Title: Dynamic Quantum State Collapse with Feedback (DQC-F)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Abstract: Quantum state collapse is a fundamental challenge in quantum information processing. Achieving high-fidelity quantum measurements relies critically on the accuracy of the collapse process. Static collapse algorithms frequently produce suboptimal results, necessitating continuous optimization. This paper introduces Dynamic Quantum State Collapse with Feedback (DQC-F), a novel algorithm that dynamically adjusts the collapse probability based on the system's internal state, thereby improving the fidelity of quantum measurements. The core mechanism leverages reinforcement learning to iteratively refine the collapse probability, leading to significant improvements in measurement accuracy compared to existing static collapse methods. This approach addresses the limitations of current algorithms by providing a mechanism for continuous improvement, a crucial step toward achieving reliable quantum computation. We detail the algorithm's design, implementation, and experimental validation, demonstrating substantial gains in measurement fidelity across a range of quantum systems.

DOI: 10.5281/zenodo.22184327

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