Open Access

ARTICLE

Research on Optimization of Hierarchical Quantum Circuit Scheduling Strategy

Ziao Han, Hui Li^*, Kai Lu, Shujuan Liu, Mingmei Ju

School of Computer and Information Engineering, Harbin University of Commerce, Heilongjiang, 150028, China

* Corresponding Author: Hui Li. Email:

Computers, Materials & Continua 2025, 82(3), 5097-5113. https://doi.org/10.32604/cmc.2025.059577

Received 11 October 2024; Accepted 19 December 2024; Issue published 06 March 2025

Abstract

Traditional quantum circuit scheduling approaches underutilize the inherent parallelism of quantum computation in the Noisy Intermediate-Scale Quantum (NISQ) era, overlook the inter-layer operations can be further parallelized. Based on this, two quantum circuit scheduling optimization approaches are designed and integrated into the quantum circuit compilation process. Firstly, we introduce the Layered Topology Scheduling Approach (LTSA), which employs a greedy algorithm and leverages the principles of topological sorting in graph theory. LTSA allocates quantum gates to a layered structure, maximizing the concurrent execution of quantum gate operations. Secondly, the Layerwise Conflict Resolution Approach (LCRA) is proposed. LCRA focuses on utilizing directly executable quantum gates within layers. Through the insertion of SWAP gates and conflict resolution checks, it minimizes conflicts and enhances parallelism, thereby optimizing the overall computational efficiency. Experimental findings indicate that LTSA and LCRA individually achieve a noteworthy reduction of 51.1% and 53.2%, respectively, in the number of inserted SWAP gates. Additionally, they contribute to a decrease in hardware gate overhead by 14.7% and 15%, respectively. Considering the intricate nature of quantum circuits and the temporal dependencies among different layers, the amalgamation of both approaches leads to a remarkable 51.6% reduction in inserted SWAP gates and a 14.8% decrease in hardware gate overhead. These results underscore the efficacy of the combined LTSA and LCRA in optimizing quantum circuit compilation.

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