Low-Level and NUMA-Aware Optimization for High-Performance Quantum Simulation

• URL: http://arxiv.org/abs/2506.09198v2
• Date: Thu, 06 Nov 2025 13:38:24 GMT
• Title: Low-Level and NUMA-Aware Optimization for High-Performance Quantum Simulation
• Authors: Ali Rezaei, Luc Jaulmes, Maria Bahna, Oliver Thomson Brown, Antonio Barbalace,
• Abstract summary: This work focuses on performance enhancements through targeted low-level and NUMA-aware tuning on a single-node system.<n>We introduce an open-source, high-performance extension to the QuEST state vector simulator that integrates state-of-the-art low-level and NUMA-aware optimizations for modern processors.
• Score: 0.3280871442296501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scalable classical simulation of quantum circuits is crucial for advancing quantum algorithm development and validating emerging hardware. This work focuses on performance enhancements through targeted low-level and NUMA-aware tuning on a single-node system, thereby not only advancing the efficiency of classical quantum simulations but also establishing a foundation for scalable, heterogeneous implementations that bridge toward noiseless quantum computing. Although few prior studies have reported similar hardware-level optimizations, such implementations have not been released as open-source software, limiting independent validation and further development. We introduce an open-source, high-performance extension to the QuEST state vector simulator that integrates state-of-the-art low-level and NUMA-aware optimizations for modern processors. Our approach emphasizes locality-aware computation and incorporates hardware-specific techniques including NUMA-aware memory allocation, thread pinning, AVX-512 vectorization, aggressive loop unrolling, and explicit memory prefetching. Experiments demonstrate substantial speedups--5.5-6.5x for single-qubit gate operations, 4.5x for two-qubit gates, 4x for Random Quantum Circuits (RQC), and 1.8x for the Quantum Fourier Transform (QFT). Algorithmic workloads further achieve 4.3-4.6x acceleration for Grover and 2.5x for Shor-like circuits. These results show that systematic, architecture-aware tuning can significantly extend the practical simulation capacity of classical quantum simulators on current hardware.

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