How to Use Machine Learning to Build a Quantum ComputerExport this event to calendar

Friday, November 24, 2017 — 2:30 PM EST

Phys10 Undergraduate Seminar Series

Roger Melko
Roger MelkoAssociate Professor & Canada Research Chair in Computational Many-Body Physics

Department of Physics and Astronomy, University of Waterloo

The power of quantum computers comes from the collective behavior of infinitely complex assemblies of interacting qubits. This complexity is reminiscent of the “curse of dimensionality” commonly encountered in machine learning.  Despite this curse, the machine learning community has developed techniques with remarkable abilities to interpret complex sets of real-world data, such as images or natural languages, on today's conventional computers. Here, I show that modern neural network architectures for machine learning can be used to identify simple (and not so simple) patterns of correlations in arrays of interacting bits and qubits.  From this, I show how stochastic neural networks can be used to to efficiently represent quantum wavefunctions on a classical computer.  This ability enables a host of machine learning applications in error correction and quantum state tomography, which will be used for designing near-term quantum computers and other devices.

>> learn more about Dr. Melko's research

Location 
AL - Arts Lecture Hall
116
200 University Avenue West

Waterloo, ON N2L 3G1
Canada
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