Speaker
Guillermo Javier Preisser Beltrán
(Quantinuum)
Description
We present variational methods for combinatorial optimization based on product-state (PS) and matrix-product-state (MPS) ansatzes. The approach performs variational energy minimization of a quantum annealing Hamiltonian and incorporates stochastic exploration through an iterated local search (ILS) metaheuristic. The resulting quantum-inspired optimization algorithms are benchmarked on Max-Cut instances containing up to 50,000 variables. We demonstrate that the proposed methods outperform traditional (M)PS approaches, classical ILS, the Quantum Approximate Optimization Algorithm (QAOA), and several other variational quantum-inspired optimization techniques.
| Topic | Tensor methods and quantum-inspired machine learning |
|---|---|
| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |
Author
Guillermo Javier Preisser Beltrán
(Quantinuum)
Co-authors
Dr
Conor Mc Keever
(Quantinuum)
Dr
Michael Lubasch
(Quantinuum)