Abstract: Quantum computers have demonstrated quantum advantages over classical computers in various tasks. However, the current Noisy Intermediate-Scale Quantum (NISQ) quantum era encounters challenges arising from i) Noisy system with high error rates, ii) limited scalability of quantum systems for large algorithms, and iii) lack of access to practical quantum hardware. In this talk,  I will discuss our recent works targeting these challenges that make efforts towards high-fidelity, scalable, and accessible quantum computing systems. Specifically, I will introduce i) a compilation framework that performs quantum gate orchestration to reduce the noise and execution time in photonic quantum computing, ii) a circuit-cutting algorithm to partition a given circuit into smaller sub-circuits with improved fidelity, and iii) a quantum circuit emulation framework that leverages high-performance computing resources to efficiently simulate given quantum circuits.

Bio: Dr. Xulong Tang is an Assistant Professor in the Computer Science Department at the University of Pittsburgh. He received his Ph.D. degree from the Pennsylvania State University in 2019. His current research focuses on i) designing next-generation GPU architectures and systems, ii) "exploring efficient edge computing, and iii) advancing quantum computing systems. His work has been published in top-tier venues including MICRO, HPCA, ISCA, ASPLOS, PLDI, DAC, NeurIPS, ICLR, and ECCV. You can find out more about him at https://xzt102.github.io/.

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This talk is presented by the Department of Computer Science.