Unsupervised Machine Learning on a Hybrid Quantum Computer

TL;DR

This paper demonstrates a hybrid quantum-classical approach to unsupervised learning by training a 19-qubit quantum processor to solve a clustering problem using quantum approximate optimization and Bayesian methods.

Contribution

It introduces a method for hybridizing classical and quantum resources to perform unsupervised learning on a quantum processor, showing robustness to noise.

Findings

Successful training of a 19-qubit quantum processor for clustering

Robustness of the hybrid algorithm to realistic noise

Evidence of classical optimization compensating for quantum imperfections

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.