Quantum Annealing

Combinatorial optimization problems are known to be NP-hard, and no polynomial-time algorithm is known for finding exact solutions. However, by formulating them as quadratic unconstrained binary optimization (QUBO) problems, quantum annealing technology can be applied. We research novel approaches for efficiently solving combinatorial optimization problems in drug discovery using quantum annealers.

• QUBO formulation of fragment-based docking: A QUBO problem is formulated by encoding constraints such as steric clashes and covalent bonds between fragments, enabling quantum annealing-based docking calculations
• Quantum-classical hybrid pre-screening: Development of pre-screening methods leveraging quantum annealing for combinatorial optimization of fragment placements, enabling rapid identification of promising compounds
• Black-box optimization: Application of machine learning-based QUBO formulation to problems where manual QUBO formulation is difficult