Adaptive Dissipative State Preparation through Reinforcement Learning

Abstract:Dissipative algorithms approach the problem of ground state preparation by mimicking the natural thermalization of a quantum system in contact with a large, low-temperature thermal environment. The environment can be efficiently simulated by a single ancilla qubit with a variable energy gap that is repeatedly coupled to the system qubits to generate a dissipative channel, and then reset after each interaction. Here we present an adaptive implementation of the dissipative algorithm, RL-Adapt, that uses single-shot reinforcement learning to optimize the selection of ancilla frequency and system-bath interaction operator to maximize energy dissipation without relying on a priori knowledge of the system spectrum. The adaptive implementation results in significantly reduced convergence times and can successfully find the ground state even for non-ideal operator pools that fail to converge using non-adaptive, uniform random operator and ancilla frequency selection.

arXiv:2609.31370 [quant-ph]

(or arXiv:2609.31370v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.31370 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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