Irfan Siddiqi | Noise Tailoring for Precision Quantum Benchmarking
▶Title: Noise Tailoring for Precision Quantum Benchmarking
▶Speaker: Irfan Siddiqi (UC Berkeley)
▶Abstract: Contemporary methods for benchmarking noisy quantum processors typically measure average error rates or process infidelities. However, thresholds for fault-tolerant quantum error correction are given in terms of worst-case error rates - defined via the diamond norm - which can differ from average error rates by orders of magnitude. One method for resolving this discrepancy is to randomize the physical implementation of quantum gates, using techniques like randomized compiling (RC). We find that, under RC, gate errors are accurately described by a stochastic Pauli noise model without coherent errors, and that spatially-correlated coherent errors and non-Markovian errors are strongly suppressed. Our results show that randomized benchmarks are a viable route to both verifying that a quantum processor's error rates are below a fault-tolerance threshold, and to bounding the failure rates of near-term algorithms, if - and only if - gates are implemented via randomization methods which tailor noise.
MCQST
▶ Conference: https://www.mcqst.de/conference2023
▶ Website: https://www.mcqst.de
▶ Twitter: / mcqst_cluster
▶ LinkedIn: / mcqst