Abstract
AbstractQuantum error correction of a surface code or repetition code requires the pairwise matching of error events in a space‐time graph of qubit measurements, such that the total weight of the matching is minimized. The input weights follow from a physical model of the error processes that affect the qubits. This approach becomes problematic if the system has sources of error that change over time. Here, it is shown that the weights can be determined from the measured data in the absence of an error model. The resulting adaptive decoder performs well in a time‐dependent environment, provided that the characteristic timescale τenv of the variations is greater than , with the duration of one error‐correction cycle and the typical error probability per qubit in one cycle.
Highlights
To execute algorithms on a quantum computer, one must prevent the accumulation of errors by monitoring and correcting them in control hardware
We demonstrate the method on the repetition code with time-dependent error rates
We summarize the elements of quantum error correction [2, 22] that we need in what follows
Summary
To execute algorithms on a quantum computer, one must prevent the accumulation of errors by monitoring and correcting them in control hardware. For an important class of error correcting codes, the syndrome identifies the end points of an error chain in a space-time graph of ancilla measurements. The weights that govern the optimization problem can be readily obtained if one has a calibrated model of the sources of error in the system [16] Such an error model may not be available, and the error rates may vary in time during the quantum computation. This complication has motivated the search for an adaptive decoder, that would infer the weights from the syndrome without requiring updates of the error model [17,18,19,20,21]. We demonstrate the method on the repetition code with time-dependent error rates
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