Supervised Learning with Quantum Measurements

• URL: http://arxiv.org/abs/2004.01227v2
• Date: Fri, 12 Feb 2021 15:09:28 GMT
• Title: Supervised Learning with Quantum Measurements
• Authors: Fabio A. Gonz\'alez, Vladimir Vargas-Calder\'on, Herbert Vinck-Posada
• Abstract summary: This paper reports a novel method for supervised machine learning based on the mathematical formalism that supports quantum mechanics. The method uses projective quantum measurement as a way of building a prediction function. One remarkable characteristic of the method is that it does not require learning any parameters through optimization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper reports a novel method for supervised machine learning based on the mathematical formalism that supports quantum mechanics. The method uses projective quantum measurement as a way of building a prediction function. Specifically, the relationship between input and output variables is represented as the state of a bipartite quantum system. The state is estimated from training samples through an averaging process that produces a density matrix. Prediction of the label for a new sample is made by performing a projective measurement on the bipartite system with an operator, prepared from the new input sample, and applying a partial trace to obtain the state of the subsystem representing the output. The method can be seen as a generalization of Bayesian inference classification and as a type of kernel-based learning method. One remarkable characteristic of the method is that it does not require learning any parameters through optimization. We illustrate the method with different 2-D classification benchmark problems and different quantum information encodings.

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