Title: Dynamic Phase Transitions in Quantum Materials – Adaptive Transition Rate Control

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum materials exhibit unique and often complex behavior, particularly in phase transitions. These transitions are fundamental to many quantum phenomena, and precise control over their rate is crucial for manipulating quantum states and devices. Traditional control methods often struggle to achieve the desired level of precision, particularly when dealing with intricate material geometries and rapidly changing parameters. This paper proposes a novel approach to address this challenge by leveraging adaptive control algorithms to dynamically adjust control parameters during phase transition processes within quantum materials. We introduce a reinforcement learning (RL) framework designed to predict and manipulate material behavior, optimizing the transition rate. This research explores the potential of using RL to enhance the controllability and predictability of quantum phase transitions. The core mechanism involves a dynamic feedback loop that continuously adjusts control parameters based on real-time measurements, leading to improved optimization of transition rates. We present a preliminary implementation of this framework and demonstrate its effectiveness in simulating and predicting phase transition behavior in specific quantum materials. The focus is on providing a practical method for controlling quantum phase transitions, a critical step towards realizing advanced quantum technologies.

DOI: 10.5281/zenodo.22453029

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