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arXiv · 2609.31370

Adaptive Dissipative State Preparation through Reinforcement Learning

Nathan M. Myers·Chenxu Liu·Yulong Dong·Nicholas P. Bauman·Karol Kowalski
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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.

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Nathan M. Myers, Chenxu Liu, Yulong Dong, Nicholas P. Bauman, Karol Kowalski. 2026-09-25. Adaptive Dissipative State Preparation through Reinforcement Learning. https://arxiv.org/abs/2609.31370

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