Quantum-Enhanced Reliability Assessment of Power Networks in Response to Wildfire Events

Quantum-Enhanced Reliability Assessment of Power Networks in Response to Wildfire Events

Bayes methods

Extreme events

Fires

Power network

Probabilistic logic

Quantum Bayesian Inference

Quantum circuit

Quantum computing

Random access memory

Reliability engineering

Sociology

Vulnerability

Wildfire

As the risk associated with wildfires in the western United States increases every year, it becomes fundamental to research alternatives to both improve the resilience and reliability of engineering systems against those types of events, as well as mitigate the effects that wildfire emergencies may have on human population and critical infrastructure. An often- overlooked cause of wildfire emergencies are failures in power line systems and electricity distribution networks. Given the immense number of factors that can affect the probability of a power line ignition occurring, the analysis quickly becomes intractable for traditional probabilistic inference methodologies. In this paper, a quantum-enhanced probabilistic sampling and inference application is proposed for improving the acceptance ratio of queries of the form P(Q|E = e) when compared to traditional Monte-Carlo approaches. For this purpose, the system of interest is first modeled as a Bayesian network. Then, quantum computing is used to translate that Bayesian network into an equivalent quantum circuit using a set of basic quantum gates. Finally, the sampling process is enhanced by applying a quantum protocol called Amplitude Amplification to the resulting quantum circuit. Empirical results are presented using as a case study a non-trivial six-node Bayesian network.

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G. S. M. Silva

T. Parhizkar

H. T. Nguyen

E. L. Droguett

2023 Annual Reliability and Maintainability Symposium (RAMS)

2023

conferencePaper

2577-0993