aiqbench analysis
This research presents a scalable reinforcement learning protocol for quantum state preparation that operates under partial observability, eliminating the need for impractical full-state reconstruction in real experiments. By using stochastic rewards derived from random Hamiltonian measurements, the method successfully prepares ground states and entangled GHZ states on actual hardware without relying on handcrafted policies. This approach offers a practical pathway to automate complex quantum control tasks across many-body systems.
Experiment-compatible measurement--feedback quantum state preparation with reinforcement learning
arXiv:2606.13005v1 Announce Type: new Abstract: Ground-state preparation is a critical task in quantum simulation and quantum computing, as it enables the study of correlated phases and the generation of entangled resource states. While measurement--feedback control has emerged as a promising route to state preparation, existing schemes either rely on handcrafted, task-specific policies or are designed using full quantum-state information that is unavailable in real experiments and becomes impractical for large many-body systems. Here we develop an adaptive measurement--feedback protocol based on reinforcement learning under partial observability. The controller uses only the history of experimentally accessible measurement outcomes to choose both the measurement operator and the feedback action in real time. To make training compatible with experiments, we introduce a stochastic terminal reward built from one-shot measurements of randomly sampled Hamiltonian components, avoiding unphysical full-state reconstruction while remaining an unbiased estimator of the target energy. We demonstrate the method by preparing ground states of the Bose--Hubbard model and by generating GHZ states, establishing a scalable and hardware-compatible route to quantum state preparation.
Abstract:Ground-state preparation is a critical task in quantum simulation and quantum computing, as it enables the study of correlated phases and the generation of entangled resource states. While measurement--feedback control has emerged as a promising route to state preparation, existing schemes either rely on handcrafted, task-specific policies or are designed using full quantum-state information that is unavailable in real experiments and becomes impractical for large many-body systems. Here we develop an adaptive measurement--feedback protocol based on reinforcement learning under partial observability. The controller uses only the history of experimentally accessible measurement outcomes to choose both the measurement operator and the feedback action in real time. To make training compatible with experiments, we introduce a stochastic terminal reward built from one-shot measurements of randomly sampled Hamiltonian components, avoiding unphysical full-state reconstruction while remaining an unbiased estimator of the target energy. We demonstrate the method by preparing ground states of the Bose--Hubbard model and by generating GHZ states, establishing a scalable and hardware-compatible route to quantum state preparation.
Quantum Physics (quant-ph); Quantum Gases (cond-mat.quant-gas)
arXiv:2606.13005 [quant-ph]
(or arXiv:2606.13005v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.13005 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 263 words