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Characterizing large-scale quantum computers via cycle benchmarking.

Alexander Erhard1, Joel J Wallman2,3, Lukas Postler1

  • 1Institute for Experimental Physics, University of Innsbruck, 6020, Innsbruck, Austria.

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|November 27, 2019
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Summary
This summary is machine-generated.

Cycle benchmarking offers a scalable method to identify quantum computing errors. This technique confirms error rates do not worsen as quantum processors grow larger.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing Hardware

Background:

  • Quantum computing promises significant speedups for complex problems.
  • Accurate error characterization is crucial for building reliable quantum processors.
  • Existing methods struggle with the vast number of potential errors in large systems.

Purpose of the Study:

  • To develop a scalable protocol for characterizing errors in multi-qubit quantum processors.
  • To address limitations of current techniques in accounting for correlated noise.

Main Methods:

  • Introduced cycle benchmarking, a rigorous protocol for error characterization.
  • Applied the protocol to an ion-trap quantum computer with up to 10 qubits.
  • Quantified local and global errors in both non-entangling and entangling operations.

Main Results:

  • Demonstrated the practicality of cycle benchmarking on a 10-qubit ion-trap system.
  • Measured multi-qubit entangling gate fidelities from [Formula: see text] (2 qubits) to [Formula: see text] (10 qubits).
  • Validated that error rates per gate do not increase with system size.

Conclusions:

  • Cycle benchmarking provides a scalable solution for quantum error characterization.
  • The results indicate that increasing quantum processor size does not inherently raise error rates per gate.
  • This work advances the development of robust, large-scale quantum computers.