HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction

• URL: http://arxiv.org/abs/2503.15403v1
• Date: Wed, 19 Mar 2025 16:44:21 GMT
• Title: HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
• Authors: Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, Rajeev Singh,
• Abstract summary: This study explores the potential of hybrid quantum-classical approaches to assist in financial trend prediction.<n>A custom Quantum Neural Network (QNN) regressor is introduced, designed with a novel ansatz tailored for financial applications.
• Score: 3.5418331252013897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid quantum-classical approaches to assist in financial trend prediction by leveraging quantum resources for improved feature representation and learning. A custom Quantum Neural Network (QNN) regressor is introduced, designed with a novel ansatz tailored for financial applications. Two hybrid optimization strategies are proposed: (1) a sequential approach where classical recurrent models (RNN/LSTM) extract temporal dependencies before quantum processing, and (2) a joint learning framework that optimizes classical and quantum parameters simultaneously. Systematic evaluation using TimeSeriesSplit, k-fold cross-validation, and predictive error analysis highlights the ability of these hybrid models to integrate quantum computing into financial forecasting workflows. The findings demonstrate how quantum-assisted learning can contribute to financial modeling, offering insights into the practical role of quantum resources in time-series analysis.

Related papers

• Hybrid Quantum Neural Networks with Variational Quantum Regressor for Enhancing QSPR Modeling of CO2-Capturing Amine [0.9968037829925945]
  We develop hybrid quantum neural networks (HQNN) to improve structure-property relationship modeling for CO2-capturing amines.<n>HQNNs improve predictive accuracy for key solvent properties, including basicity, viscosity, boiling point, melting point, and vapor pressure.
  arXiv  Detail & Related papers  (2025-03-01T07:26:45Z)
• Contextual Quantum Neural Networks for Stock Price Prediction [0.0]
  We apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network.<n>This architecture represents the first of its kind in quantum finance, offering superior predictive power and computational efficiency.<n>Our findings highlight the transformative potential of QML in financial applications, paving the way for more advanced, resource-efficient quantum algorithms.
  arXiv  Detail & Related papers  (2025-02-26T22:39:23Z)
• Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting [0.24739484546803336]
  We present the Quantum Kernel-Based Long short-memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures.<n>QK-LSTM captures intricate nonlinear dependencies and temporal dynamics with fewer trainable parameters.
  arXiv  Detail & Related papers  (2024-12-12T01:16:52Z)
• Exploring Hybrid Quantum-Classical Methods for Practical Time-Series Forecasting [0.0]
  Time-series forecasting is essential for strategic planning and resource allocation.<n>We explore two quantum-based approaches for time-series forecasting.<n>We compare the results of these two methods to evaluate their effectiveness and potential advantages for practical forecasting applications.
  arXiv  Detail & Related papers  (2024-12-07T11:14:17Z)
• Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz [0.8192907805418583]
  Quantum-Train (QT) is a hybrid quantum-classical machine learning framework. It maps quantum state measurements to classical neural network weights. Traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. We introduce a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes.
  arXiv  Detail & Related papers  (2024-09-11T03:51:34Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy [67.02951777522547]
  We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs) We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution. Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
  arXiv  Detail & Related papers  (2022-05-10T08:05:59Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• The dilemma of quantum neural networks [63.82713636522488]
  We show that quantum neural networks (QNNs) fail to provide any benefit over classical learning models. QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. These results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.
  arXiv  Detail & Related papers  (2021-06-09T10:41:47Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.