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Shadows of quantum machine learning.

Sofiene Jerbi1,2, Casper Gyurik3, Simon C Marshall3

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Summary
This summary is machine-generated.

Quantum machine learning models can now be deployed classically after training on quantum computers. This approach enables a quantum learning advantage for broader practical applications.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Computational Complexity

Background:

  • Quantum machine learning (QML) offers computational advantages but requires quantum hardware for evaluation.
  • Evaluating trained QML models on new data necessitates access to quantum computers, limiting practical use.

Purpose of the Study:

  • To introduce a novel class of QML models trainable with quantum resources but deployable classically.
  • To enable practical QML applications by decoupling training from evaluation.

Main Methods:

  • Developed a training methodology resulting in a 'shadow model' for classical deployment.
  • Proved universality for classically-deployed QML.
  • Analyzed learning capacities and compared them to fully quantum and classical models.

Main Results:

  • The proposed models are universal for classically-deployed QML.
  • These models exhibit restricted learning capacities compared to fully quantum models.
  • A provable learning advantage over classical learners is achieved under standard complexity assumptions.

Conclusions:

  • Quantum machine learning can offer advantages even when quantum computers are used solely for training.
  • Classical deployment of QML models broadens their applicability in real-world scenarios.
  • This research facilitates the integration of QML into various practical contexts by overcoming hardware limitations for evaluation.