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1. (WO2018106556) TOMOGRAPHY AND GENERATIVE DATA MODELING VIA QUANTUM BOLTZMANN TRAINING

Pub. No.:    WO/2018/106556    International Application No.:    PCT/US2017/064394
Publication Date: Fri Jun 15 01:59:59 CEST 2018 International Filing Date: Tue Dec 05 00:59:59 CET 2017
IPC: G06N 99/00
Applicants: MICROSOFT TECHNOLOGY LICENSING, LLC
Inventors: WIEBE, Nathan O.
KIEFEROVA, Maria
Title: TOMOGRAPHY AND GENERATIVE DATA MODELING VIA QUANTUM BOLTZMANN TRAINING
Abstract:
Quantum neural nets, which utilize quantum effects to model complex data sets, represent a major focus of quantum machine learning and quantum computing in general. In this application, example methods of training a quantum Boltzmann machine are described. Also, examples for using quantum Boltzmann machines to enable a form of quantum state tomography that provides both a description and a generative model for the input quantum state are described. Classical Boltzmann machines are incapable of this. Finally, small non-stoquastic quantum Boltzmann machines are compared to traditional Boltzmann machines for generative tasks, and evidence presented that quantum models outperform their classical counterparts for classical data sets.