| Téléchargement | - Voir la version finale : QuantumLeap: hybrid quantum neural network for financial predictions (PDF, 9.7 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1016/j.eswa.2022.116583 |
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| Auteur | Rechercher : Paquet, Eric1Identifiant ORCID : https://orcid.org/0000-0001-6515-2556; Rechercher : Soleymani, Farzan1Identifiant ORCID : https://orcid.org/0000-0001-8668-0710 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Sujet | deep quantum neural network; financial prediction; regression; parallel learning; deep neural network; hybrid quantum neural network |
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| Résumé | This paper introduces a new hybrid deep quantum neural network for financial predictions, the QuantumLeap system. This system consists of an encoder that transforms a partitioned financial time series into a sequence of density matrices; a deep quantum network that predicts the density matrix at a later time; and a classical network that measures, from the output density matrix, the maximum price reached by a security at a later time. The deep quantum network is isomorphic to a deep classical network and is computationally tractable. A hybrid deep network is associated with each time stride, allowing for parallelisation of the learning process. The classical network is a learnable measurement apparatus which infers, from the output density matrix, the maximum price reached by a security for a given time. Experimental results associated with 24 securities clearly demonstrate the accuracy and efficiency of the system in both the regression and extrapolation regimes. |
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| Date de publication | 2022-01-31 |
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| Maison d’édition | Elsevier |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 3cb4a0ca-ac98-4e9d-a673-b15bdb868395 |
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| Enregistrement créé | 2022-03-09 |
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| Enregistrement modifié | 2022-03-09 |
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