Quantum Techniques in Machine Learning

Session

Contributed talks

7 Dec 2026, 11:00

Jan Mouton Learning Centre (Stellenbosch University)

269. Quantum machine learning advantages beyond hardness of evaluation

Riccardo Molteni (Leiden University)

07/12/2026, 11:00

Talk

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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...