Fault Trees represent an essential tool in the reliability and risk assessment of engineering systems. By decomposing the structure of the system into Boolean function, Fault Trees allow the quantitative and qualitative analysis of the system. One of the main important tasks in Fault Tree analysis is the identification of Minimal Cut Sets, defined as groups of components that present the least path of resistance toward a system's failure. Identifying them allows reliability engineers to enhance the reliability and safety of the system, making system failures less likely to occur. However, the minimal cut set identification problem is challenging to solve, due to the exponential growth experienced in the number of feasible configurations as the system's size grows linearly. Over the last few years, quantum computation has been heralded as a promising tool to tackle computational challenges of increased complexity. The reason for this is the promising prospects that the use of quantum effects has for challenging computational tasks. However, its application into Probabilistic Risk Assessment and reliability engineering, and in particular to challenges related to the Fault Tree model, is still uncharted territory. To fill this gap, the objective of the paper is to integrate quantum computation into the Fault Tree Model and present an assessment of their capabilities for the minimal cut set identification problem. To this end, this paper proposes a novel algorithm to encode a fault tree into a quantum computer and to perform the identification of minimal cut sets with increased efficiency via the application of the Quantum Amplitude Amplification protocol. For validation purposes, a series of theoretical and numerical results, the latter obtained using a quantum simulator, are presented in which the proposed algorithm is compared against traditional approaches, such as Monte Carlo sampling.
Fault Trees represent an essential tool in the reliability and risk assessment of engineering systems. By decomposing the structure of the system into Boolean function, Fault Trees allow the quantitative and qualitative analysis of the system. One of the main important tasks in Fault Tree analysis is the identification of Minimal Cut Sets, defined as groups of components that present the least path of resistance toward a system's failure. Identifying them allows reliability engineers to enhance the reliability and safety of the system, making system failures less likely to occur. However, the minimal cut set identification problem is challenging to solve, due to the exponential growth experienced in the number of feasible configurations as the system's size grows linearly. Over the last few years, quantum computation has been heralded as a promising tool to tackle computational challenges of increased complexity. The reason for this is the promising prospects that the use of quantum effects has for challenging computational tasks. However, its application into Probabilistic Risk Assessment and reliability engineering, and in particular to challenges related to the Fault Tree model, is still uncharted territory. To fill this gap, the objective of the paper is to integrate quantum computation into the Fault Tree Model and present an assessment of their capabilities for the minimal cut set identification problem. To this end, this paper proposes a novel algorithm to encode a fault tree into a quantum computer and to perform the identification of minimal cut sets with increased efficiency via the application of the Quantum Amplitude Amplification protocol. For validation purposes, a series of theoretical and numerical results, the latter obtained using a quantum simulator, are presented in which the proposed algorithm is compared against traditional approaches, such as Monte Carlo sampling.
Abstract Failure probability estimation and identifying failure paths are crucial for ensuring the reliable operation of complex systems like autonomous ships. Fault tree Analysis is a widely used method to assess potential causes of system failure. However, as system complexity increases, identifying minimal cut sets becomes computationally challenging. This study explores the use of quantum computing to overcome these limitations. By encoding fault events as qubits, quantum computing leverages superposition and entanglement to accelerate computations. We developed a quantum fault tree circuit and applied the quantum amplitude amplification (QAA) algorithm to efficiently estimate failure probabilities and identify the minimal cut sets. Using IBM’s Qiskit environment, we implemented and tested the methodology on the power system of an autonomous ship. The results demonstrate the potential of quantum computing to enhance failure risk analysis, enabling efficient assessment of large, complex systems beyond the capabilities of classical computers.
Fault tree analysis is a technique widely used in risk and reliability analysis of complex engineering systems given its deductive nature and relatively simple interpretation. In a fault tree, events are usually represented by a binary variable that indicates whether an event occurs or not, traditionally associated with the values 1 and 0, respectively. Different events are linked together using logical gates, modelling the dependencies that a subsystem or system may have over its basic components. In this study, quantum computing is leveraged to propose a novel approach to encode a traditional fault tree into a quantum algorithm. This quantum fault tree method uses quantum bits to represent basic events, effectively encoding the original fault tree into a quantum circuit. The execution of the resulting quantum circuit represents a full simulation of the fault tree, and multiple executions can be utilized to compute the failure probability of the whole system. The proposed approach is tested on a case study portraying a dynamic positioning system. Results verify that the quantum-based proposed approach is able to effectively obtain the dynamic positioning failure probability through simulation, opening promising opportunities for future investigations in the area.
