Quantum Logic Circuit Adaptive Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to optimizing quantum logic circuits through adaptive optimization. The core idea centers around dynamically adjusting circuit parameters based on real-time performance evaluation, leveraging reinforcement learning. A "Quantum Logic Optimizer" is constructed, employing reinforcement learning to assess circuit performance and, crucially, integrating quantum error correction and decoherence mitigation techniques to enable dynamic parameter adjustments. The aim is to enhance circuit efficiency and fidelity. The proposed methodology offers a promising path towards improving the performance of quantum logic circuits, particularly in scenarios where the initial circuit design may not be optimal for all possible inputs or operating conditions. This work explores the potential of combining dynamic optimization with established quantum error mitigation strategies, representing a significant step forward in the field.

DOI: 10.5281/zenodo.22195178

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