterized quantum circuits (PQCs) for regression tasks. By exploiting the Fourier structure of PQC models and the lazy training approximation, we establish a precise connection between overparameterized gradient descent on variational parameters, the quantum neural tangent kernel, and the emergence of...

143. Double Descent in Quantum Extreme Learning Machines

Ege Yilmaz (University of Basel), Sabri Meyer (University of Basel)

Poster

Understanding generalization in quantum machine learning, in particular the phenomenon of double descent, is a crucial open problem, especially as these models move into overparameterized regimes. Yet for quantum extreme learning machines (QELMs), the precise relationship between architectural choices, physical parameters, and the resulting shape of the risk curve remains poorly understood. We...

229. Double-bracket quantum algorithms for thermal state preparation

Andrew Wright (NTU)

Poster

We propose quantum algorithms for preparing thermal states via the simulation of the thermofield double states. The key idea is to leverage double-bracket quantum algorithms to implement imaginary-time evolution on thermofield double states, whose reduced state realizes the Gibbs state. Our method, termed double-bracket thermofield double (DB-TFD), introduces two variants. The first, the...

125. Dynamical Lie algebra and CNOT gates

Mrs Orlane Zang (Orange Innovation)

Poster

Parameterized Quantum Circuits (PQCs) have emerged as a central
framework for variational algorithms in the NISQ regime, with major
applications in quantum simulation, combinatorial optimization, and,
more recently, quantum machine learning (QML). By developing a
new approach for computing dynamical Lie algebras associated with
quantum circuits combining parametrized gates and...

173. Efficient Circuit Transpilation of Commuting Gates on 2D Lattices

Sabina Dragoi (ETH Zurich, IBM Research)

Poster

Combinatorial optimization problems are central to many applications but can be challenging to solve. Quantum approaches such as the Quantum Approximate Optimization Algorithm (QAOA) offer new tools with which to tackle such problems. However, QAOA circuits inherit the interaction structure of the target Hamiltonian, often resulting in deep circuits when compiled onto hardware with limited...

208. Efficient Learning of Low Bond Dimension Born Machines

Maude Lizaire

Poster

Parameterized quantum circuits (PQCs) are increasingly studied as machine learning models, yet whether they can offer provable advantages over classical approaches remains largely open. In the context of generative modelling, Born Machines, which generate probability distributions according to the Born rule from parameterized quantum states, are a natural candidate for quantum advantage, as...

129. Efficient upsampling for tensor-network and quantum-state encoded functions

Siddhartha Emmanuel Morales Guzman (Technology Innovation Institute)

Poster

Quantum machine-learning and simulation algorithms often assume data encoded directly in quantum amplitudes or q-sample states, where probability distributions are represented coherently. Yet changing the resolution of such data is nontrivial: since the cost of refining a grid from (2^n) to (2^{n+m}) is exponential. We introduce a multiscale upsampling framework that refines quantum-state and...

177. Empirical evidence of generalization in quantum machine learning in small-data regime

Leena Anthony (FHNW, EPFL)

Poster

Quantum machine learning is a promising paradigm for learning from limited data, a bottleneck in data scarce regimes like medical imaging. Quantum convolutional neural networks are especially attractive here, combining a hierarchical architecture, strong inductive bias, and a parameter count that grows logarithmically with system size. Their appeal rests on generalization bounds showing that a...

81. End-to-End Variational Quantum Sensing with Weighted Graph States

Dr Byron Alexander (Stellenbosch University), Mr Unathi Skosana (Stellenbosch University)

ML for quantum, physics

Poster

Entangled quantum states can improve the precision of parameter estimation beyond classical limits, but realizing this advantage in practice requires protocols that remain effective under realistic noise, hardware constraints, and finite measurement data. Here, we study an end-to-end quantum sensing protocol in which weighted graph states are used as metrological resource states for...

226. Entanglement-assisted Hamiltonian dynamics learning

Guillermo Abad López (Grup d'Informació Quantica (GIQ), UAB)

Poster

Approximating the dynamics generated by complex many-body Hamiltonians with simpler, experimentally feasible models is a central challenge at the intersection of quantum simulation and quantum learning. Quantum generative adversarial networks (QGANs) have recently emerged as a powerful framework for learning unitary dynamics, often achieving significantly more compact representations than...

248. Evolutionary Discovery of Quantum Kernel Architectures for Qubit-Agnostic Generalisation

William Marais (NiTheCS)

Poster

Quantum kernel methods embed classical data into quantum Hilbert spaces via parameterised feature-map circuits, but the design of these circuits remains largely manual, relying on fixed ansätze chosen by convention rather than principled optimisation. We present an evolutionary algorithm (EA) framework that searches directly over the space of quantum circuit architectures using a hierarchical...

164. Experimental data re-uploading with provable enhanced learning capabilities

Martin Mauser (University of Vienna)

Poster

Data re-uploading offers a compelling paradigm for near-term quantum machine learning. In this work, we present the full experimental realization of the originally proposed data re-uploading on a tunable integrated photonic processor. While the deep circuit requirements of multi-layer architectures present severe coherence-time bottlenecks for alternative quantum hardware, the integrated...

166. Exploiting biased noise in supervised variational quantum models

Connor van Rossum (University of Queensland)

Poster

Variational quantum algorithms (VQAs) have dominated literature as tools for demonstrating quantum utility on near-term quantum hardware, with applications in optimisation, quantum simulation, and machine learning. While researchers have studied how easy VQAs are to train, the effect of quantum noise on the classical optimisation process is still not well understood. Contrary to expectations,...

156. Exploring the Prospects of Geospatial Artificial Intelligence (GeoQAI) for Crime Intelligence

Dr Makhamisa Senekane (University of Johannesburg)

Poster

Geospatial artificial intelligence (GeoQAI) entails the deployment of artificial intelligence (AI) using the geospatial data. Thus, in essence, GeoAI fuses together AI and geospatial science. The quantum version of GeoAI is geospatial quantum AI; which fuses together quantum machine learning (QML) and geospatial science. In this work, we explore the prospects of applying QML for crime...

310. Exponential concentration and symmetries in Quantum Reservoir Computing

Antonio Sannia (Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC;USRA Research Institute for Advanced Computer Science (RIACS))

Poster

Quantum reservoir computing (QRC) is an emerging framework for near-term quantum machine learning that offers in-memory processing, platform versatility across analogue and digital systems, and avoids typical trainability challenges such as barren plateaus and local minima. The exponential number of independent features of quantum reservoirs opens the way to a potential performance improvement...

54. Exponential speedup in measurement property learning with post-measurement states

Zhenhuan Liu

Poster

Learning properties of quantum states and channels is known to benefit from resources such as entangled operations, auxiliary qubits, and adaptivity, whereas the resource structure of measurement learning, namely, learning properties of quantum measurement operators, remains poorly understood. In this work, we identify a measurement learning task for which access limited to classical...

308. Exponentially many initializations to avoid barren plateaus: too much choice?

Ricard Puig (École Polytechnique Fédérale de Lausanne (EPFL))

Poster

This submission is based on [arXiv:2606.18515]

Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure for barren plateaus is simply to initialize the parameters more carefully. Here we show that the situation is subtler. We introduce a first-moment framework that gives...

87. Fast stabilizer state preparation via AI-optimized graph decimation

Gabriel Matos (Quantinuum)

Poster

Efficient stabilizer state preparation is essential in quantum algorithms, measurement-based quantum computation, and quantum error correction. We introduce graph decimation, a scalable formulation of stabilizer-state circuit synthesis as a sequential decision-making problem on graph states and a compact classical learning environment for quantum circuit synthesis. The method uses exact graph...

110. Fermionic Shadow Regression: Amortized Learning of Molecular Electron Dynamics

Aniket Deshpande (University of Illinois, Urbana-Champaign)

Poster

Algorithmic shadow spectroscopy extracts molecular transition frequencies from randomized measurements of many observables over time at a fixed molecular geometry. We ask whether the underlying object, the time-dependent fermionic-shadow signal matrix of degree-two Majorana observables, can instead be learned across a geometry scan, amortizing shadow-spectroscopy observables so that the...

26. Fourier analysis of quantum neural network with non-linear data embedding

Haiyue Kang (University of Melbourne)

Poster

Fourier analysis has become a crucial tool for understanding the expressivity of Variational Quantum Circuit (VQC) models, as well as an important indicator of barren plateaus. While existing literature has only studied angle-embedded VQCs in a noiseless environment, here we develop the Fourier analysis of VQCs with non-linear data embedding, with particular focus on amplitude embedding, which...

105. Fourier Symmetrization for Geometric Quantum Machine Learning

Mr Letao Wang (CentraleSupélec - Université Paris-Saclay)

Variational QML

Poster

Geometric quantum machine learning incorporates symmetry into quantum models, using intrinsic problem structure to improve data efficiency, generalization, and trainability. Quantum Fourier models (QFMs) are also important because many angle encoding quantum models have outputs that admit finite Fourier series, so their expressivity can be analyzed through Fourier coefficients. In this work,...

128. From Layers to Gradients at 100 Qubits: The TQML Quantum Machine Learning Platform

Maniraman Periyasamy (Terra Quantum AG)

Poster

TQML is an integrated quantum machine learning platform that spans the full workflow, from data to trained model, inside ordinary PyTorch and JAX. Quantum machine learning today covers a wide ansatz design space, from hardware-efficient and NISQ-style circuits through data re-uploading and tensor-network models to symmetry- and particle-number-preserving constructions such as...

119. Fundamentals of quantum Boltzmann machine learning with visible and hidden units

Mark Wilde (Cornell University)

Poster

One of the primary applications of classical Boltzmann machines is generative modeling, wherein the goal is to tune the parameters of a model distribution so that it closely approximates a target distribution. Training relies on estimating the gradient of the relative entropy between the target and model distributions, a task that is well understood when the classical Boltzmann machine has...

127. Generative Quantum Data Embeddings for Supervised Learning

Jaewoong Heo (Yonsei University)

Poster

The classification performance of quantum supervised learning on classical data is fundamentally controlled by the feature map that embeds input into quantum states. To promote quantum state distinguishability, we propose an energy-based generative learning framework that synthesizes gate sequences to optimize embedding structures and refine data-tailored parameters using a fidelity-based...

292. Geometry-aware variational real-time quantum evolution

André Juan Ferreira Martins (Technology Innovation Institute)

Poster

Simulating the real-time dynamics of quantum systems is a key application of quantum computing. Variational quantum simulation (VQS) attempts to overcome the resource-intensive requirements of methods such as Trotterization by using a variational quantum circuit, controlled by $m$ parameters, to simulate the time evolution.
However, so far, VQS and all its variants suffer from at least one of...

