BioTwin-Q: A Causal Multimodal Quantum Digital Twin for Personalized Drug Discovery and Precision Healthcare

Precision healthcare requires models that can integrate heterogeneous patient data, reason about treatment effects rather than correlations alone, and evaluate therapeutic options before clinical deployment. Existing digital-twin approaches provide patient-specific simulation, while causal machine learning supports individualized treatment-effect estimation and quantum machine learning offers emerging tools for molecular representation and optimization. This paper proposes BioTwin-Q, a causal multimodal quantum digital twin framework that combines electronic health records, omics, medical imaging, wearable signals, clinical laboratory data, drug molecular representations and biomedical knowledge in a continuously updated patient twin. A structural causal model forms the reasoning layer for intervention and counterfactual analysis, while a hybrid quantum-classical module is selectively applied to molecular property prediction and candidate optimization. The framework identifies three design-level findings: multimodal fusion is required to reduce single-source bias, causal constraints are required to distinguish actionable treatment effects from predictive associations, and quantum components should be used as task-specific accelerators rather than replacements for classical models. BioTwin-Q is intended for drug prioritization, treatment-response simulation, adverse-event risk assessment and clinician-in-the-loop precision decision support. The paper also defines an evaluation protocol covering predictive accuracy, calibration, individualized treatment effects, molecular validity, uncertainty and safety.