Quantum Power Flows: From Theory to Practice
This work addresses the challenge of optimizing renewable energy grids using quantum computing, but it is incremental as it focuses on benchmarking and near-term implementations rather than achieving full practical solutions.
The paper explores the application of quantum computing algorithms, such as HHL, to power-flow problems in smart grids, suggesting potential exponential speedup but highlighting practical limitations like noise and hardware requirements, with experimental implementation on 6 qubits for a truncated version.
Climate change is becoming one of the greatest challenges to the sustainable development of modern society. Renewable energies with low density greatly complicate the online optimization and control processes, where modern advanced computational technologies, specifically quantum computing, have significant potential to help. In this paper, we discuss applications of quantum computing algorithms toward state-of-the-art smart grid problems. We suggest potential, exponential quantum speedup by the use of the Harrow-Hassidim-Lloyd (HHL) algorithms for sparse matrix inversions in power-flow problems. However, practical implementations of the algorithm are limited by the noise of quantum circuits, the hardness of realizations of quantum random access memories (QRAM), and the depth of the required quantum circuits. We benchmark the hardware and software requirements from the state-of-the-art power-flow algorithms, including QRAM requirements from hybrid phonon-transmon systems, and explicit gate counting used in HHL for explicit realizations. We also develop near-term algorithms of power flow by variational quantum circuits and implement real experiments for 6 qubits with a truncated version of power flows.