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We developed a new method to precisely characterize analog quantum simulators using superconducting qubits. This technique accurately estimates Hamiltonian parameters and identifies errors, crucial for advancing quantum computing.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Superconducting Qubit Systems

Background:

  • Accurate characterization of analog quantum simulators is essential for achieving beyond-classical computational capabilities.
  • Superconducting qubits are a leading platform for building quantum simulators, but precise Hamiltonian parameter estimation remains challenging.

Purpose of the Study:

  • To develop a scalable and robust Hamiltonian learning algorithm for superconducting-qubit analog quantum simulators.
  • To precisely estimate free Hamiltonian parameters from time-series data, even in the presence of state-preparation and measurement (SPAM) errors.

Main Methods:

  • A scalable Hamiltonian learning algorithm robust against SPAM errors.
  • A novel super-resolution technique, tensorESPRIT, for frequency extraction from matrix time-series.
  • Constrained manifold optimization for parameter estimation.

Main Results:

  • Precise estimation of Hamiltonian parameters for up to 14 superconducting qubits with sub-MHz accuracy.
  • Tomographic information about SPAM errors was obtained.
  • A spatial implementation error map for a 27-qubit grid was constructed.

Conclusions:

  • The developed toolkit enables accurate characterization of analog quantum processors.
  • This work advances the understanding, calibration, and improvement of quantum simulators.
  • The findings are critical for the development of quantum simulators capable of complex computations.