Please join us for the next institute seminar on Friday, January 10 at 10:30am in DC 1302.
Title: Designing a quantum computer with generative models
Abstract: The power of quantum computers comes from the collective behavior of infinitely complex assemblies of interacting qubits. While giving quantum computers their power, manipulating this complexity also presents significant challenges for scientists and engineers building the current generation of quantum devices. Recently, it was demonstrated that industry-standard generative models can be adapted to learn powerful representations of quantum computers, when trained on data produced by measuring qubit states. In this talk, I will explain how such machine learning techniques can be used to efficiently represent quantum wavefunctions on a conventional computer, and to generate measurement probes inaccessible to conventional laboratory techniques. I demonstrate the power of this approach with real experimental qubit data obtained from a quantum computer composed of individual atoms, cooled to a fraction of a degree above absolute zero. Such generative modelling techniques, together with state-of-the art representations, will open the door for the machine learning assisted design of quantum computers well into the future.
Speaker Bio:
Dr. Melko's research interests involve strongly-correlated many-body systems, with a focus on emergent phenomena, ground state phases, phase transitions, quantum criticality, and entanglement. He emphasizes computational methods as a theoretical technique, in particular the development of state-of-the-art algorithms for the study of strongly-interacting systems. Dr. Melko's work has employed Monte Carlo simulations and Density Matrix Renormalization Group methods to explore the low-temperature physics of classical and quantum magnetic materials, cold atoms in optical lattices, bosonic fluids and low-dimensional systems. He is particularly involved in studying microscopic models that display interesting quantum behavior in the bulk, such as superconducting, spin liquid, topological, superfluid or supersolid phases. He is also interested in broader ideas in computational physics, the development of efficient algorithms for simulating quantum mechanical systems on classical computers, and the relationship of these methods to the field of quantum information science.
Date and Time:
Friday, January 10, 2020
10:30 AM - 11:30 AM
Location: DC 1302
Light refreshments will be available.