John Watrous
Director of Education and Training Institute for Quantum Computing, University of Waterloo
What I've learned from quantum education
John Watrous is an educator and researcher specializing in quantum information and computation. He is the author of the book The Theory of Quantum Information and the creator of the course Understanding Quantum Information and Computation. His research interests include quantum complexity theory and quantum information theory.
This will be a personal perspective talk on quantum education centered around John Watrous’s experiences as an educator over the past three decades. He will share stories and observations spanning the development of courses, writing lecture notes and books, and an exploration into digital education. Along the way, he will share lessons he has learned through teaching quantum information and computation, and reflect on the role of education in building our field.
Sergio Boixo
Director of Quantum Computing, Google Quantum AI
Quantumly Verifiable Quantum Advantage via Out-of-Time-Order Correlators
Sergio Boixo leads the Quantum Computer Science group at Google Quantum AI. He is an APS Fellow, and was a research professor at USC, and a postdoc at Harvard and Caltech. Sergio has a doctorate in physics from UNM, and a master's degree from UAB.
We present a new candidate for practical quantum advantage based on estimating the out-of-time-order correlator (OTOC), which frames the demonstration as a quantumly verifiable expectation value problem. We identify a "Goldilocks zone" where the observable retains measurable inverse-polynomial fluctuations, yet remains classically intractable due to "large-loop" constructive interference. We report on the experimental implementation of this protocol on a superconducting processor, and argue that the required computation exceeds the capacity of current tensor network and heuristic classical simulations. Finally, we show that this computational hardness translates to practical utility by applying the protocol to a Hamiltonian learning instance, successfully determining the geometry of a molecule in an NMR experiment.