Year: 2025
Type: article
Abstract: Gate set tomography (GST) allows for a self-consistent characterization of noisy quantum information processors (QIPs). The standard device-agnostic approach treats the QIPs as black boxes that are only constrained by the laws of physics, attaining full generality at aconsiderable resource cost: numerous circuits built from the gate set must be run in order to amplify each of the gate set parameters. In this work, we show that a microscopic parametrization of quantum gates under time-correlated noise on the driving phase, motivated by recent experiments with trapped-ion gates, reduces the required resources enabling a more efficient version of GST. By making use of the formalism of filter functions over the noise spectral densities, we discuss the minimal parametrizations of the gate set that include the effect of finite correlation times and non-Markovian quantum evolutions during the individual gates. We compare the estimated gate sets obtained by our method and the standard long-sequence GST, discussing their accuracies in terms of established metrics, as well as showcasing the advantages of the parametrized approach in terms of the sampling complexity for specific examples.
Source: npj Quantum Information
Authors: P. Viñas, A. Bermudez
Institutions: Instituto de Física Teórica, Universidad Autónoma de Madrid
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,290
Awards: Modular Industrial Large-scaLE quaNtum computing with trapped IONs, phase 1, G1105844351, G1175115578, G1432373144, G1562697177, G1678613413, G2029354939, G210041235, G2262748287, G2601397997, G282847690, G300979063, G3165068977, G34346518, G3480869486, G3681454997, G4042783231, G425668148, G4442677895, G4526231891, G4768733587, G480488906, G491712853, G5080475149, G5195188938, G5404395142, G5635532810, G5662822759, G624877701, G7084143925, G7452299184, G7805626310, G7863371703, G8118709007, G8497050386
OpenAlex ID: W4407202964