Entangling Oncology: Variational Quantum Eigensolver-Driven Optimization for Patient-Specific Multi-Modal Cancer Therapy Planning

Optimizing personalized cancer treatment plans that combine chemotherapy, radiation, and immunotherapy presents a complex challenge due to the need to balance efficacy with minimizing side effects across diverse patient profiles. This study introduces a novel hybrid quantum-classical framework leveraging the Variational Quantum Eigensolver (VQE) for multi-modal optimization. Treatment planning is formulated as a parameterized Hamiltonian, where the ground state corresponds to an optimal therapy regimen tailored to individual genomic and clinical data. The quantum VQE algorithm estimates ground state energy via quantum simulators, while classical optimizers iteratively refine circuit parameters. Benchmarking on synthetic datasets demonstrates that the hybrid approach enhances treatment personalization and computational efficiency over classical methods. Although current quantum hardware constraints limit immediate deployment, our findings validate the potential for quantum-enhanced optimization in clinical oncology. This research lays foundational groundwork for integrating near-term quantum algorithms into medical decision-making, with promising implications for improving cancer treatment outcomes and patient quality of life.