250. Ghost Image Reconstruction with Classical and Quantum Convolutional Neural Networks

Shawal Kassim (PhD Student University of the Witwatersrand)

Poster

Image reconstruction is a critical problem in Optics, particularly in Ghost Imaging experiments [1, 2]. While enabling non-local quantum imaging without state collapse, it requires $\approx 2N^2$ measurements for an $N \times N$ image [3], causing challenges in reconstruction speed and fidelity. Classical reconstruction methods remain computationally intensive.
To address this, we propose...

314. Gibbs State Simulation with Pauli and Majorana Propagation

Manuel Rudolph (EPFL)

Poster

Thermal state preparation is a critical subroutine in many quantum algorithms, especially in Quantum Machine Learning (QML) and condensed matter physics. Generative modeling tasks, such as training Quantum Boltzmann Machines (QBMs), rely heavily on drawing samples from thermal distributions or computing expectation values over Gibbs states to evaluate loss functions and gradients. However,...

165. Global Optimization for Parametrized Quantum Circuits

Antonios Varvitsiotis (Singapore University of Technology and Design)

Poster

In the absence of error correction, noisy intermediate-scale quantum devices are typically operated using PQCs trained to minimize a loss function. Finding the circuits' optimal parameters is a hard optimization problem, where global guarantees are known only for highly structured cases of limited practical relevance, and first-order methods can fail even locally due to barren plateaus. In...

205. Graph Neural Networks on Quantum Computers: a Trainability and Classical Simulability Analysis

Theodor Iosif (Quantum Learning Labs, Department of Computer Science, University College London)

Poster

Graph Neural Networks (GNNs) have shown promise in solving complex graph-based optimization problems, with implementations on both classical and quantum computers. Whilst promising better scalability with the problem size thanks to the complex structure of Hilbert spaces, quantum GNNs are prone to untrainability; in particular, the barren plateau problem.

In this work, we provide a...

24. Grokking and epoch-wise double descent in quantum neural networks

Daniel Pranjic (Fraunhofer IAO)

Poster

Grokking, the delayed transition from memorization to generalization, is a fundamental phenomenon in gradient-based learning, yet its dynamics within variational quantum machine learning (QML) remain largely unexamined. In this work, we report the empirical observation of both the grokking transition and epoch-wise double descent in a two-qubit quantum neural network (QNN) under a complete...

210. Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians

Mr Aleksei Ustimenko (Simulacra), Timothy Heightman (ICFO - Institute of Photonic Sciences)

Poster

We formulate $\text{spin-}1/2$ quantum ground-state learning as manifold variational optimization over centrally odd scalar
functions on $\mathrm{SU}(2)^N$. This replaces explicit Hilbert-space
vector amplitudes with manifold functions acted on by Lie-derivative
Hamiltonians via automatic differentiation. We prove a novel variational principle in this function-space that preserves the...

273. Hamiltonian Decoded Quantum Interferometry

Alexander Schmidhuber (MIT), Jonathan Lu (Massachusetts Institute of Technology)

Poster

We introduce Hamiltonian Decoded Quantum Interferometry (HDQI), a quantum algorithm utilizing coherent Bell measurements and the symplectic representation of the Pauli group to reduce Gibbs sampling and Hamiltonian optimization to classical decoding. For a Pauli Hamiltonian $H$ and any degree-$\ell$ polynomial $\mathcal{P}$, HDQI prepares a purification of $\rho_{\mathcal{P}}(H) =...

176. Hardware-Aware Tensor Networks for Real-Time Machine Learning at Particle Colliders

Sagar Addepalli (SLAC National Accelerator Lab)

Poster

The 3-page abstract is attached.

22. Hardware-Realistic Surface-Code Simulation under Stochastic Gate Noise

Ms Cherilyn Christen (EPFL), Mr Nathaniel Pacey (EPFL)

Poster

This work introduces a hardware-realistic simulation framework for modeling quantum circuits under physically motivated stochastic gate noise, addressing a key limitation in existing simulators that decouple decoherence from gate execution and ignore non-trace-preserving dynamics. Built on the quantum-gates library and extended with CERN's Quantum Technology Initiative, the framework adds...

63. Harmoniq: Efficient Quantum Data Augmentation Inspired by Quantum Harmonic Analysis

Kristina Kirova (Johannes Kepler University Linz)

Poster

Quantum machine learning has attracted significant interest in recent years. Most existing approaches, however, are variational in nature and require extensive parameter optimization subroutines.
Here, we propose a conceptually distinct quantum machine learning approach that goes beyond the variational paradigm.
Harmoniq takes a novel data augmentation technique from quantum harmonic...

231. How Do Hybrid Quantum-Classical Physics-Informed Neural Networks Learn?

Jay Shah (BosonQ Psi Corp (BQP))

Poster

Physics-Informed Neural Networks (PINNs) revolutionized the field of Scientific Machine Learning. The community has explored several variants of PINN. Shah et al. introduced Quantum-Assisted Physics-Informed Neural Network (QAPINN)$-$ a hybrid quantum-classical variant of PINN. QAPINN reduced weights up to 20\% as compared to the equivalent classical PINN while improving or maintaining...

191. Hybrid Quantum-Classical Logistic Regression for Calibrated Classification of Pulsar Candidates

Ms Chanelle Matadah Manfouo (Department of Physics, Stellenbosch University, Stellenbosch, 7600, South Africa; African Institute for Mathematical Sciences Research and Innovation Centre, Kigali, Rwanda; National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch, 7600, South Africa})

Poster

Modern radio surveys produce large numbers of candidate signals that must be screened to identify new pulsars. This is a rare-event classification problem, since most candidates correspond to noise or radio-frequency interference. In this setting, candidate ranking is as important as binary classification, so predicted probabilities should be reliable for telescope follow-up decisions.

This...

66. Implementing Quantum Cell Automata on a real neutral atom device

Rivan Rughubar (Forschungszentrum Julich)

Experimental/Hardware QML

Poster

Quantum cellular automata (QCA) provide a compelling framework for distributed quantum computation and the simulation of many-body quantum dynamics through the application of local, translation-invariant update rules. These update rules can also be used in the context of machine learning. In this work, we implement a QCA framework designed to solve the Maximum Independent Set graph problem on...

181. Improved regret bounds for structured online learning of quantum states

Dr Akshay Bansal (TU Wien)

Poster

Shadow tomography is fundamental to quantum information processing that focuses on predicting measurement outcomes rather than reconstructing the full state. Its online variant models adaptive and potentially adversarial measurement scenarios, where a learner sequentially predicts outcomes while competing with the best fixed quantum state in hindsight.

We show that exploiting additional...

44. Improving Generalization and Trainability of Quantum Eigensolvers via Graph Neural Encoding

Jungyun Lee (Yonsei Univ.)

Poster

Determining Hamiltonian ground states is central to physics, chemistry, and combinatorial optimization, but is often classically intractable due to the exponential growth of Hilbert space. While convergence-guaranteed quantum eigensolvers require high-overlap initial states, standard variational quantum eigensolver (VQE) used to prepare such states requires instance-specific optimization and...

122. Is Quantum Cross-Band Fusion Worth Its Cost? A Data-Scaling Ablation of Hierarchical Quantum DCT Encoders in Biomedical Image Classification

Prof. Miguel Ángel González Ballester (BCN MedTech, Universitat Pompeu Fabra, Barcelona, BSC, Barcelona Supercomputer Center, Barcelona, ICREA, Barcelona, Spain)

Poster

Hierarchical quantum spectral encoders often include a global ``fusion'' stage that
entangles otherwise disjoint frequency subspaces, at the price of doubling the
variational quantum parameter count and the two-qubit gate depth. We ask a single,
practical question: \emph{does this fusion stage actually improve classification, and
does the answer change as training data grows?} We run a...

86. Layerwise Fourier–Dirac Supervision for Modular Generalisation in Data-Reuploading Quantum Neural Networks

Carringtone Kinyanjui (Songhai, University of Manchester), Mr Martin Wagah (Songhai, University of Cambridge)

Variational QML

Poster

Data-reuploading quantum neural networks provide a useful setting for examining how variational quantum circuits represent structured classical functions. This talk studies modular addition as a finite-group learning problem in which generalisation requires recovery of a cyclic rule, rather than interpolation from local numerical regularities. We consider simulated seven-qubit, four-layer...

217. Learning anisotropic kernels for quantum generative modeling with the maximum mean discrepancy

Oriol Balló-Gimbernat (Eurecat, CVC, UAB)

Poster

Training quantum generative models with the maximum mean discrepancy (MMD) avoids explicit likelihood evaluation, but transfers a central design choice to the kernel. In quantum circuit Born machines over bitstrings, the commonly used Gaussian kernel is an isotropic function of Hamming distance. This kernel can be interpreted as a weighted mixture of Pauli-Z parity observables, with all...

241. Learning Dynamics and Interpretability in Quantum-Inspired Tensor Network Classifiers Across Biomedical, Neuroimaging, and Remote Sensing Applications

Domenico Pomarico (University of Bari)

Poster

Quantum-inspired machine learning based on Matrix Product States (MPS) provides scalable models for high-dimensional classification while naturally exposing physically interpretable observables derived from quantum many-body theory. We present a unified analysis of MPS classifiers across biomedical omics, structural neuroimaging, hyperspectral remote sensing, and wildfire susceptibility...

100. Learning ground state observables from quantum computing experiments

Ben Jaderberg (IBM)

Poster

Please find attached our extended abstract for submission

14. Learning pure quantum states in any dimension (almost) without regret

Josep Lumbreras (Nanyang Technological University)

Poster

We extend quantum state tomography with minimal cumulative disturbance to arbitrary finite-dimensional pure states. A learner sequentially receives fresh copies of an unknown pure state, chooses a rank-one projector for each copy using the previous outcomes, and performs the corresponding two-outcome projective measurement. The goal is to learn the state while keeping the chosen projectors...

289. Learning quantum states efficiently with the shadow overlap

Mrs Douaa Salah (INRIA, EPFL), Dr Jonas Landman (INRIA / ENS)

Poster

Training a parametrized quantum circuit $U(θ)$ to reproduce an n-qubit target state |ψ⟩ is usually posed as maximizing the fidelity $|⟨ψ|ψ_θ⟩|²$. This faces two obstacles: 1) fidelity requires coherent access to |ψ⟩, and 2) being a global observable, fidelity suffers barren plateaus, with gradients vanishing as $2^{−Ω(n)}$. Huang, Preskill and Soleimanifar recently proposed the shadow overlap,...

