Toward Practical Quantum Computing Systems with Intelligent Cross-Stack Co-Design

Abstract:

Quantum Computing (QC) has the potential to solve classically hard problems with greater speed and efficiency, and we have witnessed exciting advancements in QC in recent years. However, there remain substantial gaps between the application requirements and the available devices in terms of reliability, software framework support, and efficiency. To close the gaps and fully unleash quantum power, it is critical to perform AI-enhanced co-design across various technology stacks, from algorithm and program design, to compilation, and hardware architecture.

In this talk, I will provide an overview of my contributions to the architecture and system-level supports for quantum computing. At the algorithm and program level, I will introduce QuantumNAS, a framework for quantum program structure (ansatz) design for variational quantum algorithms. QuantumNAS adopts an intelligent search engine and utilizes the noisy feedback from quantum devices to search for program structure and qubit mapping tailored for specific hardware, leading to notable resource reduction and reliability enhancements. Then, at the compilation and control level, I will discuss Q-Pilot, 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 architecture and design automation front, I will present SpAtten, an algorithm-architecture-circuit co-design aimed at Transformer-based quantum error correction decoding. SpAtten supports on-the-fly 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 toward building software and hardware supports for practical quantum advantages.

Bio:

Hanrui Wang is a Ph.D. Candidate at MIT EECS, advised by Prof. Song Han. His research focuses on architecture and system-level supports for quantum computing, and AI for quantum. His work appears in conferences such as MICRO, HPCA, QCE, DAC, ICCAD, and NeurIPS and has been recognized by the 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, MLCommons Rising Star in ML and Systems, and ISSCC 2024 Rising Star. His work is supported by the Qualcomm Innovation Fellowship, Baidu Fellowship, and Unitary Fund. He is the creator of the TorchQuantum library, which has been adopted by the 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 the QuCS “Quantum Computer Systems” forum for quantum education.