Unsupervised Machine Learning on a Hybrid Quantum Computer
Abstract
Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.
Cite
@article{arxiv.1712.05771,
title = {Unsupervised Machine Learning on a Hybrid Quantum Computer},
author = {J. S. Otterbach and R. Manenti and N. Alidoust and A. Bestwick and M. Block and B. Bloom and S. Caldwell and N. Didier and E. Schuyler Fried and S. Hong and P. Karalekas and C. B. Osborn and A. Papageorge and E. C. Peterson and G. Prawiroatmodjo and N. Rubin and Colm A. Ryan and D. Scarabelli and M. Scheer and E. A. Sete and P. Sivarajah and Robert S. Smith and A. Staley and N. Tezak and W. J. Zeng and A. Hudson and Blake R. Johnson and M. Reagor and M. P. da Silva and C. Rigetti},
journal= {arXiv preprint arXiv:1712.05771},
year = {2017}
}
Comments
7 pages + appendix, many figures