41. Learning Temporal Patterns in Financial Time Series: A Comparative Study of Different Quantum Algorithms

Prof. Gerhard Hellstern (DHBW Stuttgart)

Poster

See appended pdf

93. Limits of quantum generative models with classical sampling hardness

Sabrina Herbst (TU Wien)

Poster

Sampling tasks have been successful in establishing quantum advantages, which has fueled research on using them for generative modeling. In particular, the potential to build generative models on classically hard distributions would immediately preclude classical simulability, due to theoretical separations. Implications of using such distributions on model trainability have, however, not been...

204. Local Fidelities for Enhanced Quantum Kernels

André Sequeira (INESC TEC)

Poster

Quantum kernels (QKs) provide a promising framework for quantum-enhanced machine learning but suffer from the vanishing similarity phenomenon, also known as kernel concentration. As the number of qubits increases, the variance of kernel values decays exponentially, making accurate estimation prohibitively expensive while simultaneously degrading the generalization capability of the resulting...

92. Local tensor-train surrogates for quantum learning models

Sreeraj Rajindran Nair (University of Technology Sydney (UTS))

Poster

A key bottleneck in quantum machine learning is the computational cost of repeated quantum circuit evaluations during the inference phase. To address this, we present a framework for constructing fast, cheap, provably accurate classical tensor-train surrogates of fully trained quantum machine learning models within local patches of their input data space. The approach combines Taylor...

222. Logic-Inspired Parameterised Quantum Circuits for Quantum Machine Learning

Tamanna Dasanjh (Quantum Engineering Technology Labs, H. H. Wills Physics Laboratory and School of Electrical, Electronic, and Mechanical Engineering, University of Bristol, BS8 1FD, UK and Quantum Engineering Centre for Doctoral Training, H. H. Wills Physics Laboratory and School of Electrical, Electronic, and Mechanical Engineering, University of Bristol, BS8 1FD, UK)

Poster

Poster abstract is uploaded as a PDF, as requested.

242. Low Overhead Precise Gate Importance Estimation for Pruning Parametrized Quantum Circuits

Rodrigo Martínez Sanz (Universidad Politécnica de Valencia)

Poster

Pruning parametrized quantum circuits requires quantifying the local contribution of each gate to the cost function, denoted as the importance in the classical domain, a quantity that existing methods approximate through first- or second-order Taylor expansions. We show that this approximation can be avoided by deriving a closed-form expression for the exact importance at zero additional cost....

211. Machine learning with minimal use of quantum computers: Provable advantages in Learning Under Quantum Privileged Information (LUQPI)

Vasilii Bokov (Leiden University)

Poster

Quantum machine learning is often listed as a promising application of quantum computers, in part due to numerous proofs of possible quantum advantages. A central question is how small a role quantum computers can play while still enabling provable learning advantages over classical methods. We study an especially restricted setting where a quantum computer is used only as a feature extractor:...

199. Machine Learning-Accelerated Discovery of Topological Superconductors: Insights from Bi₂Se₃/Nb Heterostructures for Fault-Tolerant Quantum Computing

Beauty Masolo (University of Pretoria)

Poster

Topological superconductors hosting Majorana zero modes offer a promising pathway to fault-tolerant quantum computing, yet material realization remains a critical bottleneck. This study investigates the Bi₂Se₃/Nb heterostructure while employing machine learning to accelerate discovery of materials with analogous properties. Recent experimental work on Bi₂Se₃/NbSe₂ Josephson junctions reveals...

192. Machine Learning-based Quantum Error Mitigation for Variational Algorithms

Nikita Korolev (Russian Quantum Center)

Poster

We investigate a machine learning-based quantum error mitigation method for the VQE algorithm. A training sample of near-Clifford schemes with classical simulation of noise-free observers is used. A comparison of regression models under various types of noise is conducted. It is shown that Ridge regression reduces the energy error and, at elevated noise levels, outperforms Zero Noise...

317. Markov Chain Monte Carlo with Imaginary Time Evolution for Portfolio Optimization

Shruti Jain (Northeastern University London)

Poster

Portfolio optimization (PO) aims to find asset weights that balance risk and return, but the number of candidate portfolios grows exponentially with asset count, making exhaustive search intractable for classical methods such as mean-variance optimization, Monte Carlo simulation, and linear/quadratic programming. Quantum computing offers a potential path forward—superposition and entanglement...

48. Measurement-induced non-commutativity in adaptive fermionic linear optics

Yifan Tang

Poster

Fermionic linear optics (FLO) with Gaussian resources is efficiently classically simulable, with many-body amplitudes reducing to determinants and Pfaffians. We show this is no longer true when fermions carry internal $d$-level labels and undergo mid-circuit number monitoring with classical feedforward. The measurement record routes the selected blocks into a fixed Bell-fusion geometry that...

243. Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials

Asel Sagingalieva (Terra Quantum AG)

Poster

Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason, we apply multi-objective hyperparameter optimization to both objectives. Additionally, we experiment with modified architectures...

187. Multistage Variational Quantum Learning for Compositional Concept Generalization

Mina Abbaszadeh (University College London (UCL))

Poster

Compositional concept generalization, the ability to understand novel combinations of previously learned concepts, remains a major challenge for multimodal AI systems. We present a multistage variational quantum learning framework that extends the Distributional Compositional Categorical (DisCoCat) model to vision-language reasoning for image–text pairs. The proposed approach first learns ...

260. Multivariate quantum generative models from coherent filtering and Gibbs-IQP correspondence

Oleksandr Kyriienko (University of Sheffield)

Poster

We propose an approach for constructing multivariate probability distributions with efficient pre-training, compatible with the train-on-classical deploy-on-quantum workflow. We build models in a latent space, selecting modes that are defined by orthogonal transformations (Fourier, Hadamard, IQP etc). Starting from a uniform state of equal mode amplitudes, imaginary-time filtering reweights...

57. Multivariate quantum reservoir computing with discrete and continuous variable systems

Tobias Fellner (University of Stuttgart)

Poster

Quantum reservoir computing (QRC) is an appealing quantum machine learning model: the quantum system acts as a fixed dynamical feature map and only a linear readout is trained, so learning reduces to a convex problem that avoids the barren plateaus and costly variational loops of quantum neural networks, while remaining well suited to near-term hardware. Yet QRC has been studied almost...

282. Native Measurement Based Variational Ansatz: Design and Analysis

Rajarsi Pal (ENS PSL, INRIA)

Poster

Variational quantum eigensolvers are typically formulated in the circuit model, where parameterized unitaries prepare Ansatz states whose parameters are optimized to approximate the ground state of a given Hamiltonian. In measurement-based quantum computation (MBQC), however, variational state preparation is realized using an entangled graph state, adaptive single-qubit measurements in...

85. Navigating the trainability–expressibility trade-off in quantum machine learning

Peter Röseler (Jülich Supercomputing Centre, Forschungszentrum Jülich; Universität zu Köln)

Poster

This work studies the trainability–expressibility trade-off in variational quantum machine learning. Highly expressive parameterized quantum circuits can suffer from barren plateaus, while overly restricted circuits may lack useful representational power. We introduce a finite-sample, dimension-free trainability certificate based on gradient fluctuations, allowing trainability to be used as a...

179. Near-Optimal Parameter Tuning of Level-1 QAOA for Ising Models

V Vijendran (A*STAR QInC and CQT, NUS)

Poster

The Quantum Approximate Optimisation Algorithm (QAOA) tackles combinatorial optimisation problems by encoding their solutions into the ground state of an Ising Hamiltonian prepared by a $p$-level parameterised circuit, with the angles tuned classically. Parameter optimisation is widely regarded as a central bottleneck, even for the shallowest circuits. Focusing on QAOA at $p=1$ (QAOA$_1$), we...

56. No universal purification in quantum mechanics

Zhenhuan Liu

Poster

Many central tasks in fundamental physics and quantum information processing are possible only insofar as mixed quantum states can be made purer. In this work, we prove that the linearity and positivity of quantum mechanics impose general restrictions on quantum purification, unveiling a new fundamental principle of quantum information processing. We first establish that no quantum operation...

55. Non-Markovian Noise Suppression Simplified through Channel Representation

Zhenhuan Liu

Poster

Non-Markovian noise, arising from memory effects in the environment, poses substantial challenges to conventional quantum noise suppression protocols, including quantum error correction and mitigation. We introduce a channel representation for arbitrary non-Markovian quantum dynamics, termed the Choi channel, which translates the complex dynamics of non-Markovian noise into the familiar...

46. Normalizing Flows for Classical Simulation of Quantum Circuits in Phase Space

Edward Jiang (ICFO)

Poster

We present a novel framework to learn a representation of quantum circuits based on the phase
space formulation of many-qubit systems. Given that N-qubit states can be represented as quasiprobability distributions on a 2N-dimensional manifold, we show how unitary gates can be parameterized as normalizing flows between these distributions, opening a new avenue for AI-assisted
quantum circuit...

145. On Quantum Advantage in Sampling Using Instantaneous Quantum Polynomial Circuits

AFRAD MUHAMED BASHEER

Poster

The field of quantum generative models based on Instantaneous Quantum Polynomial (IQP) circuits has seen significant interest in recent years, driven by results proving classical difficulty of sampling from sparse anti-concentrated IQP circuits and efficient trainability. Practical demonstrations of the former are crucial in establishing the superiority of these circuits in generating samples...

174. On the Quantum Advantage in Black-Box Adversarial Attacks

Fei Meng (Max-Planck-Institute for the Science of Light), Dr Jinge Bao (University of Edinburgh), Mr Junhao Xu (Lund University)

Poster

Black-box adversarial attacks, where the attacker has no access to model gradients, require expensive query-based search over high-dimensional input spaces. To the best of our knowledge, this work for the first time studies the quantum algorithms for this problem and shows the quantum advantage over classical methods. We start from Grover’s algorithm, which offers quadratic improvements for...

171. Optimal algorithmic complexity of inference in quantum kernel methods

Elies Gil-Fuster (Freie Universitaet Berlin, Heinrich Hertz Institute)

Poster

Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning.
A key bottleneck is the cost of inference: evaluating a weighted sum of kernel values to additive precision.
The standard approach estimates each term independently via sampling, yielding a query complexity that scales with the number of training data and the 2-norm of the weights...

321. Optimal Stabilizer Testing and Learning with Limited Quantum Memory

Louis Schatzki

Poster

We study stabilizer state testing and learning with limited coherent quantum memory. Here one sequentially receives copies of an unknown n-qubit state, but may keep only k qubits of coherent quantum memory between measurements. With unrestricted memory, seminal work of Gross, Nezami and Walter showed how to test n-qubit stabilizer states using 6 copies unlike the learning complexity of...

