Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA challenge. We devise a quantum TSA (qTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A low-depth, high expressibility quantum neural network for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm for efficient qTSA training; 3) A systematical analysis on qTSA's performance under various quantum factors. qTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of qTSA.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningQuantum Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning
Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data privacy. Yet, conventional FL aggregation rem…
Federated LearningTensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing
Variational Quantum Computing (VQC) faces fundamental scalability barriers, primarily due to barren plateaus and sensitivity to quantum noise. To address these challenges, we introduce TensorHyper-VQC, a novel tensor-tra…
Quantum Machine LearningVQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning, yet their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges…
Quantum Machine LearningA Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor
In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience. The QHAM is based on a quantum neuron design which can be ut…
BIG-bench Machine LearningContinual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing
We introduce CL-QAS, a continual quantum architecture search framework that mitigates the challenges of costly amplitude encoding and catastrophic forgetting in variational quantum circuits. The method uses Tensor-Train …