Algorithmic Error Mitigation Scheme for Current Quantum Processors

Abstract

We present a hardware agnostic error mitigation algorithm for near term quantum processors inspired by the classical Lanczos method. This technique can reduce the impact of different sources of noise at the sole cost of an increase in the number of measurements to be performed on the target quantum circuit, without additional experimental overhead. We demonstrate through numerical simulations and experiments on IBM Quantum hardware that the proposed scheme significantly increases the accuracy of cost functions evaluations within the framework of variational quantum algorithms, thus leading to improved ground-state calculations for quantum chemistry and physics problems beyond state-of-the-art results.

References in corpus (4)

• Hybrid quantum-classical algorithms and quantum error mitigation
• Mitigating measurement errors in multi-qubit experiments
• Non-unitary operations for ground-state calculations in near term quantum computers
• Quantum computed moments correction to variational estimates

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