Scalable noise characterisation of syndrome extraction circuits with averaged circuit eigenvalue sampling

Characterising the performance of noisy quantum circuits is central to the production of prototype quantum computers and can enable improved quantum error correction that exploits noise biases identified in a quantum device. We describe an implementation of averaged circuit eigenvalue sampling (ACES), a general framework for the scalable noise characterisation of quantum circuits. ACES is capable of simultaneously estimating the Pauli error probabilities of all gates in a Clifford circuit, and captures averaged spatial correlations between gates implemented simultaneously in the circuit. By rigorously analysing the performance of ACES experiments, we derive a figure of merit for their expected performance, allowing us to optimise ACES experimental designs and improve the precision to which we estimate noise given fixed experimental resources. Since the syndrome extraction circuits of quantum error correcting codes are representative components of a fault-tolerant architecture, we demonstrate the scalability and performance of our ACES protocol through circuit-level numerical simulations of the entire noise characterisation procedure for the syndrome extraction circuit of a distance 25 surface code with over 1000 qubits. Our results indicate that detailed noise characterisation methods are scalable to near-term quantum devices. We release our code in the form of the Julia package AveragedCircuitEigenvalueSampling.jl.

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