Oriel Kiss, Francesco Tacchino, et al.
Machine Learning: Science and Tech.
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.
Oriel Kiss, Francesco Tacchino, et al.
Machine Learning: Science and Tech.
Francesco Tacchino, Alessandro Chiesa, et al.
Journal of Materials Chemistry C
Manuel John, Julian Schuhmacher, et al.
Entropy
Laurin E. Fischer, Daniel Miller, et al.
PRResearch