Feedback-based quantum control for safe and synergistic drug combination design

Drug-drug interactions (DDIs) play an important role in the safety and efficacy of combination therapies. Despite the availability of large DDI databases, selecting multi-drug combinations that balance safety, therapeutic benefit, and the number of drugs remains a challenging combinatorial problem. In this work, we present a quantum-control-based framework for DDI-aware drug combination optimization, in which known harmful and synergistic interactions are encoded into Ising Hamiltonians as penalties and rewards. The optimization is carried out using the feedback-based quantum algorithm FALQON, a gradient-free variational approach. We consider two clinically motivated tasks: the Maximum Safe Subset problem and the Synergy-Constrained Optimization problem. Numerical simulations on small-scale datasets, including examples based on Drugs.com and SYNERGxDB, illustrate the feasibility of the approach and its ability to identify meaningful drug combinations, including COVID-19 case studies. These results serve as a proof-of-principle and a test-bed for applying quantum optimization methods to DDI-aware problems.