Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor

This work explores the use of variational quantum algorithms to optimize energy distribution among users in energy communities, using real data from a community lab. This requires integrating various energy sources, storage solutions, and the ability to respond to variations in demand within energy systems, while maintaining the capacity to adapt to the variability of renewable energy sources. Given the increasing complexity as the problem size grows and the limitations of classical computing methods in addressing it, we study an approach that models the problem using QUBO and solves it applying quantum optimization algorithms. Our analysis includes a comparative study of two variational techniques, QAOA and VQE, and the implementation of the former on Qmio, an actual superconducting quantum processor. Although deploying QAOA on Qmio required specific adaptations to the hardware, we were able to achieve a shift of the probability distribution towards lower-energy solutions without error mitigation techniques, which highlights both QAOA’s potential and the intrinsic challenges of quantum optimization.