Quantum inspired qubit qutrit neural networks for real time financial forecasting

• URL: http://arxiv.org/abs/2604.18838v1
• Date: Mon, 20 Apr 2026 21:03:33 GMT
• Title: Quantum inspired qubit qutrit neural networks for real time financial forecasting
• Abstract summary: The study highlights significant differences in training times and performance metrics across models.<n>The Quantum Qutrit-based Neural Network consistently outperforms with advantages in risk-adjusted returns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By outlining methodologies, architectures, and training procedures, the study highlights significant differences in training times and performance metrics across models. While all models demonstrate robust accuracies above 70%, the Quantum Qutrit-based Neural Network consistently outperforms with advantages in risk-adjusted returns, measured by the Sharpe ratio, greater consistency in prediction quality through the Information Coefficient, and enhanced robustness under varying market conditions. The QQTN not only surpasses its classical and qubit-based counterparts in multiple quantitative and qualitative metrics but also achieves comparable performance with significantly reduced training times. These results showcase the promising prospects of Quantum Qutrit-based Neural Networks in practical financial applications, where real-time processing is critical. By achieving superior accuracy, efficiency, and adaptability, the proposed models underscore the transformative potential of quantum-inspired approaches, paving the way for their integration into computationally intensive fields.

Related papers

• A Comprehensive Analysis of Accuracy and Robustness in Quantum Neural Networks [0.0]
  Quantum Machine Learning (QML) has recently emerged as a highly promising research frontier.<n>This study focuses on the critical dimensions of generalization, accuracy, and robustness.
  arXiv  Detail & Related papers  (2026-04-28T20:53:23Z)
• Quantum-Enhanced Neural Contextual Bandit Algorithms [50.880384999888044]
  This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm.<n>QNTK-UCB is a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T09:58:14Z)
• Parameter efficient hybrid spiking-quantum convolutional neural network with surrogate gradient and quantum data-reupload [0.20999222360659606]
  Spiking Quantum Neural Network (SQNN) combines principles from spiking neural networks (SNNs) and quantum computing.<n>SQDR-CNN enables joint training of convolutional SNNs and quantum circuits within a single backpropagation framework.<n>We achieve 86% of the mean top-performing accuracy of the SOTA SNN baselines, yet uses only 0.5% of the smallest spiking model's parameters.
  arXiv  Detail & Related papers  (2025-12-03T15:43:33Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks [76.53016529061821]
  Liquid Quantum Neural Network (LQNet) and Continuous Time Recurrent Quantum Neural Network (CTRQNet) developed.<n>LQNet and CTRQNet achieve accuracy increases as high as 40% on CIFAR 10 through binary classification.
  arXiv  Detail & Related papers  (2024-08-28T00:56:03Z)
• Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification [10.069224006497162]
  We introduce a novel architecture termed Parallel Proportional Fusion of Quantum and Spiking Neural Networks (PPF-QSNN) The proposed PPF-QSNN outperforms both the existing spiking neural network and the serial quantum neural network across metrics such as accuracy, loss, and robustness. This study lays the groundwork for the advancement and application of quantum advantage in artificial intelligent computations.
  arXiv  Detail & Related papers  (2024-04-01T10:35:35Z)
• The cross-sectional stock return predictions via quantum neural network and tensor network [0.0]
  We investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. We evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm.
  arXiv  Detail & Related papers  (2023-04-25T00:05:13Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• 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 neural networks with deep residual learning [29.929891641757273]
  In this paper, a novel quantum neural network with deep residual learning (ResQNN) is proposed. Our ResQNN is able to learn an unknown unitary and get remarkable performance.
  arXiv  Detail & Related papers  (2020-12-14T18:11:07Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)

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.