228. Optimising Trotter-Suzuki Simulations of Markovian Open Quantum Systems via Classical Search

Shivani Pillay (University of KwaZulu-Natal)

Poster

Developing robust quantum machine learning (QML) algorithms for near-term hardware requires a deep understanding of how environmental noise, such as dissipation and decoherence, impacts algorithmic performance. Investigating these effects necessitates the accurate simulation of open quantum systems, a task typically accomplished on digital quantum computers using Trotter-Suzuki (TS) Product...

283. Optimizing quantum-classical algorithmic resources for accuracy in VQE

HARSHIT VERMA (MajuLab, CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory, 117543 Singapore ; Centre for Quantum Technologies, National University of Singapore, 117543 Singapore)

Poster

Please see the attached PDF.

153. Orbit dimensions in bosonic systems

Eliott Mamon (LIP6, Sorbonne Université)

Poster

(See attached file)

305. Out-of-distribution generalisation for learning quantum channels with low energy coherent states

Leonardo Banchi (University of Florence)

Poster

When experimentally learning the action of a continuous-variable quantum process by probing it with inputs, there will often be some restriction on the input states used. One experimentally simple way to probe a quantum channel is to use low-energy coherent states. Learning a quantum channel in this way presents difficulties, due to the fact that two channels may act similarly on low-energy...

159. Parameterised graph theory for tensor networks: rerouting entanglement, simplifying structure, and tomography

Natalie McHugh (University of Warwick), Dr Sergii Strelchuk (University of Oxford), Matthias C Caro (University of Warwick)

Poster

Parameterised graph theory studies how the complexity of solving graph-theoretic problems depends on specific parameters of a graph. This perspective has already proven fruitful for understanding the complexity of simulating quantum circuits through tensor network (TNs). However, beyond simulation, the scope of what graph theory has to offer for TNs remains underexplored. For example, how do...

245. Parametrized-circuit-free quantum regression with variance regularization

Yerassyl Balkybek (Skolkovo Institute of Science and Technology)

Poster

Quantum regression tasks for predicting properties of quantum states are commonly addressed using variational quantum algorithms. While variational quantum circuits are highly expressive and allow to achieve reasonable accuracy, training these circuits may demand a considerable amount of time and resources. In this work, we propose an approach of constructing problem-specific quantum...

50. Parity Complexity Governs Quantum Kernel Advantage in XOR-Structured Classification

Tushar Pandey

Poster

We show that a quantum ZZ kernel's advantage over classical baselines is governed by one axis: parity
complexity n, the number of features in the XOR rule. On a synthetic XOR benchmark with 22% label noise, we
compare the quantum kernel against an RBF kernel on the identical binary {0, pi} encoding, which isolates
the circuit from the encoding. A clean threshold appears: below n=9 the...

215. Particle-Number-Conserving Quantum Autoencoders for Certifiable Molecular Compression

Jian Feng Kong (Institute of Advanced Intelligence and Computing, A*STAR)

Poster

Quantum autoencoders (QAEs) compress families of quantum states onto a few latent qubits, but their latent space is a black box: one can neither certify what it contains nor read it in physical terms. We show that one physically motivated constraint, restricting the encoder to conserve particle number, resolves both problems at once. The latent becomes operationally certifiable (measuring the...

65. Pauli Noise Annealing for Escaping Optimization Stagnation in Variational Quantum Eigensolvers

Jungyun Lee (Yonsei Univ.)

Poster

Variational quantum eigensolvers (VQEs) provide a near-term approach to ground-state energy estimation, but their performance is hindered by nonconvex loss landscapes with local minima and saddle points. Although noise is usually viewed as detrimental, recent studies show that structured Pauli noise can instead regularize loss landscapes by suppressing high-frequency components. Motivated by...

157. Performance Guarantees for Quantum Neural Estimation of Entropies

Mark Wilde (Cornell University), Sreejith Sreekumar (L2S, CNRS, CentraleSupelec, University Paris-Saclay), Prof. Ziv Goldfeld (Cornell University)

Poster

Estimating quantum entropies and divergences is an important problem in quantum physics, information theory, and machine learning. Quantum neural estimators (QNEs), which utilize a hybrid classical-quantum architecture, have recently emerged as an appealing computational framework for estimating these measures. Such estimators combine classical neural networks with parametrized quantum...

71. Photonic quantum machine learning methods for Malware classification

Grégoire Barrué (Orange Innovation)

Poster

We propose a benchmark of several photonic approaches in order to classify malware and benign PE file. This unusual use-case for QML combined with the use of the photonic technology allows to show that this technology is suitable for our cybersecurity use-case and that it has promising results.

254. Portfolio Optimisation and Non-Stationary Market Forecasting: Dual Computational Frameworks using Classical and Quantum Algorithms

Shawal Kassim (PhD Student University of the Witwatersrand)

Poster

Modern Portfolio Optimisation faces computational bottlenecks. As the number of assets grow, the number of combinatorial solutions grow exponentially and become computationally difficult for classical computers to solve high dimensional problems from these large datasets, in the necessary time required. Financial computational strategies rely on the ability to acquire high-dimensional...

75. Post-Selection Guided Encodings for QML

Gustav Jäger (German Aerospace Center (DLR e.V.))

Poster

This work pioneers an approach to QML that incorporates post-selection into its workflow. As a scarce resource tied directly to the number of reasonably available shots, I investigate a replacement strategy for utilized post-selection. Post-selection is introduced into the QML model by adding trainable controlled rotations onto post-selected ancillas, which allows for the partial...

72. PPO-QGP: Automated Quantum Kernel Design for Gaussian Process Regression

Kerem Bükrü

Poster

Gaussian process regression is a widely used kernel-based supervised machine learning method. The kernel choice highly affects the regression quality and usually requires extensive hyperparameter tuning. Quantum fidelity kernels have emerged as expressive alternatives for regression tasks. However, designing a suitable circuit requires domain-specific knowledge and is often done heuristically....

190. Practical Quantum Error Mitigation for Noisy Quantum Hardware

Daniil Rabinovich (Russian Quantum Center), Mr Nikita Korolev (Russian Quantum Center), Mr Zahar Sayapin (Skolkovo Institute of Science and Technology)

Poster

Practical deployment of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices requires error mitigation techniques that remain effective under realistic experimental constraints. Existing approaches often rely on accurate noise characterization, simplified assumptions about hardware errors, or substantial computational overhead, limiting their scalability in practical settings....

255. Probabilistic modeling over permutations using quantum computers

Vasilis Belis (Xanadu)

Poster

Quantum computers provide a super-exponential speedup for performing a Fourier transform over the symmetric group, an ability for which practical use cases have remained elusive so far. In this work, we leverage this ability to unlock spectral methods for machine learning over permutation-structured data, which appear in applications such as multi-object tracking and recommendation systems....

300. Probing Nontrivial Betti Numbers with Variational Quantum Methods

Zherui Wang (Leiden University)

Poster

Topological data analysis (TDA) extracts robust global features from high-dimensional data by representing data sets as simplicial complexes. A central challenge is to compute topological invariants of these complexes, especially the $k^{\mathrm{th}}$ Betti number, whose nonzero value indicates the presence of a $k$-dimensional hole. In this work, we study the decision problem of determining...

275. Protocol Dependent Scaling of a Single-Qubit Re-Uploading Classifier on QKD Round Data

Mr Vighneshwar Vighneshwar (IISER Tirupati)

Poster

The data re-uploading classifier of P'erez-Salinas et al. (2020) demonstrated that a single qubit, repeatedly re-encoded with classical input across sequential processing layers, can act as a universal classifier on geometric pattern-recognition benchmarks. This work evaluates whether the same architecture-with no modification to its encoding scheme, layer structure, or fidelity-based cost...

299. Provable learning separation for predicting time-evolution of quantum many-body systems

Rahul Bandyopadhyay (Leiden University)

Poster

Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantum machine learning (QML) tasks that exhibit learning separations? We address this problem by studying the learnability of quantum many-body dynamics from a probably approximately correct (PAC)-learning perspective....

266. Provable quantum–classical separation for continuous Gibbs sampling

Sehmimul Hoque (Institute for Quantum Computing, Department of Physics and Astronomy, University of Waterloo, Perimeter Institute)

Poster

Demonstrating a quantum advantage over classical computational capabilities is a central objective of quantum algorithms research. While there are numerous quantum algorithms that outperform the best known classical methods, quantum-classical separation results are comparatively rare, especially for sampling problems over continuous domain. The best known quantum–classical separation results,...

189. QCBM Improves the Performance of Classification Based on Some Graph Kernels

Dr Karim Essafi (QunaSys ApS), Dr Ronin Wu (QunaSys ApS)

Poster

We employ QCBM to enlarge molecular datasets with artificially generated samples for training SVMs to enhance the classification performance of selected kernel methods. We focus on several datasets including blood-brain barrier permeability prediction, an important task in early-stage drug discovery. On a controlled synthetic benchmark mirroring the topology of AIDS/PROTEINS/NCI1,...

38. QGSP: Quantum Graph Spectral Positioning for Graph Representation Learning

Dr Li Zhong

Poster

Graphs arise throughout molecular chemistry, social networks, and biology, but current quantum methods for graph learning often either ignore global graph geometry or depend on variational circuits that are difficult to train on near-term devices. We introduce QGSP (Quantum Graph Spectral Positioning), a parameter-free quantum graph embedding method that encodes graph structure into...

320. Quantum Advantage in Predicting Ground State Properties Beyond Constant Gaps

Nathan McMahon (Leiden University)

Poster

A central challenge in quantum machine learning (QML) is identifying physically motivated learning problems for which quantum computers exhibit advantages over classical computers. A prime candidate for such a learning task is the prediction of ground state properties of some quantum system of interest. Surprisingly, however, the prediction of ground state expectation values and classical...

89. Quantum Anomaly Detection for Future Gravitational-Wave Observatories

Domenica Dibenedetto (Maastricht University)

Poster

Future gravitational-wave (GW) observatories such as LISA and third-generation ground-based detectors will produce long, high-sensitivity data streams with increased source overlap and weak-signal regimes. We investigate quantum machine-learning approaches for semi-supervised GW anomaly detection, focusing on Quantum Variational Rewinding (QVR) and Quantum Reservoir Computing (QRC). QVR is...

263. Quantum Circuit Synthesis Using Projective Quantum Drifting Model

Hoang Anh Nguyen (Quantum AI & Cyber Security Institute (QACI))

Poster

Generating compact quantum circuits for a target unitary is a key problem in quantum compilation. In this work, we propose Projective Quantum Drifting model (PQD), a geometry-guided framework for target-unitary circuit synthesis. PQD progressively refines an initial unitary through projective drift directions, producing residual circuit blocks that improve the approximation. We compare PQD...

