Measurement-based quantum machine learning

• URL: http://arxiv.org/abs/2405.08319v1
• Date: Tue, 14 May 2024 05:17:01 GMT
• Title: Measurement-based quantum machine learning
• Authors: Luis Mantilla Calderón, Polina Feldmann, Robert Raussendorf, Dmytro Bondarenko,
• Abstract summary: A quantum neural network (QNN) is an object that extends the notion of a classical neural network to quantum models for quantum data. We propose a universal QNN in this framework which we call the multiple-triangle ansatz (MuTA)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A quantum neural network (QNN) is an object that extends the notion of a classical neural network to quantum models for quantum data. We can create a QNN by parametrizing a quantum process and then using it to model unknown relations between quantum states. In this paper, we explore how to use measurement-based quantum computation for quantum machine learning problems and propose a universal QNN in this framework which we call the multiple-triangle ansatz (MuTA). Using the proposed QNN, we solve several tasks, including learning a universal set of gates, optimizing measurement with post-processing, learning a quantum instrument, and the classification of classical data. Finally, we discuss how to train an ansatz under the hardware constraints imposed by photonic Gottesman-Kitaev-Preskill qubits. Our work demonstrates the feasibility of using measurement-based quantum computation as a framework for quantum machine learning algorithms.

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