Unified approach to data-driven quantum error mitigation

Achieving near-term quantum advantage will require effective methods for mitigating hardware noise. Data-driven approaches to error mitigation are promising, with popular examples including zero-noise extrapolation (ZNE) and Clifford data regression (CDR). Here, we propose a scalable error mitigation method that conceptually unifies ZNE and CDR. Our approach, called variable-noise Clifford data regression (vnCDR), significantly outperforms these individual methods in numerical benchmarks. vnCDR generates training data first via near-Clifford circuits (which are classically simulable) and second by varying the noise levels in these circuits. We employ a noise model obtained from IBM's Ourense quantum computer to benchmark our method. For the problem of estimating the energy of an 8-qubit Ising model system, vnCDR improves the absolute energy error by a factor of 33 over the unmitigated results and by factors of 20 and 1.8 over ZNE and CDR, respectively. For the problem of correcting observables from random quantum circuits with 64 qubits, vnCDR improves the error by factors of 2.7 and 1.5 over ZNE and CDR, respectively.

Automated summary

The proposed method, variable-noise Clifford data regression (vnCDR), outperforms popular error mitigation methods ZNE and CDR in numerical benchmarks by first generating training data using near-Clifford circuits and varying noise levels, and then applying a noise model obtained from IBM's Ourense quantum computer. In the problem of estimating energy of an 8-qubit Ising model system, vnCDR improves absolute energy error by a factor of 33 compared to unmitigated results, and by factors of 20 and 1.8 compared to ZNE and CDR, respectively. In correcting observables from random quantum circuits with 64 qubits, vnCDR improves error by factors of 2.7 and 1.5 compared to ZNE and CDR, respectively.