Motivated by its unique capabilities, quantum computation has gained significant attention over the last decade with numerous models and algorithms proposed for dealing with engineering challenges.The field of risk and reliability has also seen a growing interest in this area, with studies exploring Quantum Machine Learning for remaining useful life, Quantum Optimization for condition monitoring in civil structures, and Quantum Inference for enhancing Bayesian network models, to name a few.However, a common limitation across these works is the lack of thorough comparisons between the proposed quantum algorithms and their state-of-the-art classical counterparts.This critical gap must be addressed not only to evaluate the field's current state reliably but also to guide its development toward the most promising paths for achieving a quantum advantage.There are two key challenges to addressing this gap.First, quantum computation operates on fundamentally different principles than traditional computing, making direct comparisons -such as using the number of iterations -often infeasible.Second, large-scale, error-corrected quantum computers are not yet available, so machine-to-machine comparisons are also not yet possible.In this paper, we detail a novel methodology to evaluate quantum algorithms against their classical counterparts.Our technique is based on a simple observation: quantum computers do not extend the operations that a classical computer can perform.Instead, they have the potential to make them more efficient.As such, when large problems are considered, they ought to present a shorter runtime than classical algorithms to surpass, in any sense of the word, a classical algorithm.We validate the proposed methodology by applying it to an exciting application of quantum computation within the field of system reliability: the identification of minimal cut sets via the leverage of the Grover algorithm and Quantum Amplitude Amplification.
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.
Operational demands in industries, such as the energy sector, underscore the critical need for reliable equipment capable of withstanding long-term planning and unpredictable factors.Reliability assessment is important for maintaining productivity and optimizing maintenance strategies, especially in scenarios where data limitations challenge traditional assessment methods.In this context, Bayesian inference has emerged as a dynamic tool to update reliability estimates using data from various hierarchical levels.However, conventional simulation techniques may lack computational efficiency when dealing with the reliability estimation of complex systems, creating opportunities to explore alternative approaches such as quantum computation techniques.Quantum Computing leverages principles of quantum mechanics, such as superposition and entanglement, to try to address these computational challenges more effectively.Previous works have applied quantum Bayesian networks using amplitude amplification methods to the context of risk and reliability, focusing on nodes representing discrete probability distributions.This research aims to enhance this approach by incorporating continuous marginal and conditional probabilities into the analysis, which is particularly relevant for systems that rely on these distributions to model events.We explore the encoding of continuous probability distributions within the amplitude amplification framework, aiming to improve the efficiency and precision of probabilistic inference.Additionally, we apply this methodology to Bayesian networks, benchmarking the performance of quantum methods against classical simulation techniques like Monte Carlo to identify scenarios where quantum techniques demonstrate clear advantages.
The increased availability of specialized quantum hardware has made possible the exploration of the field by the general scientific community. Enterprises such as IBM, Google, and Microsoft have launched or will launch shortly cloud quantum computing services, which will accelerate forward the development of the field even further. It is, therefore, necessary to prepare the RAMS community to harness the advantages that quantum computing may offer to our discipline. In this paper, we present a proposed application for efficient probabilistic inference in Bayesian networks using gate-based quantum computing and a quantum algorithm called Amplitude Amplification. The proposed methodology is exemplified on a non-trivial Bayesian network, composed of nodes representing discrete probability distributions. To the best of our knowledge, this is the first attempt to use Bayesian networks in combination with Amplitude Amplification for the risk and reliability assessment context. The results indicate that the usage of quantum computing can effectively increase the efficiency of the sampling process, decreasing the number of samples required to obtain estimates of similar quality when compared to a traditional Monte Carlo sampling approach.