Southwest Quantum Information and Technology

Unifying state-of-the-art quantum error mitigation techniques

Presenting Author: Piotr Czarnik, Los Alamos National Laboratory
Contributing Author(s): Angus Lowe, Daniel Bultrini, Max Hunter Gordon, Andrew Arrasmith, Lukasz Cincio, Patrick Coles

Achieving near-term quantum advantage will require effective methods for mitigating hardware noise. Many state-of-the-art error mitigation are data-driven, employing classical data obtained from runs of different quantum circuits.  For example, Zero-noise extrapolation (ZNE) uses variable noise data, Clifford-data regression (CDR) uses data from near-Clifford circuits and Virtual Distillation (VD) utilizes data produced from different numbers of state preparations. First, we propose a novel, scalable error mitigation method that conceptually unifies ZNE and CDR. Our approach, called variable-noise Clifford data regression (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  and show that it significantly outperforms these individual methods. Next, we generalize vnCDR unifying CDR, ZNE and VD under a general data-driven error mitigation framework that we call UNIfied Technique for Error mitigation with Data (UNITED). We find that for sufficiently large shot resources UNITED outperforms the individual methods and vnCDR. Specifically, we employ a realistic noise model obtained from a trapped ion quantum computer to benchmark UNITED and show that for our largest considered shot budget (10^{10}), UNITED gives the most accurate correction.

Read this article online: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.033098, https://arxiv.org/abs/2107.13470

(Session 5 : Thursday from 12:00pm-2:00 pm)