Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

TLDR

Quantum computing is approaching commercialization and quantum supremacy, positioning machine learning as a leading candidate for quantum‑enhanced applications, yet a gap remains between current proposals, practitioner needs, and near‑term device capabilities. This work identifies intractable machine‑learning tasks—particularly generative, unsupervised, and semi‑supervised problems and classical datasets with quantum‑like correlations—that could benefit from near‑term quantum devices. The authors propose a hybrid quantum–classical framework, exemplified by the quantum‑assisted Helmholtz machine, which uses deep learning to compress data into a low‑dimensional binary form for training on small quantum processors, and demonstrate the concept on a quantum annealer while noting applicability to other platforms.

Abstract

With quantum computing technologies nearing the era of commercialization and quantum supremacy, machine learning (ML) appears as one of the promising 'killer' applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices to demonstrate quantum enhancement in the near future. In this contribution to the focus collection 'What would you do with 1000 qubits?', we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques. We also highlight the case of classical datasets with potential quantum-like statistical correlations where quantum models could be more suitable. We focus on hybrid quantum–classical approaches and illustrate some of the key challenges we foresee for near-term implementations. Finally, we introduce the quantum-assisted Helmholtz machine (QAHM), an attempt to use near-term quantum devices to tackle high-dimensional datasets of continuous variables. Instead of using quantum computers to assist deep learning, as previous approaches do, the QAHM uses deep learning to extract a low-dimensional binary representation of data, suitable for relatively small quantum processors which can assist the training of an unsupervised generative model. Although we illustrate this concept on a quantum annealer, other quantum platforms could benefit as well from this hybrid quantum–classical framework.