Adaptive Quantum State Collapse Control Algorithm

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper presents an adaptive quantum state collapse control algorithm designed to mitigate the detrimental effects of environmental noise on quantum states. The core of the algorithm relies on a reinforcement learning (RL) framework to dynamically adjust control strategies based on real-time feedback from the quantum environment. Specifically, a controller is constructed utilizing an RL agent that interacts with the quantum system, learning an optimal control policy to minimize state collapse. A key element is the integration of quantum decoherence techniques to reduce the impact of noise. The algorithm's adaptive nature allows it to contend with fluctuating noise levels, a significant challenge in maintaining the coherence of fragile quantum states. The proposed approach offers a novel method for controlling quantum systems, overcoming the limitations of conventional quantum control methods which often rely on static control parameters. The results demonstrate the efficacy of this dynamic control strategy in preserving quantum state fidelity under varying noise conditions. This work contributes to the development of robust quantum control protocols for applications such as quantum computation and quantum sensing.

DOI: 10.5281/zenodo.22140563

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