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Quantum Machine Learning over Infinite Dimensions.

Hoi-Kwan Lau1, Raphael Pooser2,3, George Siopsis3

  • 1Institute of Theoretical Physics, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.

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Summary
This summary is machine-generated.

This study extends quantum machine learning to infinite-dimensional systems using photonic quantum computers. This generalization promises exponential speedups over classical algorithms for complex computational tasks.

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Area of Science:

  • Quantum Information Science
  • Computer Science
  • Machine Learning

Background:

  • Quantum machine learning (QML) is an emerging field merging quantum computation with machine learning.
  • Current QML proposals predominantly use finite-dimensional, discrete-variable systems.
  • There is a need to explore more complex and practical quantum substrates for QML.

Purpose of the Study:

  • To generalize quantum machine learning to infinite-dimensional systems.
  • To present critical subroutines for continuous-variable QML on photonic quantum computers.
  • To outline a potential experimental implementation for future demonstrations.

Main Methods:

  • Generalization of QML algorithms to infinite-dimensional Hilbert spaces.
  • Development of critical subroutines for continuous-variable (CV) quantum computation.
  • Utilizing an all-photonic quantum computing architecture.

Main Results:

  • Demonstrated the feasibility of QML in infinite-dimensional systems.
  • Identified subroutines enabling exponential speedups compared to classical algorithms.
  • Proposed a blueprint for experimental realization on photonic platforms.

Conclusions:

  • Infinite-dimensional quantum machine learning is achievable and practical.
  • Continuous-variable photonic quantum computers can accelerate QML tasks.
  • This work provides a foundation for advanced photonic QML implementations.