Measurement-based quantum machine learning

TL;DR

This paper introduces MuTA, a measurement-based quantum neural network framework that demonstrates versatility in learning, classification, and quantum state processing, with potential advantages for MBQC hardware.

Contribution

It proposes MuTA, a universal MBQC-based quantum neural network architecture with features like bias engineering and scalable training, filling a gap in MBQC QML research.

Findings

MuTA can learn universal quantum gates under noise.

MuTA successfully classifies quantum states and classical data.

Incorporates photonic GKP qubit hardware constraints.

Abstract

Quantum machine learning (QML) leverages quantum computing for classical inference, furnishes the processing of quantum data with machine-learning methods, and provides quantum algorithms adapted to noisy devices. Typically, QML proposals are framed in terms of the circuit model of quantum computation. The alternative measurement-based quantum computing (MBQC) paradigm can exhibit lower circuit depths, is naturally compatible with classical co-processing of mid-circuit measurements, and offers a promising avenue towards error correction. Despite significant progress on MBQC devices, QML in terms of MBQC has been hardly explored. We propose the multiple-triangle ansatz (MuTA), a universal quantum neural network assembled from MBQC neurons featuring bias engineering, monotonic expressivity, tunable entanglement, and scalable training. We numerically demonstrate that MuTA can learn a universal set of gates in the presence of noise, a quantum-state classifier, as well as a quantum instrument, and classify classical data using a quantum kernel tailored to MuTA. Finally, we incorporate hardware constraints imposed by photonic Gottesman-Kitaev-Preskill qubits. Our framework lays the foundation for versatile quantum neural networks native to MBQC, allowing to explore MBQC-specific algorithmic advantages and QML on MBQC devices.