Resource-Aware Scheduling of Quantum Algorithms using Constraint Programming

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

The advent of quantum computing promises transformative advancements across various scientific and technological domains. However, realizing this potential hinges critically on the efficient execution of quantum algorithms. A significant hurdle lies in the intricate orchestration of quantum resources – qubits, coherence times, and gate fidelity – to meet the demanding requirements of complex quantum algorithms. Traditional scheduling methods often fail to adequately account for these constraints, leading to suboptimal performance and increased execution times. This paper proposes a novel approach to quantum algorithm scheduling by formulating the problem as a Constraint Programming (CP) model. This approach leverages the power of CP solvers to systematically explore the solution space, identifying schedules that maximize resource utilization while adhering to stringent hardware limitations. We introduce a framework for representing quantum algorithms and their associated constraints within a CP environment, enabling automated optimization. The core of the method involves defining constraints related to qubit connectivity, coherence time limitations, and gate fidelity errors. The resulting CP model is then solved using state-of-the-art CP solvers, producing optimized schedules that demonstrate significant improvements in resource efficiency compared to naive scheduling strategies. This work represents a substantial advance in quantum algorithm scheduling, offering a robust and scalable solution for harnessing the full power of quantum hardware.

DOI: 10.5281/zenodo.22269620

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