Adaptive Quantum Circuit Compilation with Bayesian Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum circuit compilation is a crucial step in the process of executing quantum algorithms on near-term quantum hardware. The challenge lies in translating high-level quantum algorithms, often expressed using universal gate sets, into efficient, gate-level circuits that minimize gate count and circuit depth, thereby reducing error rates and improving execution times. Traditional compilation methods often rely on hand-crafted heuristics or limited search algorithms, failing to fully explore the exponentially large search space of possible circuit transformations. This work proposes an adaptive quantum circuit compilation framework leveraging Bayesian optimization. The system dynamically explores the space of potential circuit transformations, learning to identify optimal compilation strategies through a probabilistic modeling approach. The core claim is that Bayesian optimization can effectively adaptively compile quantum circuits, optimizing for performance and minimizing gate count. The system incorporates a compiler that utilizes Bayesian optimization to explore the vast space of possible quantum circuit transformations, learning to identify the most efficient compilation strategies. The results demonstrate the potential of this approach to significantly reduce the complexity of quantum circuits while maintaining algorithm fidelity.

DOI: 10.5281/zenodo.22480499

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