  Transient stability assessment (TSA), a cornerstone for resilient operations
of today's interconnected power grids, is a grand challenge yet to be addressed
since the genesis of electric power systems. This paper is a confluence of
quantum computing, data science and machine learning to potentially resolve the
aforementioned challenge caused by high dimensionality, non-linearity and
uncertainty. We devise a quantum TSA (qTSA) method, a low-depth, high
expressibility quantum neural network, to enable scalable and efficient
data-driven transient stability prediction for bulk power systems. qTSA renders
the intractable TSA straightforward and effortless in the Hilbert space, and
provides rich information that enables unprecedentedly resilient and secure
power system operations. Extensive experiments on quantum simulators and real
quantum computers verify the accuracy, noise-resilience, scalability and
universality of qTSA. qTSA underpins a solid foundation of a quantum-enabled,
ultra-resilient power grid which will benefit the people as well as various
commercial and industrial sectors.
