| Download | - View final version: QuantumLeap: hybrid quantum neural network for financial predictions (PDF, 9.7 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.eswa.2022.116583 |
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| Author | Search for: Paquet, Eric1ORCID identifier: https://orcid.org/0000-0001-6515-2556; Search for: Soleymani, Farzan1ORCID identifier: https://orcid.org/0000-0001-8668-0710 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | deep quantum neural network; financial prediction; regression; parallel learning; deep neural network; hybrid quantum neural network |
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| Abstract | 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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| Publication date | 2022-01-31 |
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| Publisher | Elsevier |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 3cb4a0ca-ac98-4e9d-a673-b15bdb868395 |
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| Record created | 2022-03-09 |
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| Record modified | 2022-03-09 |
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