Title: Dynamically Altering Quantum Circuit Parameters via Bayesian Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum circuit design is a complex and computationally intensive process, often requiring extensive trial and error. Traditional methods rely on manually defining and adjusting circuit parameters, limiting the potential for optimization. This paper introduces a novel approach utilizing Bayesian optimization to dynamically alter quantum circuit parameters in real-time, addressing the limitations of static parameter design. We propose a framework where the system's output serves as the objective function for Bayesian optimization, iteratively refining circuit parameters to achieve desired performance characteristics. This approach offers a more robust and adaptive method compared to traditional techniques, potentially unlocking significant improvements in quantum computation. The core mechanism leverages the inherent adaptability of biological systems to guide the optimization process, providing a foundation for a more efficient and effective design methodology. This research explores the potential for leveraging Bayesian optimization to enhance the design and execution of quantum circuits, leading to improved performance and reduced computational cost.

DOI: 10.5281/zenodo.22583536

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