Abstract Studying the propagation of failure probabilities in interconnected systems such as electrical distribution networks is traditionally performed by means of Monte Carlo simulations. In this paper, we propose a procedure for creating a model of the system on a quantum computer using a restricted representation of Bayesian networks. We present examples of this implementation on sample models using Qiskit and test them using both quantum simulators and IBM Quantum hardware. The results show a correlation in the precision of the results when considering the number of Monte Carlo iterations alongside the sum of shots in a single quantum circuit execution.
Abstract Studying the propagation of failure probabilities in interconnected systems such as electrical distribution networks is traditionally performed by means of Monte Carlo simulations. In this paper, we propose a procedure for creating a model of the system on a quantum computer using a restricted representation of Bayesian networks. We present examples of this implementation on sample models using Qiskit and test them using both quantum simulators and IBM Quantum hardware. The results show a correlation in the precision of the results when considering the number of Monte Carlo iterations alongside the sum of shots in a single quantum circuit execution.
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware.
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.
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 incorporation of renewable energy and the expanding size of power systems have elevated operational uncertainty, thereby raising the risk of load loss. Additionally, the analysis duration has been extended as a consequence. Swift and imperative risk assessments are vital for guaranteeing the safety and reliability of modern power systems operations. In this paper, the utilization of Quantum computing methodologies to enhance the efficiency of the risk assessment in modern power systems is explored. Two prevalent methods, namely Monte Carlo simulation and Bayesian networks, are the focus of this study. The first part introduces a Quantum Monte Carlo circuit designed to analyze risk with greater efficiency compared to the conventional Monte Carlo simulations commonly employed on classical computers. Moreover, the exploration of Quantum computing in Bayesian networks offers a novel approach for modeling and analyzing dependencies within power systems. The potential of Quantum computing to redefine risk assessment methodologies in the domain of power system reliability is illuminated by this research.
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.
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.