Izenburua
Error estimation in current noisy quantum computersBeste instituzio
Donostia International Physics Center (DIPC)Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU)
Bertsioa
Bertsio argitaratua
Eskubideak
© 2024 The AuthorsSarbidea
Sarbide irekiaArgitaratzailearen bertsioa
https://doi.org/10.1007/s11128-024-04384-zNon argitaratua
Quantum Information Processing Argitaratzailea
Springer NatureGako-hitzak
NISQquantum computing
error mitigation
quantum circuit
Laburpena
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. [-]
Finantzatzailea
Diputación Foral de GipuzkoaPrograma
Sin informaciónZenbakia
2023-QUAN-000019-01 QIALaguntzaren URIa
Sin informaciónProiektua
Konputazio kuantikoa adimen artifizialeko algoritmoetan: konputazio klasikotik Quantum Machine Learning-eraBildumak
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