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Machine learning for estimation and control of quantum systems.

Hailan Ma1,2, Bo Qi3,4, Ian R Petersen1

  • 1School of Engineering, Australian National University, Canberra, ACT 2601, Australia.

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Summary
This summary is machine-generated.

Machine learning enhances quantum technologies by improving control and estimation of complex quantum systems. This review covers neural networks, gradient methods, evolutionary computation, and reinforcement learning for quantum tasks.

Keywords:
machine learningneural networkquantum controlquantum estimationquantum measurementreinforcement learning

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Area of Science:

  • Quantum Information Science
  • Artificial Intelligence
  • Control Theory

Background:

  • Advancing quantum technologies requires sophisticated control and calibration of complex quantum systems.
  • Machine learning (ML) offers powerful data-driven approaches to address these challenges.
  • Quantum estimation and control are critical for realizing quantum computation, simulation, and sensing.

Purpose of the Study:

  • To review significant machine learning applications in quantum estimation and control.
  • To highlight ML techniques for enhancing the efficiency and robustness of quantum systems.
  • To provide an overview of current research at the intersection of ML and quantum control.

Main Methods:

  • Neural network-based quantum state estimation.
  • Gradient-based quantum optimal control.
  • Evolutionary computation for learning quantum system control.
  • Machine learning for quantum robust control.
  • Reinforcement learning for adaptive quantum control.

Main Results:

  • ML methods demonstrate significant capabilities in learning complex quantum dynamics.
  • Neural networks show promise for accurate quantum state estimation.
  • Gradient and evolutionary methods offer efficient pathways for quantum optimal control.
  • Reinforcement learning enables adaptive control strategies for quantum systems.

Conclusions:

  • Machine learning is a transformative tool for the advancement of quantum technologies.
  • The integration of ML with quantum control and estimation is crucial for future quantum systems.
  • Further research in ML-driven quantum control will accelerate progress in quantum computation, simulation, and sensing.