In this episode, host Sebastian Hassinger sits down with Xiaodi Wu, Associate Professor at the University of Maryland, to discuss Wu’s journey through quantum information science, his drive for bridging computer science and physics, and the creation of the quantum programming language SimuQ.
Guest Introduction
- Xiaodi Wu shares his academic path from Tsinghua University (where he studied mathematics and physics) to a PhD at the University of Michigan, followed by postdoctoral work at MIT and a position at the University of Oregon, before joining the University of Maryland.
- The conversation highlights Wu’s formative experiences, early fascination with quantum complexity, and the impact of mentors like Andy Yao.
Quantum Computing: Theory Meets Practice
- Wu discusses his desire to blend theoretical computer science with physics, leading to pioneering work in quantum complexity theory and device-independent quantum cryptography.
- He reflects on the challenges and benefits of interdisciplinary research, and the importance of historical context in guiding modern quantum technology development.
Programming Languages and Human Factors
- The episode delves into Wu’s transition from theory to practical tools, emphasizing the major role of human factors and software correctness in building reliable quantum software.
- Wu identifies the value of drawing inspiration from classical programming languages like FORTRAN and SIMULA—and points out that quantum software must prioritize usability and debugging, not just elegant algorithms.
SimiQ: Hamiltonian-Based Quantum Abstraction
- Wu introduces SimuQ, a new quantum programming language designed to treat Hamiltonian evolution as a first-class abstraction, akin to how floating-point arithmetic is fundamental in classical computing.
- SimiQ enables users to specify Hamiltonian models directly and compiles them to both gate-based and analog/pulse-level quantum devices (including IBM, AWS Braket, and D-Wave backends).
- The language aims to make quantum simulation and continuous-variable problems more accessible, and serves as a test bed for new quantum software abstractions.
Analog vs. Digital in Quantum Computing
- Wu and Hassinger explore the analog/digital divide in quantum hardware, examining how SimuQ leverages the strengths of both by focusing on higher-level abstractions (Hamiltonians) that fit natural use cases like quantum simulation and dynamic systems.
Practical Applications and Vision
- The conversation highlights targeted domains for SimuQ, such as quantum chemistry, physics simulation, and machine learning algorithms that benefit from continuous-variable modeling.
- Wu discusses his vision for developer-friendly quantum tools, drawing parallels to the evolution of classical programming and the value of reusable abstractions for future advancements.
Listen to The New Quantum Era podcast for more interviews with leaders in quantum computing, software development, and scientific research.