95. Quantum Decoding Algorithms

Jan Ljubas (University of Copenhagen / Shanghai Jiao Tong University / NYU Shanghai)

Fault tolerant/quantum algos

Poster

Quantum Decoding Algorithms (QDA) provide a new framework for solving combinatorial optimization problems by combining ideas from coding theory with quantum interference and quantum Fourier transform. The central algorithm within this framework is Decoded Quantum Interferometry (DQI), which reformulates optimization problems such as max-LINSAT through algebraic encodings that enable quantum...

111. Quantum Diffusion Models for Medical Imaging

Francesco Aldo Venturelli (Universitat Pompeu Fabra, Quantic Barcelona Supercomputing Center)

Poster

Diffusion Models (DMs) are a leading class of physics-inspired generative AI (GenAI) models. DMs obtained state-of-the-art results with Denoising Diffusion Probabilistic Models (DDPMs), which progressively add noise to data and then reverse the process by learning the noise added at each forward time step (denoising). Despite their benefits, they require intensive computational resources, long...

265. Quantum element-wise transforms

Zane Rossi (The University of Tokyo)

Poster

Quantum algorithms for basic numerical linear algebraic tasks have proven essential for translating diverse problems to a unified quantum computational context. Many of these tasks—e.g., applying a function to the spectrum of a matrix embedded in a unitary process (a so-called block encoding)—are well-addressed by techniques like quantum singular value transformation (QSVT). However, there...

297. Quantum Evolution Kernel for Node-Attributed Graphs

Manuel Padovani (University of Verona)

Poster

We present a novel approach to embed node-attributed graphs onto neutral-atom quantum computers, which extends PASQAL's quantum evolution kernel (QEK) by introducing a node-level feature encoding based on three-level quantum states. Despite recent advances, the incorporation of multiple continuous node attributes into analog quantum graph kernels remains a challenging and mostly unexplored...

206. Quantum Fourier Generative Models Trainable at Large Scale

Cenk Tüysüz (CERN)

Poster

We propose an algorithmic framework for building and training quantum generative models corresponding to multivariate probability distributions. Our model uses parallel Fourier feature maps for embedding continuous-valued variables combined with a forrelation-type quantum circuit for tuning Fourier coefficients of the quantum model. Crucially, we develop a distinct training strategy where...

144. Quantum Framework for Simulating Linear PDEs with Robin Boundary Conditions

Mr Nikita Guseynov (Shanghai Jiao Tong University), Mr Xiajie Huang (Shanghai Jiao Tong University)

Poster

We present a quantum framework for simulating broad classes of linear PDEs with space-- and time-dependent coefficients, inhomogeneous terms, mixed derivatives, and general boundary data. The method covers Robin boundary conditions, with Neumann, Dirichlet, and periodic cases obtained through the corresponding discretized operators. Starting from finite differences, the framework homogenizes...

169. Quantum hybrid feature selector

Alexey Melnikov (Terra Quantum AG)

Poster

Unsupervised feature selection is essential for high-dimensional machine learning, improving model quality and efficiency while providing interpretable insights. Existing methods struggle to simultaneously achieve robustness, interpretability, and computational efficiency, and feature selection remains largely unexplored in quantum machine learning despite the potential of quantum models to...

74. Quantum Layers for Deep Neural Network

Domenica Dibenedetto (Maastricht University)

Poster

Integrating quantum computation into deep neural networks remains a central challenge in quantum machine learning, because quantum measurements are probabilistic and do not naturally fit standard gradient-based optimization pipelines. This work investigates trainable quantum layers as modular representation-learning components that can be inserted into pre-trained classical neural networks...

287. Quantum Learning of Classical Correlations with continuous-domain Pauli Correlation Encoding

Vicente P. Soloviev (Fujitsu Research Ltd.)

Poster

We propose a quantum machine learning framework for estimating classical covariance matrices using parameterized quantum circuits within the Pauli-Correlation-Encoding (PCE) paradigm. We introduce two quantum covariance estimators: the \emph{C-Estimator}, which constructs the covariance matrix through a Cholesky-type factorization to enforce positive (semi)definiteness, and a computationally...

136. Quantum Machine Learning Architectures for Semi-Supervised Time-Series Anomaly Detection

Jesús Bonilla García (ARQUIMEA Research Center)

Poster

Semi-supervised anomaly detection in multivariate time series requires models capable of learning normal system dynamics while remaining efficient enough for deployment in resource-constrained environments. We investigate two lightweight quantum machine learning architectures that address this challenge through complementary paradigms: Quantum Reservoir Computing (QRC), which exploits the...

132. Quantum memory advantage for quantum process tomography

Mr Antonio Anna Mele (FU Berlin)

Talk

A central open problem in quantum process tomography is whether access to quantum memory yields a learning separation for arbitrary quantum channels. Coherent protocols may preserve quantum information across channel uses, whereas incoherent protocols must measure after each use and proceed only with classical memory. The remaining question is whether classical adaptive protocols can...

327. Quantum Neural ODEs: Towards Exponential Quantum Advantage in Inference and Learning

Abhinav Muraleedharan (University of Toronto)

Poster

We present quantum algorithms that implement a class of exponentially large Neural Differential Equations (Neural ODEs) on quantum computers using only polynomial quantum resources. Neural ODEs define the forward propagation of a neural network as the solution of a nonlinear differential equation, so from this viewpoint, the forward pass (inference) becomes a simulation problem. We show that...

258. Quantum Neuromorphic Vision: Event-Based Analog Quantum Graph Neural Network

Kristian Sotirov (King's College London)

Poster

Asynchronous event-based graph neural networks (AEGNNs)
have recently emerged as an efficient paradigm for processing sparse
and high-temporal-resolution data from neuromorphic cameras. Here,
we propose quantum analog AEGNNs (QA-AEGNNs), a novel frame-
work to implement AEGNNs on neutral-atom quantum computers. The
streaming event data, produced by a neuromorphic camera, is mapped
to an...

252. Quantum Optimisation Algorithms on Calculating Ramsey Numbers

Shawal Kassim (PhD Student University of the Witwatersrand)

Poster

Computing Ramsey numbers $R(k,l)=n$ involves finding the smallest graph of order $n$ that contains either a $k$-clique or an $l$-independent set under a two-coloring [1]. This presents a formidable computational challenge because the classical search space grows super-exponentially with the number of vertices, making the problem intractable.This work utilizes Quantum Optimization Algorithms on...

28. Quantum ring all-reduce: communication and privacy advantages for distributed learning

Maria Gragera Garces (University of Edinburgh)

Poster

We show that a quantum communication layer enables provable advantages throughout the
distributed learning pipeline, regardless of the model's nature (classical or quantum).
Starting from ring all-reduce (the universal primitive for large-scale gradient
aggregation) we prove that pre-shared entanglement reduces per-link \emph{online} communication by a
provably optimal factor of two and...

52. Quantum Self Supervised Learning for Semantic Segmentation Application

Dr Tirupati Bolisetti (University of WIndsor), Wassim Hadj-Cherif (Nantes Université)

Poster

Semantic segmentation underpins scene understanding, yet pixel-level annotation remains the main issue in domains such as medical imaging or remote sensing. Self-supervised learning (SSL) extracts rich representations without labels, and quantum computing offers high-dimensional Hilbert-space encodings; combining the two, however, is constrained by the limited qubit budgets of the Noisy...

247. Quantum Simulation of Open Quantum Systems

Prof. Ilya Sinayskiy (University of KwaZulu-Natal)

Poster

A simulation of quantum systems is one of the most exciting use cases for quantum computers. The simulation of closed quantum systems, or Hamiltonian simulation, has been explored in recent years. Novel methods have been developed, improving the widely used and well-known Suzuki Lie Trotter product formulas. However, in many practical situations, one must consider unavoidable interaction with...

196. Quantum Spectral Anomaly Detection

Yewei Yuan (Shanghai Jiao Tong University)

Poster

A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal. Classically, principal component analysis (PCA) for centered data computes the anomaly score by evaluating the test sample relative to the subspace spanned by the selected leading eigenvectors. However, for quantum...

323. Quantum spectral proper orthogonal decomposition

Jona Erle (University of Oxford)

Poster

Spectral proper orthogonal decomposition (SPOD) is a widely used method for extracting physically meaningful dominant structures from turbulent flows, at a classical cost $\tilde{O}(MN_fL^2)$ dominated by a spectral decomposition at each of $N_f$ frequencies. We propose a quantum algorithm, qSPOD, that applies a quantum Fourier transform to represent these frequency-wise problems as a single...

115. Quantum Tilted Loss in Variational Quantum Optimization: Theory and Applications

Dr Josep Lumbreras (Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore), Yixian Qiu (Centre for Quantum Technologies, National University of Singapore, Singapore)

Poster

Variational quantum algorithms are promising for near-term quantum devices, but their optimization is hindered by barren plateaus and finite-shot noise. We introduce the Quantum Tilted Loss (QTL), a one-parameter nonlinear objective obtained by exponentially tilting measurement outcomes of an observable. The tilt selects the relevant spectral region: zero tilt recovers the expectation value,...

185. Quantum Topological Data Encoding

Adam Wesolowski (Royal Holloway University of London), Prof. Daniel Leykam (Science, Mathematics and Technology Cluster, Singapore University of Technology and Design), Mr Dimitrios Thanos (Leiden Institute of Advanced Computer Science (LIACS), Leiden University), Lirandë Pira (NUS)

Poster

Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations. Quantum machine learning offers the possibility of processing high-dimensional data in Hilbert spaces, but its practical success depends critically on how classical data is encoded into quantum states. We...

139. Quantum Walk-Based Exploration of Drug-Like Chemical Space

Mohamed Iheb Hergli (Laboratoire MEEP – LR16IT04 - Institut Pasteur de Tunis – Université de Tunis el Manar, Tunisie)

Poster

Motivation
Drug lead optimization requires navigating a chemical space estimated at 10⁶⁰ molecules. Classical approaches like evolutionary algorithms, Bayesian optimization, and reinforcement learning build exploration distributions from past fitness evaluations, so they cannot amplify probability toward unobserved, high-fitness regions, a structural limitation, not a scaling...

276. Quantum-efficient architecture within a hybrid neural pipeline for speech enhancement

Marius-Alexandru Olaru (Alexandru Ioan Cuza's University of Iasi)

Poster

Current research increasingly focuses on integrating parameterized quantum circuits into neural networks to create hybrid models capable of achieving performance competitive with classical counterparts—yet with fewer parameters—in machine learning tasks. However, optimizing the quantum architecture remains a bottleneck for the advancement of hybrid models. Our study proposes an efficient...

