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Dihapto-Coordinated Conjugated Carbocycles (η<sup>2</sup>-C<sub><i>n</i></sub>H<sub><i>n</i></sub> <i>n</i> = 5–8): Blurring the Line Between Aromatic and Antiaromatic Hydrocarbons
A two-wave longitudinal mediation study of the relationships between organizational ostracism, future anxiety, and work engagement among teachers
Tetracene Functionalized Si(111) Achieves Enhanced Solar-to-Chemical Energy Conversion via Molecular Acceptor States
Balancing mental health through predictive modeling for healthcare workers during public health crises
Mechanisms and Synthetic Applications of Cyclic, Nonstabilized Isodiazenes: Nitrogen-Atom Insertion into Pyrrolidines and Related Rearrangements
Quantum inspired qubit qutrit neural networks for real time financial forecasting
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.