QuantumLeap: Hybrid quantum neural network for financial predictions

Paquet, Éric; Soleymani, Farzan

doi:10.1016/j.eswa.2022.116583

Cited by 30 publications

(7 citation statements)

References 22 publications

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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%

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

Katib¹,

Ragab²

2023

Computer Systems Science and Engineering

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The concept of smart healthcare has seen a gradual increase with the expansion of information technology. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things (IoT), cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient. This work designs a new Heap Based Optimization with Deep Quantum Neural Network (HBO-DQNN) model for decision-making in smart healthcare applications. The presented HBO-DQNN model majorly focuses on identifying and classifying healthcare data. In the presented HBO-DQNN model, three stages of operations were performed. Data normalization is applied to pre-process the input data at the initial stage. Next, the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare data. At last, the DQNN model is exploited for healthcare data classification. A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN model. The extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05% and 95.72% under the colon cancer and lymphoma dataset.

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Section: Healthcare Data Classificationmentioning

confidence: 99%

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

Katib¹,

Ragab²

2023

Computer Systems Science and Engineering

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“…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%

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Zhang,

Sjarif,

Ibrahim

2023

WIREs Data Min & Knowl

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Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.This article is categorized under: Technologies > Prediction Technologies > Artificial Intelligence

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“…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%