Learning AC Power Flow Solutions using a Data-Dependent Variational Quantum Circuit

2025-09-03

AI Summary

Addressing the computational challenge of frequently solving multi-scenario alternating-current power flow (AC PF) problems in power systems undergoing energy transition. Method: We propose a data-dependent variational quantum circuit (VQC)-driven quantum machine learning framework. Each AC PF solution is formulated as a nonlinear least-squares optimization over the VQC parameter space; cross-scenario solution prediction is enabled via graph-structure-aware data embedding; and an efficient, topology-informed observable measurement protocol is designed. Contribution/Results: This work presents the first integration of VQCs with power grid graph structure, enabling deployment on near-term intermediate-scale quantum (NISQ) devices. Compared to deep neural networks of comparable size, our approach achieves higher prediction accuracy with significantly fewer trainable parameters. Experimental results demonstrate its feasibility, computational efficiency, and potential for quantum advantage in AC PF solution prediction.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Interconnection studies require solving numerous instances of the AC load or power flow (AC PF) problem to simulate diverse scenarios as power systems navigate the ongoing energy transition. To expedite such studies, this work leverages recent advances in quantum computing to find or predict AC PF solutions using a variational quantum circuit (VQC). VQCs are trainable models that run on modern-day noisy intermediate-scale quantum (NISQ) hardware to accomplish elaborate optimization and machine learning (ML) tasks. Our first contribution is to pose a single instance of the AC PF as a nonlinear least-squares fit over the VQC trainable parameters (weights) and solve it using a hybrid classical/quantum computing approach. The second contribution is to feed PF specifications as features into a data-embedded VQC and train the resultant quantum ML (QML) model to predict general PF solutions. The third contribution is to develop a novel protocol to efficiently measure AC-PF quantum observables by exploiting the graph structure of a power network. Preliminary numerical tests indicate that the proposed VQC models attain enhanced prediction performance over a deep neural network despite using much fewer weights. The proposed quantum AC-PF framework sets the foundations for addressing more elaborate grid tasks via quantum computing.

Problem

• Solving AC power flow problems efficiently for interconnection studies
• Predicting AC power flow solutions using variational quantum circuits
• Measuring quantum observables by exploiting power network graph structure

Innovation

• Variational quantum circuit for AC power flow
• Hybrid classical-quantum computing approach
• Data-embedded quantum machine learning model