35. Quantum-Enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters

Mr Borja Aizpurua (Multiverse Computing Research SL)

Poster

We introduce Cayley Unitary Adapters (CUAs), a hardware-efficient way to inject trainable quantum parameters into a frozen large language model (LLM). Each adapter is a block-diagonal unitary whose blocks are parameterised by the Cayley transform of a skew-symmetric matrix; the parameters are trained entirely classically by knowledge distillation, and the trained two-qubit blocks are then...

149. Quantum-Enhanced Reinforcement Learning for Robotic Control

M. Lautaro Hickmann (Institute for AI Safety and Security, German Aerospace Center (DLR e.V.))

Poster

A wide variety of Quantum Machine Learning approaches, particularly those employing Variational Quantum Circuits (VQCs), have shown potential for solving learning tasks beyond classical capabilities. The combination of quantum circuits and Reinforcement Learning (RL), known as Quantum Reinforcement Learning (QRL), has recently been explored to enhance learning efficiency in challenging...

121. Quantum-Enhanced Topological Data Analysis for Robust Recovery of Cyclic Manifolds

Zuzana Leova (University of Melbourne)

Poster

High-dimensional, sparse datasets capturing continuous biological processes, such as the cell cycle, are fundamental to understanding disease progression, such as cancer [1]. However, recovering these cyclic structures from highly stochastic data severely challenges classical machine learning. Standard metrics like Euclidean distance measure linear paths between data points; in noisy spaces...

9. Quantum-Inspired Machine Learning for Bioimaging: Two State-Discrimination Case Studies

Giuseppe Sergioli (University of Cagliari)

Poster

Please, see attachment

19. Quantum-inspired Phylogenetic Tree Construction

Yeuko Manganyi (University of Cape Town)

Poster

Traditional heuristics for phylogenetic tree construction are classically intractable due to the NP hard nature of the problem, this is because of the super-exponential growth of the number of possible tree topologies. In other words, the search space is far too large for classical computations This projects looks into the use of Quantum algorithms, specifically Quantum Approximate...

270. QuCLIP: A Structured Quantum Vision-Language Model

Mr Tilen Limbäck-Stokin (University College London)

Poster

Current Vision-Language Models (VLMs) often fail at compositional reasoning, treating text inputs as bag of words. To address this without relying on large, classical negative-sampled datasets, we introduce QuCLIP, a language Informed quantum vision-language model.

QuCLIP maps the derivations of a linguistic framework with a transparent syntax/semantics interfact onto Variational Quantum...

34. QuSOP: a quantum learning–optimisation framework for robust dose selection in early clinical development

Dr Kuan-Cheng Chen (Imperial College London)

Poster

Dose selection in early-stage clinical trials often relies on pharmacokinetic–pharmacodynamic (PK/PD) models, where limited data, inter-subject variability and safety constraints make optimisation challenging. We introduce \textbf{QuSOP}, a hybrid quantum-classical framework for safe dose selection in Phase 1 trials. QuSOP combines a dual long short-term memory (LSTM) pipeline with Monte Carlo...

103. Ravines in quantum cost landscapes: opportunities for enhanced VQA predictions

Felix J. Beckmann (Fraunhofer Institute for Industrial Engineering IAO)

Poster

The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs). We systematically analyze ravines— low-cost paths connecting local minima — using an adapted version of the nudged elastic band (NEB) algorithm, a method originating from theoretical chemistry. By training quantum neural...

94. Reinforcement Learning for Optimal Control in Quantum-Computer-Enhanced Electron Microscopy

Sebastian Egginger (Johannes Kepler University Linz)

Poster

Standard intensity-based detectors in electron microscopy measure particles such as photons or electrons individually, thereby collapsing their quantum states and preventing entanglement-enhanced metrology. To overcome this, we are part of a project developing a novel setup that integrates a trapped-ion quantum computer into a transmission electron microscope, thereby transforming the quantum...

15. Reinforcement learning for quantum environments with memory

Josep Lumbreras (Nanyang Technological University), Dr Ruo Cheng Huang (Nanyang Technological University)

Poster

In reinforcement learning, an agent interacts sequentially with an environment to maximize a reward, receiving only partial, probabilistic feedback. This creates a fundamental exploration-exploitation trade-off: the agent must explore to learn the hidden dynamics while exploiting this knowledge to maximize its target objective. While this framework has been extensively studied classically,...

73. Reinforcement learning-based quantum feature map search leveraging magic and generalization bounds

Jan Schnabel (Fraunhofer Institute for Manufacturing Engineering and Automation IPA)

Learning Theory

Poster

Please find the abstract attached as a PDF document.

141. Reinforcement-Guided Adaptive Aggregation for Distributed Quantum Machine Learning

Nouhaila Innan (New York University Abu Dhabi), Walid El Maouaki (New York University Abu Dhabi)

Poster

Distributed quantum machine learning (DQML) provides a promising route for training quantum and hybrid quantum-classical models across multiple clients while keeping data local. This setting is especially relevant when data are heterogeneous and distributed across institutions, devices, or quantum processors, and direct data sharing is restricted or inefficient. However, the performance of...

162. Reinforcement-Learning-Guided Generative Synthesis of Near-Optimal Quantum Circuits

Ayman Tarig (African Institute for Mathematical Sciences)

Poster

Two-qubit gates dominate error budgets on today's quantum hardware, so compiling a target unitary into a circuit with as few two-qubit gates as possible is central to accurate quantum computation. Standard exact decompositions apply a fixed construction to every target and overspend whenever a specific unitary admits a cheaper circuit---a gap that widens with system size. We ask whether a...

60. Representational Biases in Quantum Image Encodings: A Functional and Spectral Perspective

Sergio Hernández López (University of Deusto)

Poster

Data encoding plays a central role in Quantum Machine Learning, determining the class of functions that a quantum model can represent. While Fourier-based analyses have provided valuable insights into angle-encoded models, the representational biases induced by commonly used quantum image encodings remain poorly understood.
In this work, we investigate the functional structure induced by...

77. Reservoir computing architecture with integrated photonic circuit for time-series forecasting

Valeria Cimini (Sapienza University of Rome)

Poster

Quantum reservoir systems offer a promising route to mitigate some of the optimization challenges affecting variational quantum machine-learning models, including trainability issues. In reservoir computing, the quantum dynamics are kept fixed, while only a classical readout is trained, making the approach particularly suitable for near-term photonic implementations. However, only a few...

142. Resource-efficient and robust quantum federated learning via loss aggregation

Mr Cheng-En Tsai (National Taiwan University)

Poster

Federated learning across quantum devices offers a pathway to scalable and privacy-preserving quantum machine learning. However, existing frameworks exchange large gradients or model parameters, creating severe communication and execution overheads that scale with system size and often destabilize training on heterogeneous client data. We introduce Loss Aggregation Quantum Federated Learning...

257. Robust Classification and Purification with a Single Tensor Network Born Machine

Martin Lazaro Nissen Gonzalez (University of Caen Normandy, GREYC)

Poster

Adversarial robustness is closely connected to how well classifiers capture the structure of the data distribution, motivating renewed interest in generative classifiers. We study this connection for matrix product state Born machines, tensor network models whose tractable normalization makes the learned joint density directly accessible. This tractability enables two things at once: detection...

108. Rotary Position Embeddings as Quantum Data Encodings

Sergii Strelchuk (University of Oxford)

Poster

The Levenshtein (edit) distance is a fundamental measure of string similarity, central to genomics, natural-language processing, databases, and coding theory.
However, it is costly to compute, and Hamming-based quantum fingerprints are ill-suited for it, since rigid position alignment collapses under a single insertion or deletion.
We introduce a quantum encoding of sequences, built from...

238. Routing Anonymity and Identifiability of Noisy Quantum Hardware

Ben Priestley (University of Cambridge)

Poster

Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider's proprietary backend hardware. While providers may wish to hide implementation details, scheduling choices, or even which physical device was used, noisy finite-shot outputs can carry backend-specific fingerprints: information imprinted in the classical output distribution that can reveal the...

146. Rydberg-Atom Quantum Reservoir Computing for Insurance Time-Series Forecasting

Mr George Woodman (AXA), Nathaniel Pacey (EPFL)

Poster

Quantum reservoir computing (QRC) offers a hardware-compatible alternative to variational quantum machine learning by using fixed quantum dynamics as a nonlinear reservoir and training only a classical readout. This work investigates Rydberg-atom QRC for next-step forecasting of insurance-relevant time series, where data are often short, sparse, nonlinear, seasonal, and affected by weather or...

286. Sample-based training of quantum Boltzmann machines

Cenk Tüysüz (CERN)

Poster

Quantum computers can sample from probability distributions believed to be classically intractable, providing a foundation for quantum generative modeling. Training such models, rather than their expressivity, has emerged as the central bottleneck. Gradient estimation by parameter-shift or finite-difference methods requires separate circuit executions for each parameter, a cost amplified by...

221. Scalable certification of coherent crosstalk through channel entanglement detection

Júlia Barberà Rodríguez (ICFO (Institute of Photonic Sciences))

Poster

As quantum processors scale, understanding the structure of the noise affecting their operations becomes essential for error mitigation and error correction. Many widely used characterization and mitigation methods assume the noise to be effectively stochastic, yet coherent crosstalk persists in state-of-the-art devices, and testing such assumptions typically requires full process tomography....

120. Scalable Hardware-Aware Quantum Feature Map Search for Quantum Kernel Learning

Danel Arias Alamo (Global Data Quantum // University of Deusto)

Poster

Quantum kernel methods rely on data-dependent feature maps to embed classical inputs into quantum states, where the induced fidelity kernel defines the geometry used by a classical learning algorithm. The choice of feature map is therefore a central bottleneck in quantum machine learning: generic encodings are hardware-friendly but may impose weak inductive bias, while structure-aware...

278. Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler–Leman Hierarchy

Snehal Raj (QC Ware/LIP6 CNRS)

Poster

Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the recent progress in learning from such data through message passing, a single primitive that generalises convolution and attention. Quantum counterparts have been proposed, but with limited connection to message passing and few guarantees on...

117. Scalable On-Hardware Training of Quantum Neural Networks: Application to Clinical Data Imputation

Natansh Mathur

Poster

Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes. In this work, we introduce a training framework that reduces...

256. Scalable self-configuring local Gaussian-process optimizer for variational quantum circuits

Viacheslav Kuzmin (Terra Quantum)

Poster

Training variational quantum circuits (QAOA, VQE, and quantum neural-network (QNN) models) is a noisy, shot-expensive stochastic optimization problem. The de-facto standard optimizer, SPSA, is powerful but performs well only once its hyperparameters are tuned per problem, and that tuning is itself a shot-hungry optimization.

