Efficient noise mitigation technique for quantum computing

Quantum computers have enabled solving problems beyond the current machines' capabilities. However, this requires handling noise arising from unwanted interactions in these systems. Several protocols have been proposed to address efficient and accurate quantum noise profiling and mitigation. In this work, we propose a novel protocol that efficiently estimates the average output of a noisy quantum device to be used for quantum noise mitigation. The multi-qubit system average behavior is approximated as a special form of a Pauli Channel where Clifford gates are used to estimate the average output for circuits of different depths. The characterized Pauli channel error rates, and state preparation and measurement errors are then used to construct the outputs for different depths thereby eliminating the need for large simulations and enabling efficient mitigation. We demonstrate the efficiency of the proposed protocol on four IBM Q 5-qubit quantum devices. Our method demonstrates improved accuracy with efficient noise characterization. We report up to 88% and 69% improvement for the proposed approach compared to the unmitigated, and pure measurement error mitigation approaches, respectively.

Automated summary

Quantum computers have the ability to solve problems that are beyond the capabilities of current machines, but handling noise is necessary. To address this, several protocols for efficient and accurate quantum noise profiling and mitigation have been proposed. In this study, a novel protocol is proposed to estimate the average output of a noisy quantum device efficiently, which can be used for quantum noise mitigation. The protocol approximates the average behavior of a multi-qubit system as a specific form of a Pauli Channel and uses Clifford gates to estimate the average output for circuits of different depths. The characterized Pauli channel error rates, as well as state preparation and measurement errors, are then used to construct the outputs for different depths, eliminating the need for large simulations and enabling efficient mitigation. The proposed protocol demonstrates improved accuracy with efficient noise characterization, showing up to 88% and 69% improvement compared to unmitigated and pure measurement error mitigation approaches, respectively.