Averaged circuit eigenvalue sampling

Abstract

We introduce ACES, a method for scalable noise metrology of quantum circuits that stands for Averaged Circuit Eigenvalue Sampling. It simultaneously estimates the individual error rates of all the gates in collections of quantum circuits, and can even account for space and time correlations between these gates. ACES strictly generalizes randomized benchmarking (RB), interleaved RB, simultaneous RB, and several other related techniques. However, ACES provides much more information and provably works under strictly weaker assumptions than these techniques. Finally, ACES is extremely scalable: we demonstrate with numerical simulations that it simultaneously and precisely estimates all the Pauli error rates on every gate and measurement in a 100 qubit quantum device using fewer than 20 relatively shallow Clifford circuits and an experimentally feasible number of samples. By learning the detailed gate errors for large quantum devices, ACES opens new possibilities for error mitigation, bespoke quantum error correcting codes and decoders, customized compilers, and more.

arXiv comment6 pages, 3 figures. Source code available at http://github.com/sflammia/ACES

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Cited by in corpus (10)

• The Future of Quantum Computing with Superconducting Qubits
• Classical Shadows With Noise
• Shallow shadows: Expectation estimation using low-depth random Clifford circuits
• Efficiently improving the performance of noisy quantum computers
• Tight bounds on Pauli channel learning without entanglement
• Scalable noise characterization of syndrome-extraction circuits with averaged circuit eigenvalue sampling
• Techniques for learning sparse Pauli-Lindblad noise models
• Exploring Quantum Average-Case Distances: proofs, properties, and examples
• Operational Markovianization in Randomized Benchmarking
• Qubit assignment using time reversal