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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20141118T163000Z
DTEND:20141118T171500Z
LOCATION:New Orleans Theater
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Over the past 30 years, several computational paradigms have been developed based on the premise that the laws of quantum mechanics could provide radically new and more powerful methods of information processing. One of these approaches is to encode the solution of a computational problem into the ground state of a programmable many-body quantum Hamiltonian system. Although, there is empirical evidence for quantum enhancement in certain problem instances, there is not a full theoretical understanding of the conditions for quantum speed up for problems of practical interest, especially hard combinatorial optimization and inference tasks in machine learning. In this talk, I will provide an overview of quantum computing paradigms and discuss the progress at the Google Quantum Artificial Intelligence Lab towards developing the general theory and overcoming practical limitations. Furthermore, I will discuss two algorithms that we have recently developed known as Quantum Principal Component Analysis and Quantum Boltzmann Machine.
SUMMARY:Quantum Computing Paradigms for Probabilistic Inference and Optimization
PRIORITY:3
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