We present a scalable local Gaussian-process-model (GPM) optimizer...

290. Scalable Tuning of Spin Qubit Arrays via Set Transformer

Bartosz Kreft

Poster

Tuning semiconductor spin-qubit arrays becomes intractable as devices scale from a few quantum dots to arrays of tens: cross-talk between gates forces manual tuning that already takes days for an 8-qubit register. Existing automation analyses each charge stability diagram (CSD) in isolation under the constant-interaction model, which fixes capacitances and discards tunnel coupling, the...

37. Scalable, self-verifying variational quantum eigensolver using adiabatic warm starts

Bojan Žunkovič (University of Ljubljana, Faculty of computer and information science)

Poster

Variational quantum eigensolvers (VQEs) are among the most widely studied hybrid quantum–classical algorithms for near-term devices. Yet, they are held back by three persistent obstacles: barren plateaus, convergence to spurious local optima, and the lack of an intrinsic certificate that the optimized state is the desired ground state.

We introduce and analyze an adiabatic VQE that...

84. Scaling Behavior of Quantum and Classical Models Under Data Constraints: A Controlled Benchmark Study

Favour Alfred (Ladoke Akintola University of Technology and Africa Quantum Consortium)

Variational QML

Poster

While quantum computing has introduced new
directions for computational modeling, there remains limited
empirical understanding of how quantum-inspired models behave
relative to classical approaches under varying data availability
conditions. Most existing studies evaluate models under fixed
dataset settings, limiting insight into how performance evolves
across realistic data...

123. Self-Supervised Pretraining with QCGAN-Synthesized ECGs for Abnormality Classification

Dr Jun Seita (RIKEN), Dr Naoki Nonaka (RIKEN)

Poster

Quantum generative modeling has attracted attention as a potential approach for medical time-series AI, where limited labeled data, class imbalance, and acquisition heterogeneity hinder robust representation learning. Because ECG diagnostic information is encoded in waveform morphology, periodicity, and temporal dependencies, we investigate whether ECGs synthesized by quantum conditional...

11. Sequential Spatiotemporal Magnetic-Field Reconstruction via Quantum Hamiltonian Learning with NV-Center Spin-1 Hamiltonians

Hiroshi Yamauchi (SoftBank Corp.)

Poster

We propose a quantum-Hamiltonian-learning-based sequential reconstruction framework for dynamic two-dimensional magnetic-field maps using a local likelihood model derived from a nitrogen-vacancy-center spin-1 Hamiltonian. Local measurements are generated through nitrogen-vacancy spin dynamics governed by local magnetic-field values and a shared dipolar coupling parameter, rather than by direct...

131. Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks

Maniraman Periyasamy (Terra Quantum AG)

Poster

Data loading is a bottleneck for near-term quantum machine learning: angle, amplitude, and basis encodings either underuse the exponential Hilbert space or need circuit depths beyond noisy intermediate-scale quantum (NISQ) coherence budgets. We introduce Shot-Based Quantum Encoding (SBQE), which embeds a datum in a data-dependent classical distribution over a fixed basis-state pool,...

225. Signature-Based Backdoors in Variational Quantum Models

Eleanor Kedem

Poster

Recent advances in quantum machine learning raise a basic security question: can malicious functionality be hidden inside a parameterized quantum classifier in a way that is both controllable by the attacker and difficult to recognize from the expected circuit structure? In classical machine learning, recent work on undetectable backdoors has shown that cryptographically structured triggers...

262. Simulation cost of classically simulable quantum evolutions

Su Yeon Chang (Los Alamos National Laboratory)

Poster

Over the past years, numerous quantum circuit architectures have been proposed to achieve quantum advantage on noisy quantum devices. However, many widely studied (parameterized) quantum circuits have been shown to be efficiently classically simulable, motivating the development of classical surrogate models for reproducing circuit dynamics. Beyond classical simulability, a natural question...

23. Simultaneous Multi-Anomaly Detection Using Quantum Kernel-Based Machine Learning

Dr takao Tomono (Japan Aerospace Exploration Agency)

Poster

Maintaining production equipment in manufacturing is critically important. As the number of monitored machines increases, the computational cost of conventional machine learning approaches grows rapidly. In vibration and acoustic analysis, methods combining autoregressive (AR) model coefficients with one-class support vector machines (OCSVMs) are widely used for anomaly detection. This study...

233. Spatially Informed Quantum Neural Networks for Interpretable Cancer Mutation Classification

Caitlin Jones (University of Edinburgh)

Poster

We introduce a new application of variational quantum models in which the structure is designed to reflect domain-specific spatial information in the data. Rather than treating an image as an unstructured collection of features, our approach represents different spatial regions as quantum subsystems and models their interactions through a parametrised Hamiltonian. The main property of our...

285. Stagnation and false convergence in imaginary-time evolution

Suwannachad Suwannajitt (Chulalongkorn university)

Poster

Imaginary-time evolution (ITE) is a quantum primitive for ground-state preparation. Despite guaranteed asymptotic convergence, its runtime can be dominated by finite-time stagnation plateaus where energy gradients vanish, leaving the ground state fidelity small. We formalize this dynamical bottleneck, showing it creates an intrinsic false-convergence problem: gradient-based stopping criteria...

244. Structural Conditions for Efficient Quantum PDE Solving via Tensor Networks

Ramesh Kolluru (BosonQ Psi Corp (BQP))

Poster

Quantum algorithms for partial differential equations (PDEs) are a promising route to computational advantage in fluid dynamics, yet advantage is typically assumed or shown case-by-case rather than derived. We address this gap directly, investigating two architectures within a unified framework in which PDEs are converted to tensor networks and then mapped to quantum circuits: Hamiltonian...

109. Swap Network Augmented Ansätze on Arbitrary Connectivity

Teodor Parella Dilme (ICFO - The Institute of Photonic Sciences)

Poster

Efficient parametrizations of quantum states are essential for trainable hybrid classical-quantum algorithms. A key challenge in their design consists in adapting to the available qubit connectivity of the quantum processor, which limits the capacity to generate correlations between distant qubits in a resource-efficient and trainable manner. In this work we first introduce an algorithm that...

17. Symmetry Is Not a Shortcut: Complexity and Learning Barriers in Orthogonal and Symplectic Quantum Circuits

Oxana Shaya

Poster

We study whether symmetry-constrained quantum circuits can avoid the complexity and learning barriers usually associated with Haar-random models. Focusing on circuits drawn from the special orthogonal and unitary symplectic groups, we show that these structured ensembles still generate states of exponentially large strong state complexity, with analogous behavior for group designs. We further...

316. Tangle-Nonlocality Signatures in Structured Ansatz Ensembles

Carla Rieger (Technical University of Munich)

Poster

Nonlocality certifies entanglement and generalizes to multipartite scenarios. While the two-party case is well characterized, quantifying entanglement beyond two parties is considerably more involved. In the tripartite case, one can visualize the relationship between tripartite entanglement described by the three-tangle [1] and multipartite Bell nonlocality. In this work we find that...

49. The Baseline Trap: One Hidden Setting Can Flip Quantum Decoder Comparisons

Tushar Pandey

Poster

New decoders for quantum error correction are almost always judged by how much they beat a standard classical decoder, BP-OSD. We show this verdict can hinge on a single, easily overlooked setting of that baseline, with no safe default. The setting (the belief-propagation min-sum scaling factor) can change the baseline's error rate more than a hundredfold, and the value best for one kind of...

124. The Cost of Removing Tunability in Quantum Data Re-Uploading

Anthony Yuezhang Liu (National University of Singapore), Lirandë Pira (NUS)

Poster

Fixed encoding data re-uploading quantum circuits provide a striking example of universality emerging from a highly constrained architecture. However, universality alone is insufficient for assessing the theoretical and practical value of fixed and tunable upload circuits. The resource cost of removing tunability remains poorly understood. In this work, we establish quantitative depth-error...

194. The Cross-Kernel Margin: A Robustness Measure for Quantum Kernel Methods

Saarisha Govender (University of KwaZulu-Natal)

Poster

Quantum devices in the current Noisy Intermediate-Scale Quantum (NISQ)
era are inherently affected by noise [1], which degrades the
predictive performance of quantum machine learning models [2]. This
motivates the development of theoretical tools for analysing robustness
under realistic noise. In this work, motivated by the established links
between robustness and generalisation theory...

134. Thermal-Drift Sampling: Generating Thermal Ensembles for Learning Many-Body Systems

Mr Mingrui Jing (The Hong Kong University of Science and Technology (Guangzhou)), Mr Jizhe Lai (The Hong Kong University of Science and Technology (Guangzhou)), Mr Lei Zhang (The Hong Kong University of Science and Technology (Guangzhou))

Poster

Thermal equilibrium states of many-body Hamiltonians are central to studying quantum chaos, phases of matter, and quantum machine learning. Yet generating large ensembles of thermal states across different Hamiltonians remains prohibitively costly with existing methods. Here, we introduce the quantum thermal-drift channel, a new primitive that enables a measurement-controlled sampling...

200. Tight gradient bounds in Parameterized Quantum Reinforcement Learning policies

André Sequeira (INESC TEC)

Poster

PQC-based policies are promising candidates for quantum reinforcement learning, but their trainability can be limited by barren plateaus. We analyze Born-rule quantum policies beyond local 2-design assumptions by deriving tight policy-variance and gradient bounds for practical PQC architectures. For action probabilities defined by projectors onto Hilbert-space subspaces, we prove that, under...

232. Tighter Error Bounds for the qDRIFT Algorithm

Ian Joel David (University of KwaZulu-Natal)

Poster

Recent work in quantum simulation has seen the development of several promising randomised algorithms. The qDRIFT algorithm has some beneficial properties, most notably, its favourable scaling as the number of steps required scales independently of the complexity of the system. However, existing error bounds for qDRIFT scale quadratically with the norm of the generator, limiting their...

106. TN-SHAP-Q: Exact Multilinear Extensions of Quantum Neural Networks for Shapley Attribution of Features and Gates

Farzaneh Heidari (Universite de Montreal, Mila)

Poster

We introduce TN-SHAP-Q, a multilinear framework for computing Shapley values and interactions in quantum neural networks (QNNs). For QNNs with \emph{finite-lift} data encodings, we show that the circuit output is multilinear in the lifted coordinates and, moreover, coincides exactly with the \emph{multilinear extension} (MLE) of the induced cooperative game. This connection unifies feature and...

