Measurement-based quantum machine learning

• URL: http://arxiv.org/abs/2405.08319v2
• Date: Tue, 30 Sep 2025 04:17:39 GMT
• Title: Measurement-based quantum machine learning
• Authors: Luis Mantilla Calderón, Robert Raussendorf, Polina Feldmann, Dmytro Bondarenko,
• Abstract summary: We propose a universal quantum neural network assembled from measurement-based quantum computing neurons.<n>We numerically demonstrate that MuTA can learn a universal set of gates in the presence of noise.<n>We incorporate hardware constraints imposed by photonic Gottesman-Kitaev-Preskill qubits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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