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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantum machine learning beyond kernel methods.

Sofiene Jerbi1, Lukas J Fiderer2, Hendrik Poulsen Nautrup2

  • 1Institute for Theoretical Physics, University of Innsbruck, Technikerstr. 21a, A-6020, Innsbruck, Austria. sofiene.jerbi@uibk.ac.at.

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This summary is machine-generated.

This study introduces a framework for quantum machine learning models, unifying various approaches. It reveals that linear quantum models require more qubits than data re-uploading models for certain tasks, offering insights for noisy quantum computing.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Quantum Information Theory

Background:

  • Parametrized quantum circuits are key for near-term quantum machine learning.
  • Current understanding of quantum machine learning model comparisons is limited.
  • Various quantum machine learning models exist, but their relationships are unclear.

Purpose of the Study:

  • To establish a unifying framework for quantum machine learning models.
  • To compare resource requirements of different quantum machine learning models.
  • To provide insights into model compatibility with Noisy Intermediate-Scale Quantum (NISQ) constraints.

Main Methods:

  • Identification of a constructive framework: linear quantum models.
  • Application of quantum information theory tools.
  • Analysis of qubit number and data requirements for learning tasks.

Main Results:

  • All standard parametrized quantum circuit models fit within the linear quantum model framework.
  • Data re-uploading circuits can be efficiently mapped to linear models.
  • Linear quantum models require exponentially more qubits than data re-uploading models for specific learning tasks.

Conclusions:

  • A comprehensive view of quantum machine learning models is provided.
  • Insights into resource efficiency for NISQ devices are offered.
  • The study clarifies relationships between different quantum machine learning approaches.