Data Science Talk Series 

Towards Efficient Large-scale Quantum Circuit Simulation and Beyond (In-person)

Abstract: Large-scale quantum circuit simulation is essential for developing and validating quantum algorithms and compiler/runtime strategies before deploying them on real devices, but it becomes extremely challenging as qubit count grows. Full state-vector methods require storing complex amplitudes and therefore incur an exponential memory footprint that quickly forces distribution across multiple nodes. Once distributed, performance is often dominated not by arithmetic but by data movement and by communication complexity, since many gates become “non-local” under a partitioned layout and require expensive data exchanges and synchronization across nodes. In this talk, I will talk about two work that aims to optimize the large-scale quantum circuit simulation performance and memory efficiency. HiSVSIM addresses scalability issue by compiling the circuit into a DAG, partitioning it into acyclic subcircuits, and simulating hierarchically to improve locality and reduce costly full-state passes and communication pressure. BMQSim tackles the memory-capacity bottleneck by applying high-fidelity lossy compression with point-wise error control and two-level memory management, while using circuit partitioning and pipelined compression/data movement to reduce overhead and keep simulation fast even under tight memory constraints.  

Bio of the speaker: Dr. Bo Fang is an assistant professor in the Department of Computer Science and Engineering at the University of Texas at Arlington. Prior to UTA, he works for Pacific Northwest National Laboratory as a computer scientist.  His research interests include fault-tolerant systems, GPUs and HPC computing in general, and in recent years he is mainly working on quantum noise characterization and mitigation and quantum circuit simulation. He is the recipient of the William C. Carter PhD Dissertation Award in Dependability 2020, the honorable mention of the 2020 SIGHPC Doctoral Dissertation Award, and the Exceptional Contribution Award of the HPC group at PNNL in 2022. He received the best paper award at IEEE Cluster 2022 and the best paper runner-up at ACM ICS 2025.  He received his Ph.D. in the Electrical and Computer Engineering Department at the University of British Columbia, advised by Prof. Karthik Pattabiraman and Prof. Matei Ripeanu.