Speaker
Description
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 report a quantum machine-learning model, implemented on a programmable superconducting quantum annealer, that processes temporal data at utility scale using up to 4,500 qubits, to our knowledge the largest quantum machine-learning experiment performed to date. Following the quantum reservoir-computing paradigm, the native many-body dynamics generated by reverse annealing of a transverse-field Ising system transform a streaming input into a high-dimensional representation, while a delayed-feedback channel supplies tunable memory and only a single linear output layer is trained. The reservoir displays non-Markovian memory, with capacity revivals rather than exponential forgetting, and we prove that the interactions produced during annealing are indispensable, a non-interacting reservoir retains no memory of its input. We evaluate our model experimentally on standard memory benchmarks and demonstrate that it can successfully forecast chaotic time series. These results establish quantum annealers as a scalable, optimization-free platform for temporal information processing and chart a practical route toward machine learning on large-scale quantum hardware.
| Topic | Hybrid quantum-classical approaches for learning and optimization |
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