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Active learning machine learns to create new quantum experiments.

Alexey A Melnikov1, Hendrik Poulsen Nautrup2, Mario Krenn3,4

  • 1Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria; anton.zeilinger@univie.ac.at alexey.melnikov@uibk.ac.at.

Proceedings of the National Academy of Sciences of the United States of America
|January 20, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning can aid quantum research by designing experiments to create entangled states. AI autonomously discovered new techniques, showing potential for creative roles in scientific discovery.

Keywords:
artificial intelligencemachine learningquantum entanglementquantum experimentsquantum machine learning

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Area of Science:

  • Quantum physics
  • Artificial intelligence
  • Quantum optics

Background:

  • The potential of intelligent machines in scientific research, particularly in quantum laboratories, remains largely unexplored.
  • A key challenge in quantum experiments is achieving various entanglement classes, crucial for quantum technologies.

Purpose of the Study:

  • To investigate the utility of machine learning (ML) in designing quantum experiments.
  • To explore the capability of an AI system in generating high-dimensional entangled multiphoton states.

Main Methods:

  • Utilized the projective simulation model, a physics-oriented artificial intelligence approach.
  • Challenged the AI system to autonomously design complex photonic quantum experiments.

Main Results:

  • The AI system successfully learned to create diverse entangled states and enhanced their experimental realization efficiency.
  • The system autonomously rediscovered experimental techniques that are emerging as standard in quantum optics.

Conclusions:

  • Machine learning demonstrates significant potential for creative contributions in scientific research.
  • AI systems can play a more active and innovative role in advancing quantum experimentation and discovery.