- By:
- Jha, Mayank; Date, Prasanna A
- Page Number:
- 308-313
- Book Title:
- 2025 IEEE International Conference on Quantum Computing and Engineering (QCE)
- Publication Date:
- September 17, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2025 IEEE International Conference on Quantum Computing and Engineering (QCE)
- Conference Location:
- Albuquerque, New Mexico, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/QCE65121.2025.10341
Abstract
This paper presents a novel approach for solving finite-horizon linear optimal control based on block-Toeplitz least-squares problem, by recasting the latter as Quadratic Unconstrained Binary Optimization (QUBO) suitable for adiabatic quantum computing (AQC). Classical block-Toeplitz leastsquares problem, scales as O((Nm)3) for horizon length N and control dimension m. We demonstrate that this Toeplitzstructured least-squares cost can be transformed into a QUBO formulation by introducing binary precision vectors that encode each continuous control parameter into finite number of bits. We establish the general case in mathematically rigorous manner wherein the total number of binary decision variables remain dependent on N. To respect current quantum annealer capabilities, we propose a basis-function parametrization that approximates the full control sequence with a small set of basis coefficients, reducing the total number of binary-variables and rendering the latter independent of N. Through simulation study, we show that, for large datasets, the QUBO pipeline, comprising of hardware-constant anneal step outperforms classical least square based solvers by several factors whilst demonstrating acceptable accuracy.
