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Experimental semi-autonomous eigensolver using reinforcement learning.

C-Y Pan1, M Hao1, N Barraza1

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Researchers developed a quantum algorithm using reinforcement learning to find eigenvectors of Hermitian operators. This method achieves high fidelity with fewer measurements, advancing quantum computing and AI.

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Area of Science:

  • Quantum Computing
  • Quantum Mechanics
  • Artificial Intelligence

Background:

  • Characterizing observables via Hermitian operators is essential in quantum mechanics.
  • Eigensolvers are fundamental algorithms for quantum technologies.
  • Current quantum devices require efficient algorithms for practical applications.

Purpose of the Study:

  • To implement a semi-autonomous algorithm for approximating eigenvectors of Hermitian operators.
  • To utilize single-shot measurements and a feedback loop for reduced resource demand.
  • To explore the application of reinforcement learning in quantum eigensolving.

Main Methods:

  • Implementation of a semi-autonomous eigensolver algorithm on an IBM quantum computer.
  • Utilizing single-shot measurements and a classical feedback loop for pseudo-random adjustments.
  • Framing the algorithm within the reinforcement learning paradigm.

Main Results:

  • Achieved fidelities over 0.97 for single-qubit observable eigenvectors using ~200 measurements.
  • Obtained fidelities over 0.91 for two-qubit observable eigenvectors using ~1500 measurements.
  • Demonstrated low resource demand suitable for current quantum computing devices.

Conclusions:

  • The developed algorithm efficiently approximates eigenvectors with high fidelity.
  • This approach reduces the number of measurements required, making it practical for current quantum hardware.
  • The work contributes to quantum devices capable of decision-making with partial information, supporting quantum artificial intelligence.