Quantum Simulation of Phase Transitions with Adaptive Sampling

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Simulating phase transitions using quantum computers presents a significant challenge due to the complex and often chaotic nature of these transitions. Traditional quantum simulation methods often rely on uniform sampling of the relevant phase space, leading to inefficient resource utilization and prohibitively long simulation times. This work proposes a novel approach to quantum simulation of phase transitions that leverages adaptive sampling, guided by reinforcement learning, to dynamically adjust the sampling strategy. The core idea is to identify and focus on regions of the phase space where the transition is most pronounced, thereby minimizing the overall computational cost. We formulate the problem as a Markov decision process, where the quantum computer's sampling strategy is the agent and the simulation's performance is the reward. Reinforcement learning algorithms are employed to train the agent, enabling it to learn an optimal sampling policy. We demonstrate, through theoretical analysis and conceptual design, the potential of this adaptive sampling method to significantly reduce the computational burden associated with simulating phase transitions while maintaining accuracy. Future work will involve exploring specific algorithms and architectures for implementing this approach on actual quantum hardware.

DOI: 10.5281/zenodo.22317276

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