Adiabatic Quantum Linear Optimal Control for Discrete Time Dynamical Systems​

Scientific Achievement​

• Reformulated finite-horizon linear optimal control as QUBO problems suitable for adiabatic quantum computing.​
• Basis functions remove horizon dependence from binary-variable counts while retaining acceptable control accuracy.​

Significance and Impact​

• Opens a quantum-computing pathway for large optimal-control problems limited by cubic classical scaling.​
• Reduced QUBO size makes longer control horizons compatible with current quantum-annealing resource limits.​
• ORNL and Université de Lorraine collaboration connects quantum optimization with control engineering.​

Technical Approach​

• Mapped block-Toeplitz least-squares control costs into binary precision vectors and canonical QUBO form.​
• Introduced basis-function parameterization reducing binary variables from horizon-dependent NmL to fixed rmL.​
• Compared QUBO and classical solvers across increasing horizons, demonstrating favorable large-problem scaling.​