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Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations due to a large number of generators, loads, and transmission lines. Thus, its computational complexity grows exponentially with the system size. In this paper, we aim to evaluate the alternative computing approach, particularly the use of quantum computing algorithms to solve the power system dynamics. Leveraging a symbolic programming framework, we convert the power system dynamics' DAEs into an equivalent set of ordinary differential equations (ODEs). Their data can be encoded into quantum computers via amplitude encoding. The system's nonlinearity is captured by Taylor polynomial expansion and the quantum state tensor whereas state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the dynamics of the power system with high accuracy whereas its complexity is polynomial in the logarithm of the system dimension., 6 pages, 8 figures, conference paper