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Title: Dynamic Quantum Feedback Loops for Algorithmic Convergence

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

This paper explores the potential of Dynamic Quantum Feedback Loops (DQFLs) to enhance the convergence of algorithmic processes. Classical algorithms frequently encounter difficulties in achieving optimal performance, necessitating innovative approaches. We propose a novel system where the algorithm's own parameters are dynamically adjusted based on performance feedback, fostering an iterative refinement process. This investigation focuses on the design and implementation of a DQFL framework, examining its impact on algorithm convergence speed and stability through theoretical analysis and simulation. The core principle is to leverage quantum-inspired feedback to guide the algorithm towards a more stable and efficient state.

DOI
10.5281/zenodo.22219171

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This paper explores the potential of Dynamic Quantum Feedback Loops (DQFLs) to enhance the convergence of algorithmic processes. Classical algorithms frequently encounter difficulties in achieving optimal performance, necessitating innovative approaches. We propose a novel system where the algorithm's own parameters are dynamically adjusted based on performance feedback, fostering an iterative refinement process. This investigation focuses on the design and implementation of a DQFL framework, examining its impact on algorithm convergence speed and stability through theoretical analysis and simulation. The core principle is to leverage quantum-inspired feedback to guide the algorithm towards a more stable and efficient state.
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