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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.
Summary
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.
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.
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