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Vendredi 20 Mars
| Heure: |
14:00 - 16:00 |
| Lieu: |
Salle G202, Université de Villetaneuse |
| Résumé: |
Warm-Starting QAOA for Combinatorial Optimization via Difference-of-Convex Optimization - A Case Study on Max-Cut |
| Description: |
Viet Hung Nguyen The Quantum Approximate Optimization Algorithm (QAOA) has recently been proposed as a heuristic framework for solving combinatorial optimization problems through a hybrid classicalquantum optimization procedure. The algorithm alternates parameterized quantum transformations with a classical optimization step that adjusts the circuit parameters in order to increase the probability of sampling high-quality solutions. A key factor influencing the performance of QAOA is the choice of the initial state. In standard implementations, the algorithm starts from a uniform superposition over all candidate solutions, which does not exploit structural information about the original optimization problem and may lead to inefficient parameter optimization and lower-quality solutions.
In this talk, we propose a warm-start strategy based on continuous optimization, using the Difference-of-Convex Algorithm (DCA). The idea is to exploit a continuous relaxation of the original optimization problem in order to construct an informed initialization that biases the search toward promising regions of the solution space. We illustrate the approach on instances of the Max-Cut problem and show that this strategy can significantly improve the approximation ratios obtained by QAOA. This is a joint work with HA Huy Phuc Nguyen et TA Anh Son. |
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