101. Topological Variational Quantum Kernels: A Geometric Framework for Quantum Machine Learning

Parham Ghayour (Independent Researcher)

Poster

Quantum kernel methods represent one of the most promising paradigms for achieving quantum advantage in machine learning. However, the design of expressive quantum feature maps remains largely heuristic, with little theoretical guidance from the geometry of data or the topology of the quantum state space. In this work, we introduce Topological Variational Quantum Kernels (TVQK) a new family of...

21. Towards Fully Quantum Implicit Neural Representations

Joseph Hankon (University of Southampton)

Poster

This work proposes and evaluates two novel Quantum Implicit Neural Representation (QINR) models for natural image representation. The first, is a near-fully quantum model adopting a conventional Variational Quantum Algorithm (VQA) framework with minimal classical components, tested with four encoding strategies: standard angle encoding via Pauli rotation gates $\hat{R}_x, \hat{R}_y,$ or...

209. Towards interpretable AI with quantum annealing feature selection

Emanuele Costa (Barcelona Supercomputing Center)

Poster

abstract on pdf

261. Towards Surrogate Based Dequantization of Quantum Reinforcement Learning

Pablo Rodriguez-Grasa (University of the Basque Country (UPV/EHU))

Poster

In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement learning, this approach has led to variational quantum algorithms such as quantum Q-learning. While these methods show promising empirical results, and can provide provable advantages for artificial problems, it remains unclear whether they can...

304. Trainability Along Quantum Learning Dynamics Beyond Haar Averages

Mario Herrero González (University of Edinburgh)

Poster

Parameterized quantum circuits (PQCs) are often analysed through uniform, Haar-random, or approximate-design ensembles. These averages give exact barren-plateau statements, but they describe a particular random experiment rather than the process followed by a learning algorithm. A trained circuit is initialized, moved by an optimizer, perturbed by finite-shot or stochastic-gradient noise, and...

180. Trainability and Mode Separation of Mixed IQP-QCBMs

Youngseok Lee (NORMA)

Poster

The quantum circuit Born machine (QCBM) built from an instantaneous quantum polynomial-time (IQP) circuit is classically trainable, while sampling from it is believed to be classically intractable. It is known that the ancilla-free circuit is trainable under suitable initializations, but not universal. Adding ancilla qubits enables universality, yet whether the ancilla-augmented circuit is...

296. Trainability and Tangent-Space Concentration in Parameterized Quantum Circuits

Gabriel Mejia Ruiz (Karlsruhe Institute of Technology)

Poster

In this work, we analyze trainability from the perspective of observable orbits induced by parameterized quantum circuits (PQCs). We study how the number of independent data-visible tangent directions affects the conditioning of the quantum Fisher information matrix (QFIM) and the quantum neural tangent kernel (QNTK). Our main hypothesis is that trainability is determined not only by the total...

271. Trainability Beyond Linearity in Variational Quantum Objectives

Gordon Yuan Ning Ma (Centre for Quantum Technologies, National University of Singapore)

Poster

Barren-plateau theorems establish exponential gradient suppression for variational objectives of the fixed-observable form $\operatorname{Tr}[H \rho(\theta)]$. We give an exact structural characterization of when this form is available: relative to a chosen measurement interface — the set of expectation values the loss may depend on — a fixed-observable representation exists on an explored...

235. Transform-Graph Quantum Generative Models with Scalable Parseval Training

Aaron Thomas (University of Birmingham)

Poster

This work introduces Transform-Graph Quantum Generative Models, a generalised family of classically trainable, quantum-deployable generative models denoted M(T,G,K). The framework extends recent Fourier-DQGM approaches, which avoid the fixed spectral bias of MMD-trained QCBMs by using likelihood-like training with a Parseval hidden-register estimator. While Fourier-DQGM makes training...

53. Triangle Criterion: a mixed-state magic criterion with applications in distillation and detection

Zhenhuan Liu

Poster

We introduce a mixed-state magic criterion, the Triangle Criterion, which plays a role for magic analogous to the Positive Partial Transposition (PPT) Criterion for entanglement: it combines strong detection capability, a clear geometric interpretation, and an operational link to magic distillation. Using this criterion, we uncover several new features of multi-qubit magic distillation and...

133. Turning the Knapsack Problem into a Continuous Optimization using a Quantum-Inspired Variational Method

Álvaro Troyano (Universidad Politécnica de Madrid)

Poster

We encode the binary knapsack problem as the minimization of a diagonal quantum Hamiltonian, where the linear constraint is implemented through a Fourier‑approximated projector onto feasible bitstrings. By working on the manifold of matrix product states (MPS)---implemented natively in PyTorch with automatic differentiation---we turn the combinatorial search into a tractable continuous...

223. Uncoupled Quantum Dynamics and Nash Equilibria

Prof. Emmanouil-Vasileios Vlatakis-Gkaragkounis (University of Wisconsin-Madison), Pucheng Xiong (University of Wisconsin-Madison)

Poster

We study uncoupled learning in quantum games, where each player $i$ updates using only their own payoff information and chooses a density matrix $\rho_i$. Classical results show that such uncoupled learning cannot always converge to Nash equilibrium, and we ask which parts of this picture remain true for quantum strategies. We show that two classical impossibility results still persist: smooth...

302. Unflattening by Flattening - When the Input Distribution Decides Trainability of Angle-Encoded Circuits

Mr Melvin Strobl (Karlsruhe Institute of Technology)

Poster

Barren plateaus are a common phenomenon in quantum machine learning which render parameterized circuits untrainable by flattening the loss landscape.
The Lie-algebraic theory of this effect expresses the loss variance exactly through the dimension of the circuit's dynamical Lie algebra, the readout purity and the purity of the encoded input.
For angle-encoding circuits, the dependence of...

311. Unitary Channel Testing Under a Depolarizing Noise Assumption

Hirak Ghosh (University of Warwick)

Poster

We present fast algorithms to test unitary channels -- under the depolarizing noise assumption, often made in fault-tolerant quantum computations. Our optimal algorithms answer the following question: is the noisy unitary channel implemented by a given black box identical to a target unitary or $\varepsilon$-far from it in the diamond distance, assuming that the deviation is a depolarizing...

10. Variational (Matrix) Product States for Combinatorial Optimization

Guillermo Javier Preisser Beltrán (Quantinuum)

Poster

We present variational methods for combinatorial optimization based on product-state (PS) and matrix-product-state (MPS) ansatzes. The approach performs variational energy minimization of a quantum annealing Hamiltonian and incorporates stochastic exploration through an iterated local search (ILS) metaheuristic. The resulting quantum-inspired optimization algorithms are benchmarked on Max-Cut...

7. Variational Quantum Eigensolver for Designing Metal Organic Framework Optimised for Heavy Metal Sequestration in Galamsey Polluted Waters

Awuley Augustine

Poster

Heavy metal contamination of water bodies caused by artisanal and small-scale gold mining, locally known as galamsey, remains a major environmental and public health challenge in Ghana. Mercury, lead and arsenic-related species are of particular concern because of their toxicity, persistence and potential for bioaccumulation. Metal–organic frameworks are promising adsorbents for heavy metal...

68. Weather-to-Key: Machine Learning for Planning and Operation of Satellite-QKD Ground Stations

Gianluca De Santis (Technology Innovation Institute (UAE); Sorbonne University (France))

Poster

The deployment and operation of satellite quantum-key-distribution (QKD) networks require accurate knowledge of atmospheric turbulence, which determines optical coupling efficiency and therefore directly impacts the secure key rate. Conventional turbulence characterization is based on dedicated optical instrumentation and long-term monitoring campaigns, limiting both site assessment and...

294. Weighted Quantum Signal Processing for Kolmogorov–Arnold Networks: Efficient Activation Function Realization and Quantum Resource Bounds

Rohit Sarma Sarkar (Universitat Politècnica de Catalunya), Rupayan Bhattacharjee (Universitat Politècnica de Catalunya)

Poster

Quantum Signal Processing (QSP) is a powerful approach for generating and approximating univariate polynomials but is limited by increasing circuit depth and parity restrictions. We introduce Weighted Quantum Signal Processing (WQSP), a generalization of QSP that incorporates fixed weights into signal operators, with standard QSP recovered as the unit-weight case. Classical weight encoding...

293. Weighted Quantum Signal Processing: A Resource-Efficient Framework for Univariate Polynomial Approximation

Mr Rohit Sarma Sarkar (Universitat Politècnica de Catalunya), Rupayan Bhattacharjee (Universitat Politècnica de Catalunya)

Poster

Quantum Signal Processing (QSP) generates univariate polynomials but is limited by high circuit depth and parameter cost owing to its parity constraints. We introduce Weighted QSP (WQSP), where a weight function modifies the signal operator, recovering QSP as the unit-weight case. Non-trivial integer weights yield a pruned QSP with significant reduction of parameter count vis-a-vis circuit...

188. What symmetry buys on hardware: Compressing an SU(2)-equivariant ansatz for the kagome Heisenberg antiferromagnet

Mr Besart Lajci (Perimiter), Dr Maciej Koch-Janusz (Haiqu), Richard East

Poster

The ground state of the kagome Heisenberg antiferromagnet is a candidate quantum spin liquid. The lattices natural frustration renders it challenging M.L. task for classical and quantum models. The SU(2)-equivariant ansatz of ~[1] is built from invariant gates, so it stays in one total-spin sector, and on the small
plaquettes where every triangle can host a singlet dimer it prepares the...

82. Witness expansion: a unified framework for analytical and measurable mixed-state resource detection

Yifan Tang

Poster

Quantum information science aims to harness different kinds of quantum resources to accomplish specific information-processing tasks. These resources also play an increasingly important role in addressing fundamental questions concerning quantum phases and dynamics. Therefore, developing powerful and practical methods for identifying and detecting quantum resources is of great significance,...

214. Zero-Noise Extrapolation via Cyclic Permutations of Quantum Circuit Layouts

Daniil Rabinovich (Russian Quantum Center), Nikita Korolev (Russian Quantum Center), Zahar Sayapin (Skolkovo Institute of Science and Technology)

Poster

Increasing the utility of Noisy Intermediate-Scale Quantum (NISQ) devices requires efficient error mitigation, particularly for Variational Quantum Algorithms (VQAs) where accurate expectation values dictate gradient reliability. In this work we propose a novel Cyclic Layout Permutations based Zero Noise Extrapolation (CLP-ZNE) protocol for such a task. The method leverages the inherent...

234. “Train classical, deploy quantum” requires rethinking generalization

Snehal Raj (QC Ware Corp / Sorbonne Université)

Poster

Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum generative models are considered one of the most promising applications for quantum computers, since a quantum circuit naturally produces samples from the distribution it encodes, and for suitable circuits that distribution is believed to be hard...