Data-driven quantum approximate optimization algorithm for power systems

Jing, Hang; Wang, Ye; Li, Yan

doi:10.1038/s44172-023-00061-8

Title: Data-driven quantum approximate optimization algorithm for power systems

Journal Article · 08 March 2023 · Communications Engineering

DOI: https://doi.org/10.1038/s44172-023-00061-8 · OSTI ID:1963340

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Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0019449

OSTI ID:: 1963340

Journal Information:: Communications Engineering, Journal Name: Communications Engineering Journal Issue: 1 Vol. 2; ISSN 2731-3395

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: Country unknown/Code not available

Language:: English

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