SparQSim: Simulating Scalable Quantum Algorithms via Sparse Quantum State Representations

• URL: http://arxiv.org/abs/2503.15118v2
• Date: Thu, 03 Apr 2025 08:28:02 GMT
• Title: SparQSim: Simulating Scalable Quantum Algorithms via Sparse Quantum State Representations
• Authors: Tai-Ping Sun, Zhao-Yun Chen, Yun-Jie Wang, Cheng Xue, Huan-Yu Liu, Xi-Ning Zhuang, Xiao-Fan Xu, Yu-Chun Wu, Guo-Ping Guo,
• Abstract summary: We present SparQSim, a quantum simulator implemented in C++ inspired by the Feynman-based method.<n>SparQSim operates at the register level by storing only the nonzero components of the quantum state.
• Score: 3.7112784544167248
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Efficient simulation of large-scale quantum algorithms is pivotal yet challenging due to the exponential growth of the state space inherent in both Sch\"odinger-based and Feynman-based methods. While Feynman-based simulators can be highly efficient when the quantum state is sparse, these simulators often do not fully support the simulation of large-scale, complex quantum algorithms which rely on QRAM and other oracle-based operations. In this work, we present SparQSim, a quantum simulator implemented in C++ and inspired by the Feynman-based method. SparQSim operates at the register level by storing only the nonzero components of the quantum state, enabling flexible and resource-efficient simulation of basic quantum operations and integrated QRAM for advanced applications such as quantum linear system solvers. In particular, numerical experiments on benchmarks from QASMBench and MQTBench demonstrate that SparQSim outperforms conventional Schr\"odinger-based simulators in both execution time and memory usage for circuits with high sparsity. Moreover, full-process simulations of quantum linear system solvers based on a discrete adiabatic method yield results that are consistent with theoretical predictions. This work establishes SparQSim as a promising platform for the efficient simulation of scalable quantum algorithms.

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