Randomized Benchmarking of Quantum Gates

By Emanuel Knill, Dietrich Leibfried, R. Reichle, J. Britton, R. B. Blakestad, John D. Jost, C. Langer, R. Ozeri, S. Seidelin, David J. Wineland. 2007

1,001 citations recorded by the source corpus.

A key requirement for scalable quantum computing is that elementary quantum gates can be implemented with sufficiently low error. One method for determining the error behavior of a gate implementation is to perform process tomography. However, standard process tomography is limited by errors in state preparation, measurement and one-qubit gates. It suffers from inefficient scaling with number of qubits and does not detect adverse error-compounding when gates are composed in long sequences. An additional problem is due to the fact that desirable error probabilities for scalable quantum computing are of the order of 0.0001 or lower. Experimentally proving such low errors is challenging. We describe a randomized benchmarking method that yields estimates of the computationally relevant errors without relying on accurate state preparation and measurement. Since it involves long sequences of randomly chosen gates, it also verifies that error behavior is stable when used in long computations. We implemented randomized benchmarking on trapped atomic ion qubits, establishing a one-qubit error probability per randomized $\ensuremath{\pi}/2$ pulse of 0.00482(17) in a particular experiment. We expect this error probability to be readily improved with straightforward technical modifications.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/9e5140ce6bbbf9c88906f60f48cfe8b283bffb45

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography (2016, methodology): “Randomized benchmarking (RB) [25, 26], the most commonly used method for qubit characterization, measures a single error rate ( RB) that closely approximates the gates’ average process infidelity.”
Observation of separated dynamics of charge and spin in the Fermi-Hubbard model (2020, background): “However, common calibration tools, such as randomized benchmarking [59, 60], compressed sensing [61, 62], gate set tomography [63, 64], and cross-entropy benchmarking [65] are often too slow to capture drifts and fluctuations in the hardware.”
The randomized measurement toolbox (2022, methodology): “The quintessential method for estimating average error rates in few-qubit quantum systems is called randomized benchmarking (RB) [90, 91].”
Quantum computing with trapped ions (2008, methodology): “The small atomic mass of beryllium allows for high trap frequencies and comparatively large Lamb-Dicke factors.”