Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices

TL;DR

This paper introduces a hybrid quantum-classical deep learning framework called quantum-assisted Helmholtz machine, designed to handle high-dimensional real-world datasets using small quantum computers, demonstrated on a quantum annealer with MNIST data.

Contribution

The work presents a novel hybrid quantum-classical framework that combines deep learning and quantum hardware to model complex datasets, expanding the application scope of near-term quantum devices.

Findings

Successfully modeled MNIST subset with 1644 qubits on D-Wave 2000Q

Demonstrated hybrid approach's potential for high-dimensional data processing

Framework adaptable to various quantum hardware platforms

Abstract

Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the \textit{quantum-assisted Helmholtz machine:} a hybrid quantum-classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16x16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.