As quantum computing continues to develop, quantum-inspired algorithms are emerging as powerful tools that leverage quantum principles on classical hardware to solve complex optimization problems. Universities are uniquely positioned to support research in this area by enabling access to compute resources tailored to the specific demands of quantum-inspired techniques. This session explores the strategies, challenges, advantages and best practices in supporting quantum-inspired optimization workloads on a university compute cluster. We will discuss software frameworks (such as pyqubo, Matlab tools, D-Wave's Ocean tools and NEC's Vector Annealing software) and specialized hardware considerations. The session will also highlight case studies from active research projects.
