Beihang University,
China
Man Zhang is an Associate Professor at Beihang University. Her research focuses on the verification and validation of complex systems, particularly microservices and cyber-physical systems. She explores novel approaches that leverage advanced techniques, including model-based and search-based methods, artificial intelligence, and quantum optimization, to address industrial challenges.
Quantum and quantum-inspired optimization methods are increasingly explored as promising approaches for addressing complex combinatorial problems. Many software engineering (SE) tasks such as test case prioritization, requirements selection, release planning, and system configuration can be formulated as optimization problems, making them suitable candidates for these techniques. This lecture introduces several representative quantum and quantum-inspired optimization algorithms and focuses on how they can be applied to software engineering problems. In particular, the talk discusses how SE problems can be systematically encoded into optimization formulations, such as QUBO or Ising models, enabling them to be solved by quantum or quantum-inspired optimization methods. The lecture also reviews empirical studies that evaluate the effectiveness of these approaches in SE contexts. Through representative case studies, we examine how different encoding strategies influence solution quality, scalability, and computational performance when compared with classical optimization techniques.
