  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.
