  Power system fault diagnosis is crucial for identifying the location and
causes of faults and providing decision-making support for power dispatchers.
However, most classical methods suffer from significant time-consuming, memory
overhead, and computational complexity issues as the scale of the power system
concerned increases. With rapid development of quantum computing technology,
the combinatorial optimization method based on quantum computing has shown
certain advantages in computational time over existing methods. Given this
background, this paper proposes a quantum computing based power system fault
diagnosis method with the Quantum Approximate Optimization Algorithm (QAOA).
The proposed method reformulates the fault diagnosis problem as a Hamiltonian
by using Ising model, which completely preserves the coupling relationship
between faulty components and various operations of protective relays and
circuit breakers. Additionally, to enhance problem-solving efficiency under
current equipment limitations, the symmetric equivalent decomposition method of
multi-z-rotation gate is proposed. Furthermore, the small probability
characteristics of power system events is utilized to reduce the number of
qubits. Simulation results based on the test system show that the proposed
methods can achieve the same optimal results with a faster speed compared with
the classical higher-order solver provided by D-Wave.
