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.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.

Sep 10, 20250 citations

Quantum reliability function

Masaki Sohma

Proceedings of the IEICE General ConferenceMar 7, 20010 citations

Quantum Bayesian Networks for Reliability Analysis of Unmanned Systems

Siddhesh Salfale, Gunjana Dhakad, Sonia Bara +2 more

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.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.

Sep 10, 20251 citation

Arqon: A suite of control applications enabling a reliable quantum network

Scarlett Gauthier, Thomas R. Beauchamp, Stephanie Wehner

A quantum network's purpose is to enable users to execute applications on end nodes. This requires the network to provide the service of creating entangled links between those nodes. Users of mature networks, such as the internet or the telephone network expect accepted service demands to be met reliably. We first define reliability requirements that extend classical computer network concepts to quantum network service delivery. We then introduce Arqon, a suite of control applications designed to deliver reliable service in centrally controlled quantum networks. We demonstrate through both analytic and numerical evaluation that Arqon satisfies all reliability requirements for accepted demands. These evaluations consider static network topologies. We provide a complete Python implementation and perform complexity analysis showing that admission control scales as $O(k^3)$ in the number of incoming demands $k$ and schedule computation scales as ${O(N^3)}$ in the number of accepted demands to schedule $N$ .A quantum network's purpose is to enable users to execute applications on end nodes. This requires the network to provide the service of creating entangled links between those nodes. Users of mature networks, such as the internet or the telephone network expect accepted service demands to be met reliably. We first define reliability requirements that extend classical computer network concepts to quantum network service delivery. We then introduce Arqon, a suite of control applications designed to deliver reliable service in centrally controlled quantum networks. We demonstrate through both analytic and numerical evaluation that Arqon satisfies all reliability requirements for accepted demands. These evaluations consider static network topologies. We provide a complete Python implementation and perform complexity analysis showing that admission control scales as $O(k^3)$ in the number of incoming demands $k$ and schedule computation scales as ${O(N^3)}$ in the number of accepted demands to schedule $N$.

quant-pharXiv:2604.08692Apr 13, 20260 citations

O(k^3)

in the number of incoming demands

k

and schedule computation scales as

{O(N^3)}

in the number of accepted demands to schedule

N