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.
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 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 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.
Abstract Unmanned systems play a crucial role in modern cyber-physical systems, supporting applications in defense, surveillance, and commercial operations. As these systems grow in scale and complexity, their reliability becomes increasingly challenging to assess due to intricate dependencies among components and subsystems. Traditional system reliability analysis using classical Bayesian networks struggle with computational scalability, limiting their applicability in large-scale systems. This study explores quantum Bayesian networks (QBNs) as a novel approach for reliability analysis and prediction in unmanned systems. A quantum circuit-based Bayesian network model is developed to represent component dependencies, leveraging quantum computing advantages such as superposition and entanglement. The methodology is applied to the communication system of an unmanned surface vessel using IBM’s Qiskit and executed on a supercomputer using quantum simulators. Results demonstrate the potential of quantum computing to enhance reliability and risk assessments, offering a scalable solution for applications relating to complex systems.
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.
Abstract Structural reliability analysis is essential for ensuring the safety of engineering structures, particularly complex systems like space trusses used in various industries. These 3D truss structures face uncertain loading conditions and deterioration, making reliability crucial. Traditional methods, such as Monte Carlo simulation, can be computationally intensive, especially for large systems. Quantum computing may offer a promising solution that utilizes qubits to process large amounts of information and simulate probabilistic models more efficiently. In this study, we explore the use of quantum circuits to represent the reliability problem, employing quantum algorithms like quantum Monte Carlo to estimate failure probabilities. The results indicate that quantum computing can perform structural reliability analysis, possibly reducing computational time and resources compared to classical methods. This approach shows potential to solve larger, more complex systems, providing scalable solutions for reliability analysis of engineering structures. Integrating quantum computing with structural reliability analysis could lead to more efficient, cost-effective designs of complex structures.