Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{184355,
        author = {Madhupada pavani and Dr. K. Venkata Ramana},
        title = {Enhancing Psychological Health Prediction via Federated Learning with Quantum Boltzmann Machines},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {1117-1124},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184355},
        abstract = {Psychological health disorders have surfaced as a critical public health challenge, with early discovery playing a vital part in perfecting patient issues. still, the sensitive nature of internal health data raises serious sequestration enterprises, frequently confining centralized data storehouse and analysis. This design proposes a new sequestration- conserving frame that integrates Federated Learning (FL) with a Quantum Boltzmann Machine (QBM) for internal health vaticination. The approach enables distributed model training across multiple simulated guests without participating raw data, icing data confidentiality while maintaining high prophetic performance. The QBM element enhances point birth through amount inspired probabilistic modelling, enabling the prisoner of complex, high dimensional correlations in internal health datasets. A cold-blooded amount classical armature is employed, where the QBM serves as the point representation subcaste and a classical neural network performs bracket. Experimental evaluation using real- world datasets containing PHQ- 9, anxiety, and stress assessment records demonstrates an delicacy of roughly 99% accuracy, surpassing traditional centralized literacy models. The results punctuate the eventuality of amount- enhanced allied literacy for secure, scalable, and accurate internal health diagnostics, paving the way for integration with unborn amount tackle. Keywords Federated Learning, Quantum Boltzmann Machine, Mental Health Prediction, sequestration Preservation, Quantum Machine Learning, PHQ- 9, Distributed Training, Hybrid Quantum- Classical Models.},
        keywords = {Federated Learning, Quantum Boltzmann Machine, Psychological Health Prediction, Privacy Preservation, Quantum Machine Learning, PHQ-9, Hybrid Quantum-Classical Models.},
        month = {September},
        }