Title
Error estimation in current noisy quantum computersxmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
https://ror.org/02e24yw40https://ror.org/000xsnr85
Version
http://purl.org/coar/version/c_970fb48d4fbd8a85
Rights
© 2024 The AuthorsAccess
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.1007/s11128-024-04384-zPublished at
Quantum Information Processing Publisher
Springer NatureKeywords
NISQquantum computing
error mitigation
quantum circuit
Abstract
One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyse the main sources of errors in cur ... [+]
One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyse the main sources of errors in current (IBM) quantum computers and we present a useful tool (TED-qc) designed to facilitate the total error probability expected for any quantum circuit. We propose this total error probability as the best way to estimate a lower bound for the fidelity in the NISQ era, avoiding the necessity of comparing the quantum calculations with any classical one. In order to contrast the robustness of our tool we compute the total error probability that may occur in three different quantum models: 1) the Ising model, 2) the Quantum-Phase Estimation (QPE), and 3) the Grover’s algorithm. For each model, the main quantities of interest are computed and benchmarked against the reference simulator’s results as a function of the error probability for a representative and statistically significant sample size. The analysis is satisfactory in more than the of the cases. In addition, we study how error mitigation techniques are able to eliminate the noise induced during the measurement. These results have been calculated for the IBM quantum computers, but both the tool and the analysis can be easily extended to any other quantum computer. [-]
xmlui.dri2xhtml.METS-1.0.item-oaire-funderName
Diputación Foral de Gipuzkoaxmlui.dri2xhtml.METS-1.0.item-oaire-fundingStream
Sin informaciónxmlui.dri2xhtml.METS-1.0.item-oaire-awardNumber
2023-QUAN-000019-01 QIAxmlui.dri2xhtml.METS-1.0.item-oaire-awardURI
Sin informaciónxmlui.dri2xhtml.METS-1.0.item-oaire-awardTitle
Konputazio kuantikoa adimen artifizialeko algoritmoetan: konputazio klasikotik Quantum Machine Learning-eraCollections
- Articles - Engineering [684]
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