Suppressing quantum errors by scaling a surface code logical qubit

By R. Acharya, I. Aleiner, R. Allen, T. Andersen, M. Ansmann, F. Arute, K. Arya, A. Asfaw, J. Atalaya, Ryan Babbush and 148 more. Nature, 2022

1,354 citations recorded by the source corpus.

Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction^ 1 , 2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10^−6 logical error per cycle floor set by a single high-energy event (1.6 × 10^−7 excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation. A study demonstrating increasing error suppression with larger surface code logical qubits, implemented on a superconducting quantum processor.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fa08c638941ee4f8aa4da8c33bb9720eb3d5aef3

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Citations in the corpus

Quantum error correction below the surface code threshold (2024, neutral): “[29], it was shown that data qubit leakage removal is instrumental to achieving the exponential suppression of logical errors with code distance when physical errors are well below the error threshold.”
Midcircuit Operations Using the omg Architecture in Neutral Atom Arrays (2023, background): “Within the superconducting qubits and trapped-ion architectures, high-fidelity midcircuit measurement has facilitated state tele-portation, quantum error correction, and the generation of topologically ordered entangled states [17 – 22].”
High-threshold and low-overhead fault-tolerant quantum memory (2023, background): “Small examples of the surface code with a single logical qubit have already been demonstrated experimentally by several groups 24–28 .”
A Race-Track Trapped-Ion Quantum Processor (2023, background): “[97, 98], which use a fixed architecture and parallel syndrome measurements, making for a direct comparison of different distances.”
High-fidelity parallel entangling gates on a neutral-atom quantum computer (2023, background): “This protocol has the advantage of populating atomic dark states which do not contain population in the intermediate state used for the two-photon excitation to the Rydberg state, thereby reducing spontaneous emission.”
Sparse Blossom: correcting a million errors per core second with minimum-weight matching (2023, neutral): “7 with that of PyMatching v2 on experimental data, by running both decoders on the full dataset of Google’s recent experiment demonstrating the suppression of quantum errors by scaling a surface code logical qubit from distance 3 to distance 5 [AI23; Tea22].”
Logical quantum processor based on reconfigurable atom arrays (2023, methodology): “The AOD tweezers are mobile but have several constraints 7,69 .”