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Quantum computing is an emerging computational paradigm with growing potential to address complex simulation and optimization problems in engineering and applied sciences. In particular, quantum and quantum-inspired optimization methods are well suited to combinatorial problems that can be formulated using binary decision variables, such as scheduling, resource allocation, network design, and recovery planning. These problems are commonly reformulated as Quadratic Unconstrained Binary Optimization (QUBO) models, which can be solved using quantum annealers, gate-based quantum algorithms, or hybrid quantum–classical strategies. The seminar will introduce the fundamental principles of quantum optimization and discuss both its current capabilities and its main limitations. A short hands-on session will guide participants through the formulation and solution of a simple quantum optimization problem. The second part of the seminar will present our recent work on resilience-driven optimization of recovery sequences for power-network infrastructures following disruptive events. The objective is to determine the order in which damaged components should be restored in order to minimize service disruption and accelerate system recovery. The problem is formulated as a constrained combinatorial optimization problem and mapped into a QUBO representation suitable for quantum and quantum-inspired solvers. Particular attention will be given to the integration of resilience into the optimization framework in the resulting recovery strategy.

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