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Area of Science:

  • Quantum computing
  • Quantum information science
  • Computational complexity

Background:

  • Scalable quantum computer development requires identifying noise sources causing quantum evolution errors.
  • Hardware-specific noise and decoherence complicate error detection in diverse quantum implementations.

Purpose of the Study:

  • To develop a randomized benchmarking algorithm for efficient identification and learning of mixed error models in quantum computations.
  • To provide an efficiently computable estimate of overhead for noisy quantum circuit output analysis.
  • To establish analytic noise bounds for efficient classical simulability.

Main Methods:

  • Development of a randomized benchmarking algorithm utilizing Weyl unitaries.
  • Efficient computation of overhead estimates based on interaction locality.
  • Derivation of analytic noise bounds.

Main Results:

  • The algorithm efficiently identifies and learns mixtures of error models.
  • Overhead for analyzing noisy circuit outputs decreases with increasing noise rate.
  • Analytic noise bounds are derived, indicating regimes for efficient classical simulation.

Conclusions:

  • The developed methods enable efficient classical simulation of quantum computations under certain noise conditions.
  • Application to variational quantum eigensolver ansatz circuits establishes bounds on classical simulation complexity as a function of noise.