articleNature CommunicationsAug 22, 2022GOLD OA

Generalization in quantum machine learning from few training data

MCMatthias C. CaroHHHsin-Yuan HuangMCM. CerezoKSKunal SharmaASAndrew Sornborger

Munich Center for Quantum Science and Technology · Technical University of Munich · +5 more institutions

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Abstract

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as [Formula: see text]. When only K ≪ T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to [Formula: see text]. Our results imply that the compiling of unitaries into a…

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Topics & keywords

Topics
Keywords
  • Generalization
  • Computer science
  • Quantum
  • Convolutional neural network
  • Quantum computer
  • Parameterized complexity
  • Quantum circuit
  • Quantum machine learning
UN Sustainable Development Goals
  • Industry, innovation and infrastructure
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