Adaptive Quantum Circuit Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum computing promises revolutionary advancements across diverse fields, but its current limitations stem from the complexities of quantum circuit design and optimization. This paper introduces an Adaptive Quantum Circuit Optimization framework, leveraging self-learning to dynamically adjust quantum circuit parameters, thereby enhancing both efficiency and accuracy. We detail the core mechanism, showcasing a method for automated parameter tuning based on a learned model. The framework's advantages include reduced manual intervention, improved adaptability to varying problem instances, and a demonstrable increase in quantum computation performance. We provide a comprehensive illustration of the framework's capabilities through a simplified, yet illustrative, quantum circuit optimization process. The research presents a novel approach to quantum circuit optimization, offering a significant step toward more robust and scalable quantum computing solutions.

DOI: 10.5281/zenodo.22196406

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