ECSE/CS Joint Seminar: Intelligent Cross-Stack Co-Design of Quantum Computer Systems

Hanrui Wang
Ph.D. Candidate
Massachusetts Institute of Technology
ECSE Topical Seminar
CC 318
Wed, January 31, 2024 at 4:00 PM

Quantum Computing has the potential to solve classically intractable problems with greater speed and efficiency, and recent several years have witnessed exciting advancements in this domain. However, there remains a substantial gap between the algorithmic requirements and the available device in terms of qubit number and system reliability. To close this gap, it is critical to perform the cross-stack co-design of various technology layers, from algorithm and program design, to compilation, and hardware architecture.

In this talk, I will provide an overview of my contributions in the software stack and hardware support for quantum systems. At the algorithm and program level, I will introduce QuantumNAS, a framework for quantum program structure (ansatz) design for variational quantum algorithms. QuantumNAS utilizes the noisy feedback from quantum devices to search for ansatz and qubit mapping tailored for specific hardware, leading to notable resource reduction and reliability enhancements. Then, at the compiler level, I will discuss a compilation framework for the Field-Programmable Qubit Array (FPQA) implemented by the emerging reconfigurable atom arrays. This framework leverages movable atoms for routing 2Q gates, and generates atom movements and gate scheduling with high scalability and parallelism. On the hardware support front, I will present SpAtten, an algorithm-architecture-circuit co-design aimed at Transformer-based quantum error correction decoding. SpAtten supports on-the-flying syndrome pruning to eliminate less critical inputs and boost efficiency. Finally, I will conclude with an overview of my ongoing work and my research vision towards building software and architecture supports for quantum computing, and domain-specific computing for practical quantum advantages.

Hanrui Wang

Hanrui Wang is a Ph.D. Candidate at MIT EECS advised by Prof. Song Han. His research focuses on software stack and hardware support for quantum computer systems, and AI for quantum. His work appears in conferences such as MICRO, HPCA, QCE, DAC, ICCAD, and NeurIPS and has been recognized by QCE 2023 Best Paper Award, ICML RL4RL 2019 Best Paper Award, ACM student research competition 1st Place Award, Best Poster Award at NSF AI Institute, Best Demo Award at DAC university demo, and MLCommons rising star in machine learning and systems. His work is supported by Qualcomm Innovation Fellowship, Baidu Fellowship, and Unitary Fund. He is the creator of TorchQuantum library which has been adopted by IBM Qiskit Ecosystem and PyTorch Ecosystem with 1.1K+ stars on GitHub. He is passionate about teaching and has served as a course developer and co-instructor for a new course on efficient ML and quantum computing at MIT. He is also the co-founder of QuCS “Quantum Computer Systems” forum for quantum education.