Accelerating Simulation of Quantum Circuits under Noise via Computational Reuse
Meng Wang, Swamit Tannu, Prashant J. Nair
备注：
Accepted for publication in the Proceedings of the 52nd Annual International Symposium on Computer Architecture (ISCA '25). Manuscript length: 15 pages
To realize the full potential of quantum computers, we must mitigate qubit errors by developing noise-aware algorithms, compilers, and architectures. Thus, simulating quantum programs on high-performance computing (HPC) systems with different noise models is a de facto tool researchers use. Unfortunately, noisy simulators iteratively execute a similar circuit for thousands of trials, thereby incurring significant performance overheads.
To address this, we propose a noisy simulation technique called Tree-Based Quantum Circuit Simulation (TQSim). TQSim exploits the reusability of intermediate results during the noisy simulation, reducing computation. TQSim dynamically partitions a circuit into several subcircuits. It then reuses the intermediate results from these subcircuits during computation. Compared to a noisy Qulacs-based baseline simulator, TQSim achieves a speedup of up to 3.89x for noisy simulations. TQSim is designed to be efficient with multi-node setups while also maintaining tight fidelity bounds.
Qubit Reuse Beyond Reorder and Reset: Optimizing Quantum Circuits by Fully Utilizing the Potential of Dynamic Circuits
Technical University of Munich（慕尼黑工业大学）；Munich Quantum Software Company GmbH（慕尼黑量子软件有限公司）；Software Competence Center Hagenberg GmbH (SCCH)（哈根贝格软件能力中心有限公司）
Institute for Quantum Computing, Baidu Research（百度研究院量子计算研究所）；School of Mathematics and Statistics and Hubei Key Laboratory of Computational Science, Wuhan University（武汉大学数学与统计学院）
Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization
Graduate School of Science and Technology, Keio University（Keio大学科学技术研究生院）；Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电气工程研究生院）；RIKEN Center for Computational Science（理化学研究所计算科学中心）
MERA: A Mid-Circuit-Measurement Error-Aware Compilation Framework for Quantum Circuits
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Central Florida（中佛罗里达大学）；State Key Laboratory of Quantum Information Technologies and Materials (CUHK)（量子信息技术与材料国家重点实验室）
Demonstrating advantages of dynamic quantum circuits on a hybrid superconducting qubit-cavity processor
Southern University of Science and Technology（南方科技大学）；International Quantum Academy（国际量子研究院）；Shenzhen Branch, Hefei National Laboratory（合肥国家实验室深圳分部）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
Department of Geophysics, Colorado School of Mines（地质学系，科罗拉多矿业学院）；Department of Energy Science and Engineering, Stanford University（能源科学与工程系，斯坦福大学）；Department of Petroleum Engineering, Colorado School of Mines（石油工程系，科罗拉多矿业学院）
Accelerating State-Vector Quantum Simulation on Integrated GPUs via Cache Locality Optimization: A Cross-Architecture Evaluation
Instituto de Pesquisas Eldorado（Eldorado研究所）；Universidade Federal de Santa Catarina（圣卡塔琳娜联邦大学）；Pontifícia Universidade Católica do Rio Grande do Sul（南里奥格兰德天主教 Pontifícia 大学）
