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Quantum Information Processing
Erschienen in: Quantum Information Processing 4/2021

01.04.2021

An improved hybrid quantum optimization algorithm for solving nonlinear equations

verfasst von: Yumin Dong, Jinlei Zhang

Erschienen in: Quantum Information Processing | Ausgabe 4/2021

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Abstract

This paper proposes a hybrid quantum genetic algorithm (HQGA) and the core is the use of a new quantum revolving gate strategy and population adaptive retention strategy, and with the Quasi-Newton method. HQGA has the characteristics of fast convergence speed, strong global optimization ability and local detailed optimization. Through the typical complex function, tests show that the optimized quality and efficiency of HQGA are better than traditional quantum genetic algorithms. This algorithm has a good effect in training logistic regression (convex problem) in machine learning.
Vorheriger Artikel The verification of a requirement of entanglement measures
Nächster Artikel Finite-key analysis of sending-or-not-sending twin-field quantum key distribution with intensity fluctuations

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Metadaten
Titel
An improved hybrid quantum optimization algorithm for solving nonlinear equations
verfasst von
Yumin Dong
Jinlei Zhang
Publikationsdatum
01.04.2021
Verlag
Springer US
Erschienen in
Quantum Information Processing / Ausgabe 4/2021
Print ISSN: 1570-0755
Elektronische ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-021-03067-3

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