ESE Colloquium with Jacob Biamonte on Quantum Information

Jacob Biamonte, Jingdong Foundation Endowed Chair in Quantum Information and Computation, Yanqi Lake Beijing Institute of Mathematical Sciences and Applications will be presenting the colloquium "A Brief History of Feedback: Quantum Information Meets Machine Learning"

Quantum theory enables a radical approach to cryptography and predicts new computers which could revolutionize the types of calculations that are even possible. Much current effort is focused on the intriguing possibility to control quantum systems to emulate other quantum systems, to realize advanced quantum algorithms and to bootstrap quantum information for enhanced sensing. Evident challenges arise when reasoning about quantum information systems: how might we understand, engineer and verify systems we can’t fully emulate using classical computers? Moreover, today’s quantum processors can not execute the quantum algorithms and protocols you would find in books — i.e. Shor’s celebrated factoring algorithm which would break RSA cryptography appears out of reach, at least in its known form. A global research effort has discovered a new means to operate quantum processors which requires feedback. This new method merges ideas from machine learning to view a controllable quantum system as a machine learning model. This approach is not yet fully understood. In this talk, I will explain some recent progress to understand this new approach: our discovery of several limiting features of quantum approximate optimization and the existence of avalanche effects in quantum circuit training. I will also explain some more forward looking findings, including demonstrating the problem instance independence of optimized circuit parameters and my proof that the variational model is, in theory, a universal model of quantum computation.


Throughout his career, Dr. Biamonte's research has focused on practically-driven theoretical studies of quantum information processing. He established the first experimentally relevant universal models of adiabatic quantum computation and proved universality of the variational model of quantum computation. He has published several results in the development of quantum machine learning and the applied mathematics of tensor networks. Several of his works consider the impact of noise on quantum dynamics and quantum information processing and sensing with open quantum systems.

Upon completing his undergraduate studies in electrical engineering, he worked as a quantum applications developer at D-Wave Systems Inc. in Vancouver, Canada and as a Fellow at Harvard University in Cambridge Massachusetts in the Aspuru-Guzik group. Thereafter, he obtained a PhD from the University of Oxford in 2010. He then worked as part of a joint Oxford/Singapore postdoctoral program and as a Lecturer in Physics at St Peter's College Oxford before joining the Institute for Scientific Interchange (ISI Foundation) in Torino, Italy as the Quantum Science Research Group Leader (2012-2017). In 2017, Dr. Biamonte joined the MIT founded Skolkovo Institute of Science and Technology as a Tenure Track Associate Professor. He was subsequently promoted to Head of the Laboratory for Quantum Information Processing in 2019 and to tenured Professor in 2022. In April of 2022, Dr. Biamonte became the first American born scientist to have successfully defended a higher Doctorate at the Moscow Institute of Physics and Technology. Dr. Biamonte's time in Moscow was cut abruptly short due to the war.

Dr. Biamonte's current research vision is to develop a data-driven approach to quantify emergent and collective effects in quantum information processing tasks such as computing and sensing. Dr. Biamonte and his collaborators have recently discovered or been the first to analytically predict several features of variational quantum algorithms including abrupt training transitions, reachability deficits and parameter saturations. A passionate educator, Dr. Biamonte teaches applied and engineering mathematics, quantum information theory and quantum computation at the undergraduate and graduate level.