一句话：提出 GPU 加速的批量量子线路模拟器 BQSim，利用决策图优化并转为 GPU 友好结构，实现显著加速。
量子线路模拟（QCS）帮助研究者在无真实量子计算机时理解量子操作，但现有方法大多一次只能模拟一个输入；测试与验证等应用需要批量模拟不同输入，即批量量子线路模拟（BQCS）。BQSim 受决策图（DD）紧凑表示量子门矩阵的启发，用 DD 优化量子线路以减少 BQCS 计算，并把 DD 转换为 GPU 友好的数据结构；同时采用基于任务图的执行策略，降低重复内核调用开销，并重叠内核执行与数据传输。与 cuQuantum、Qiskit Aer、FlatDD 相比，BQSim 平均分别加速 3.25×、159.06×、311.42×。
Quantum circuit simulation (QCS) plays an important role in the designs and analysis of a quantum algorithm, as it assists researchers in understanding how quantum operations work without accessing expensive quantum computers. Despite many QCS methods, they are largely limited to simulating one input at a time. However, many simulation-driven quantum computing applications, such as testing and verification, require simulating multiple inputs to reason a quantum algorithm under different scenarios. We refer to this type of QCS as batch quantum circuit simulation (BQCS). In this paper, we present BQSim, a GPU-accelerated batch quantum circuit simulator. BQSim is inspired by the state-of-the-art decision diagram (DD) that can compactly represent quantum gate matrices, but overcomes its limitation of CPU-centric simulation. Specifically, BQSim uses DD to optimize a quantum circuit for reduced BQCS computation and converts DD to a GPU-efficient data structure. Additionally, BQSim employs a task graph-based execution strategy to minimize repetitive kernel call overhead and efficiently overlap kernel execution with data movement. Compared with three state-of-the-art quantum circuit simulators, cuQuantum, Qiskit Aer, and FlatDD, BQSim is 3.25×, 159.06×, and 311.42× faster on average.
@inproceedings{JiangCCHH25,
title = {BQSim: GPU-accelerated Batch Quantum Circuit Simulation using Decision Diagram},
author = {Shui Jiang and Yi-Hua Chung and Chih-Chun Chang and Tsung-Yi Ho and Tsung-Wei Huang},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3676641.3715984}
}