Generative Quantum Eigensolver with Alán Aspuru-Guzik

The New Quantum Era - innovation in quantum computing, science and technology

Welcome back to The New Quantum Era, a podcast by Sebastian Hassinger and Kevin Rowney. After a brief hiatus, we’re excited to bring you a fascinating conversation with a true pioneer in the field of quantum computing, Alán Aspuru-Guzik. Alán is a professor at the University of Toronto and a leading figure in quantum computing, known for his foundational work on the Variational Quantum Eigensolver (VQE). In this episode, we delve into the evolution of VQE and explore Alán’s latest groundbreaking work on the Generative Quantum Eigensolver (GQE). Expect to hear about the intersection of quantum computing and machine learning, and how these advancements could shape the future of the field.

Key Highlights:

• Origins of VQE: Alan discusses the development of the Variational Quantum Eigensolver, a technique that combines classical and quantum computing to approximate the ground state of chemical systems. This method was a significant step forward in efforts to make practical use of noisy intermediate-scale quantum (NISQ) devices.
• Challenges and Innovations: The conversation touches on the challenges of variational algorithms, such as the barren plateau problem, and how Alán’s group has been working on innovative solutions to overcome these hurdles.
• Introduction to GQE: Alán introduces the Generative Quantum Eigensolver, a new approach that leverages generative models like transformers to optimize quantum circuits without relying on quantum gradients. This method aims to make quantum computing more efficient and practical.
• Future of Quantum Computing: The discussion explores the potential future workflows in quantum computing, where hybrid architectures combining classical and quantum computing will be essential. Alán shares his vision of how GQE could be foundational in this new era.
• Broader Applications: Beyond chemistry, the GQE technique has potential applications in quantum machine learning and other variational algorithms, making it a versatile tool in the quantum computing toolkit.

Mentioned in this episode:

• A variational eigenvalue solver on a quantum processor: Foundational paper on VQE technique.
• The generative quantum Eigensolver (GQE) and its application for ground state search: Alan’s latest paper on GQE and its applications.
• Tequila Framework: An extensible software framework for VQE experiments.
• The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation: A paper on learning across potential energy surfaces.
• Quantum autoencoders for efficient compression of quantum data: Early work on quantum autoencoders for molecular design.
• Beyond NISQ: The Megaquop Machine: John Preskill’s slides from Q2B SV 2024. I think John is great, but "megaquop" is very "fetch."
• Myths around quantum computation before full fault tolerance: what no-go theorems rule out and what they don't: A paper discussing myths and truths about quantum computing.

Stay tuned for more exciting episodes and deep dives into the world of quantum computing. If you enjoyed this episode, please subscribe, review, and share it on your preferred social media platforms. Thank you for listening!