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Physics-Informed Neural Networks for Quantum Control.

Ariel Norambuena1, Marios Mattheakis2, Francisco J González3

  • 1Centro de Optica e Información Cuántica, Universidad Mayor, Camino la Piramide 5750, Huechuraba, Santiago, Chile.

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Researchers developed a new artificial intelligence method using physics-informed neural networks (PINNs) for optimal quantum control. This approach efficiently solves complex quantum system problems with high accuracy and low energy demands.

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Area of Science:

  • Quantum physics
  • Artificial intelligence
  • Computational methods

Background:

  • Quantum control is crucial for understanding quantum systems.
  • Traditional optimization methods are being adapted into AI algorithms.
  • Open quantum systems present unique control challenges.

Purpose of the Study:

  • To introduce a novel computational method for optimal quantum control problems.
  • To apply this method to open quantum systems, specifically state-to-state transfer.
  • To demonstrate the advantages of the new method over standard techniques.

Main Methods:

  • Utilizing physics-informed neural networks (PINNs) for optimal quantum control.
  • Applying the PINNs methodology to solve state-to-state transfer in open quantum systems.
  • Testing the flexibility of PINNs with varying physical parameters and initial conditions.

Main Results:

  • Achieved efficient state-to-state transfer in open quantum systems.
  • Demonstrated high probability of transfer, short evolution times, and low-energy controls.
  • Showcased the adaptability of PINNs to changing physical conditions.

Conclusions:

  • Physics-informed neural networks offer a powerful and flexible computational tool for optimal quantum control.
  • PINNs provide advantages over standard control techniques for open quantum systems.
  • This method facilitates deeper exploration of quantum system dynamics and applications.