Quantum inspired qubit qutrit neural networks for real time financial forecasting

Computer Science > Artificial Intelligence [Submitted on 20 Apr 2026] Quantum inspired qubit qutrit neural networks for real time financial forecasting Kanishk Bakshi, Kathiravan Srinivasan 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. Comments: 16 pages, 7 figures. Published in Scientific Reports (2025) Subjects: Artificial Intelligence (cs.AI); Quantum Physics (quant-ph) ACM classes: I.2.6; I.5.1 Cite as: arXiv:2604.18838 [cs.AI]   (or arXiv:2604.18838v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.18838 Focus to learn more Journal reference: Scientific Reports 15, 28711 (2025) Related DOI: https://doi.org/10.1038/s41598-025-09475-0 Focus to learn more Submission history From: Kanishk Bakshi [view email] [v1] Mon, 20 Apr 2026 21:03:33 UTC (3,339 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs quant-ph References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)