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Optimal quantum control using randomized benchmarking.

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We developed a new quantum control optimization method using randomized benchmarking to quickly find errors. This technique enhances qubit gate performance and is ideal for automated quantum system optimization.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Experimental Quantum Physics

Background:

  • Quantum control is crucial for reliable quantum computations.
  • Errors in quantum gates limit the performance of quantum systems.
  • Optimizing control parameters is essential for achieving high-fidelity quantum operations.

Purpose of the Study:

  • To present a novel method for optimizing quantum control in experimental systems.
  • To rapidly infer and correct errors affecting quantum gates.
  • To improve the efficiency and accuracy of quantum gate operations.

Main Methods:

  • Utilizing a subset of randomized benchmarking measurements for rapid error inference.
  • Applying the method to optimize single- and two-qubit gates in superconducting qubits.
  • Implementing automated and closed-loop optimization strategies.

Main Results:

  • Demonstrated improvement in single- and two-qubit gate fidelity.
  • Successfully minimized gate bleedthrough, an error where one gate affects subsequent operations.
  • Identified and addressed control crosstalk in superconducting qubit systems.
  • Achieved parameter correction where control errors no longer dominate system performance.

Conclusions:

  • The presented method offers a significant advancement in optimizing quantum control.
  • It enables higher fidelity quantum gates and reduces unwanted error mechanisms.
  • The approach is suitable for real-time, automated optimization of experimental quantum systems.