International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 711KB)
A Hybrid Quantum-Classical Transformer Framework for Robust Depression Detection on Social Media: Enhancing Sensitivity via Variational Quantum Circuits
PDF (711KB), PP.203-214
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Author(s)
Index Terms
Quantum NLP, Hybrid Neural Networks, Depression Detection, Variational Quantum Circuits, PennyLane, DistilBERT, Mental Health AI
Abstract
This study proposes a hybrid quantum-classical framework for depression detection from social media text, integrating a frozen DistilBERT encoder with a variational quantum circuit (VQC)-based classification layer. The motivation stems from challenges in clinical NLP, including overfitting on limited datasets and high parameter overhead in conventional deep learning classifiers. Experiments are conducted on a balanced subset of the Reddit Self-Reported Depression Diagnosis (RSDD) dataset comprising 6,000 users. The proposed model is evaluated against classical baselines, including TF-IDF with logistic regression and a fine-tuned DistilBERT model. Results indicate that the hybrid approach achieves competitive performance, with an F1-score of 0.925 (±0.009) and improved recall (0.942 ± 0.015) compared to the classical DistilBERT baseline. Additionally, the quantum classification layer requires significantly fewer trainable parameters (72) compared to the classical dense head, demonstrating improved parameter efficiency at the classification stage. While the results suggest that variational quantum circuits can serve as an alternative non-linear classifier in low-data settings, the findings are based on simulation and require further validation on real quantum hardware. This work contributes to the emerging area of quantum natural language processing by providing an empirical evaluation of hybrid architectures on a real-world clinical text dataset.
Cite This Paper
Gaurav Kumar, Rakesh Kumar Saxena, Rocky Kumar, "A Hybrid Quantum-Classical Transformer Framework for Robust Depression Detection on Social Media: Enhancing Sensitivity via Variational Quantum Circuits", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.3, pp. 203-214, 2026. DOI:10.5815/ijieeb.2026.03.12
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