2022
DOI: 10.1016/j.eswa.2022.116583
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QuantumLeap: Hybrid quantum neural network for financial predictions

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Cited by 30 publications
(7 citation statements)
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“…Finally, the DQNN model is exploited for healthcare data classification. The quantum network comprises many quantum perceptrons [25]. Every perceptron parallels the arbitrary unitary operator having n output rubies and m input qubits, resulting in (2 m+n ) 2 − 1 complex parameter.…”
Section: Healthcare Data Classificationmentioning
confidence: 99%
“…Finally, the DQNN model is exploited for healthcare data classification. The quantum network comprises many quantum perceptrons [25]. Every perceptron parallels the arbitrary unitary operator having n output rubies and m input qubits, resulting in (2 m+n ) 2 − 1 complex parameter.…”
Section: Healthcare Data Classificationmentioning
confidence: 99%
“…The unitary operators are updated recursively layer‐by‐layer until the cost function of the QNN reaches its maximum value. This cost function is determined by fidelity, which is a distinctive measure of the proximity between the output of the QNN and the desired output averaged over the training data (Paquet & Soleymani, 2022).…”
Section: Deep Learning Models For Price Forecastingmentioning
confidence: 99%
“…It leverages the principles of quantum mechanics, such as superposition and entanglement, to perform computations that could offer advantages over classical neural networks. Paquet and Soleymani (2022) proposed a DQNN model named QuantumLeap for price forecasting. This forecasting model assumes that several entangled "particles" are arranged in a fixed order and that the state of each particle is in a two-level Hilbert space, with all Hilbert spaces being distinguishable.…”
Section: Generative Adversarial Network (Gans)mentioning
confidence: 99%
“…For example, IBM presents experimental results of quantum risk analysis on a real quantum computer [15]. The authors of [19] propose to use quantum neural networks to process financial time series and make predictions for future stock prices. It is notable that while many quantum machine learning algorithms have the potential to surpass their classical counterparts, many of them require operations on a largescale fault-tolerant quantum computer [20].…”
Section: Quantum Machine Learningmentioning
confidence: 99%