Under the dynamic evolution of catastrophic risks

resilience assessment for new power systems faces challenges such as low efficiency in high-dimensional scenario generation and exhaustive analysis

as well as inadequate quantification accuracy for high-impact low-probability events. Quantum computing

with strong parallel processing capabilities and enormous potential

promises to overcome the bottlenecks of classical computing. This paper proposes a novel hybrid quantum-classical resilience assessment approach for power systems. Firstly

the quantum computing model is developed for calculating power line failure probabilities with dynamically updated typhoon warning information. By leveraging quantum superposition to generate wind speed scenarios

the approach enables parallel estimation of failure probabilities under potential risk distributions

thereby overcoming the low efficiency of classical scenario traversal. Secondly

the resilience assessment model and framework driven by quantum circuits are designed to achieve accelerated double-convergence computation for tail risk metrics such as conditional value at risk

addressing the challenges of risk quantification in heavy-tailed distributions. Thirdly

the hybrid quantum-classical resilience assessment process is proposed

with quantum circuit optimization and Monte Carlo damage simulation combined to realize both rapid evaluation with short-term rolling warnings and full-process simulation of system failure risk losses. Finally

the effectiveness of the proposed approach is demonstrated by an IEEE 33-bus system on the IBM Qiskit quantum simulator.