Quantum Neural Networks Achieving Quantum Algorithms

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Résumé

This paper explores the possibility to construct quantum algorithms by means of neural networks endowed with quantum gates evolved to achieve prescribed goals. First tentatives are performed on the well known Deutsch and Deutsch-Jozsa problems. Results are promising as solutions are detected for different sizes and initializations of the problems using a standard evolutionary learning process. This approach is then used to design quantum operators by combining simple quantum operators belonging to a predefined set.

langue originaleAnglais
titreArtificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers
rédacteurs en chefMarcello Pelillo, Irene Poli, Debora Slanzi, Roberto Serra, Marco Villani, Andrea Roli
EditeurSpringer
Pages3-15
Nombre de pages13
Volume830
ISBN (Electronique)978-3-319-78658-2
ISBN (imprimé)978-3-319-78657-5
Les DOIs
étatPublié - 2 mai 2018

Série de publications

NomCommunications in Computer and Information Science
Volume830
ISSN (imprimé)1865-0929

Empreinte digitale

operators
learning

Citer ceci

Nicolay, D., & Carletti, T. (2018). Quantum Neural Networks Achieving Quantum Algorithms. Dans M. Pelillo, I. Poli, D. Slanzi, R. Serra, M. Villani, & A. Roli (eds.), Artificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers (Vol 830, p. 3-15). (Communications in Computer and Information Science; Vol 830). Springer. https://doi.org/doi.org/10.1007/978-3-319-78658-2_1
Nicolay, Delphine ; Carletti, Timoteo. / Quantum Neural Networks Achieving Quantum Algorithms. Artificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers. Editeur / Marcello Pelillo ; Irene Poli ; Debora Slanzi ; Roberto Serra ; Marco Villani ; Andrea Roli. Vol 830 Springer, 2018. p. 3-15 (Communications in Computer and Information Science).
@inproceedings{35abeaf25eb34230848fea13a26a808a,
title = "Quantum Neural Networks Achieving Quantum Algorithms",
abstract = "This paper explores the possibility to construct quantum algorithms by means of neural networks endowed with quantum gates evolved to achieve prescribed goals. First tentatives are performed on the well known Deutsch and Deutsch-Jozsa problems. Results are promising as solutions are detected for different sizes and initializations of the problems using a standard evolutionary learning process. This approach is then used to design quantum operators by combining simple quantum operators belonging to a predefined set.",
keywords = "Quantum Neural Networks, Quantum Algorithms, Genetic Algorithm, heuristic method",
author = "Delphine Nicolay and Timoteo Carletti",
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Nicolay, D & Carletti, T 2018, Quantum Neural Networks Achieving Quantum Algorithms. Dans M Pelillo, I Poli, D Slanzi, R Serra, M Villani & A Roli (eds), Artificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers. VOL. 830, Communications in Computer and Information Science, VOL. 830, Springer, p. 3-15. https://doi.org/doi.org/10.1007/978-3-319-78658-2_1

Quantum Neural Networks Achieving Quantum Algorithms. / Nicolay, Delphine; Carletti, Timoteo.

Artificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers. Ed. / Marcello Pelillo; Irene Poli; Debora Slanzi; Roberto Serra; Marco Villani; Andrea Roli. Vol 830 Springer, 2018. p. 3-15 (Communications in Computer and Information Science; Vol 830).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

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AB - This paper explores the possibility to construct quantum algorithms by means of neural networks endowed with quantum gates evolved to achieve prescribed goals. First tentatives are performed on the well known Deutsch and Deutsch-Jozsa problems. Results are promising as solutions are detected for different sizes and initializations of the problems using a standard evolutionary learning process. This approach is then used to design quantum operators by combining simple quantum operators belonging to a predefined set.

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Nicolay D, Carletti T. Quantum Neural Networks Achieving Quantum Algorithms. Dans Pelillo M, Poli I, Slanzi D, Serra R, Villani M, Roli A, rédacteurs en chef, Artificial Life and Evolutionary Computation - 12th Italian Workshop, WIVACE 2017, Revised Selected Papers. Vol 830. Springer. 2018. p. 3-15. (Communications in Computer and Information Science). https://doi.org/doi.org/10.1007/978-3-319-78658-2_1