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.
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.
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.
Quantum computing is an emerging technology that has the potential to change the perspectives and applications of computing in general. A wide range of applications are enabled: from faster algorithmic solutions of classically still difficult problems to theoretically more secure communication protocols. A quantum computer uses the quantum mechanical effects of particles or particle-like systems, and a major similarity between quantum and classical computers consists of both being abstracted as information processing machines. Whereas a classical computer operates on classical digital information, the quantum computer processes quantum information, which shares similarities with analog signals. One of the central differences between the two types of information is that classical information is more fault-tolerant when compared to its quantum counterpart. Faults are the result of the quantum systems being interfered by external noise, but during the last decades quantum error correction codes (QECC) were proposed as methods to reduce the effect of noise. Reliable quantum circuits are the result of designing circuits that operate directly on encoded quantum information, but the circuit’s reliability is also increased by supplemental redundancies, such as sub-circuit repetitions. Reliable quantum circuits have not been widely used, and one of the major obstacles is their vast associated resource overhead, but recent quantum computing architectures show promising scalabilities. Consequently the number of particles used for computing can be more easily increased, and that the classical control hardware (inherent for quantum computation) is also more reliable. Reliable quantum circuits haev been investigated for almost as long as general quantum computing, but their limited adoption (until recently) has not generated enough interest into their systematic design. The continuously increasing practical relevance of reliability motivates the present thesis to investigate some of the first answers to questions related to the background and the methods forming a reliable quantum circuit design stack. The specifics of quantum circuits are analysed from two perspectives: their probabilistic behaviour and their topological properties when a particular class of QECCs are used. The quantum phenomena, such as entanglement and superposition, are the computational resources used for designing quantum circuits. The discrete nature of classical information is missing for quantum information. An arbitrary quantum system can be in an infinite number of states, which are linear combinations of an exponential number of basis states. Any nontrivial linear combination of more than one basis states is called a state superposition. The effect of superpositions becomes evident when the state of the system is inferred (measured), as measurements are probabilistic with respect to their output: a nontrivial state superposition will collapse to one of the component basis states, and the measurement result is known exactly only after the measurement. A quantum system is, in general, composed from identical subsystems, meaning that a quantum computer (the complete system) operates on multiple similar particles (subsystems). Entanglement expresses the impossibility of separating the state of the subsystems from the state of the complete system: the nontrivial interactions between the subsystems result into a single indivisible state. Entanglement is an additional source of probabilistic behaviour: by measuring the state of a subsystem, the states of the unmeasured subsystems will probabilistically collapse to states from a well defined set of possible states. Superposition and entanglement are the building blocks of quantum information teleportation protocols, which in turn are used in state-of-the-art fault-tolerant quantum computing architectures. Information teleportation implies that the state of a subsystem is moved to a second subsystem without copying any information during the process. The probabilistic approach towards the design of quantum circuits is initiated by the extension of classical test and diagnosis methods. Quantum circuits are modelled similarly to classical circuits by defining gate-lists, and missing quantum gates are modelled by the single missing gate fault. The probabilistic approaches towards quantum circuits are facilitated by comparing these to stochastic circuits, which are a particular type of classical digital circuits. Stochastic circuits can be considered an emulation of analogue computing using digital components. A first proposed design method, based on the direct comparison, is the simulation of quantum circuits using stochastic circuits by mapping each quantum gate to a stochastic computing sub-circuit. The resulting stochastic circuit is compiled and simulated on FPGAs. The obtained results are encouraging and illustrate the capabilities of the proposed simulation technique. However, the exponential number of possible quantum basis states was translated into an exponential number of stochastic computing elements. A second contribution of the thesis is the proposal of test and diagnosis methods for both stochastic and quantum circuits. Existing verification (tomographic) methods of quantum circuits were targeting the reconstruction of the gate-lists. The repeated execution of the quantum circuit was followed by different but specific measurement at the circuit outputs. The similarities between stochastic and quantum circuits motivated the proposal of test and diagnosis methods that use a restricted set of measurement types, which minimise the number of circuit executions. The obtained simulation results show that the proposed validation methods improve the feasibility of quantum circuit tomography for small and medium size circuits. A third contribution of the thesis is the algorithmic formalisation of a problem encountered in teleportation-based quantum computing architectures. The teleportation results are probabilistic and require corrections represented as quantum gates from a particular set. However, there are known commutation properties of these gates with the gates used in the circuit. The corrections are not applied as dynamic gate insertions (during the circuit’s execution) into the gate-lists, but their effect is tracked through the circuit, and the corrections are applied only at circuit outputs. The simulation results show that the algorithmic solution is applicable for very large quantum circuits. Topological quantum computing (TQC) is based on a class of fault-tolerant quantum circuits that use the surface code as the underlying QECC. Quantum information is encoded in lattice-like structures and error protection is enabled by the topological properties of the lattice. The 3D structure of the lattice allows TQC computations to be visualised similarly to knot diagrams. Logical information is abstracted as strands and strand interactions (braids) represent logical quantum gates. Therefore, TQC circuits are abstracted using a geometrical description, which allows circuit input-output transformations (correlations) to be represented as geometric sub-structures. TQC design methods were not investigated prior to this work, and the thesis introduces the topological computational model by first analysing the necessary concepts. The proposed TQC design stack follows a top-down approach: an arbitrary quantum circuit is decomposed into the TQC supported gate set; the resulting circuit is mapped to a lattice of appropriate dimensions; relevant resulting topological properties are extracted and expressed using graphs and Boolean formulas. Both circuit representations are novel and applicable to TQC circuit synthesis and validation. Moreover, the Boolean formalism is broadened into a formal mechanism for proving circuit correctness. The thesis introduces TQC circuit synthesis, which is based on a novel logical gate geometric description, whose formal correctness is demonstrated. Two synthesis methods are designed, and both use a general planar representation of the circuit. Initial simulation results demonstrate the practicality and performance of the methods. An additional group of proposed design methods solves the problem of automatic correlation construction. The methods use validity criteria which were introduced and analysed beforehand in the thesis. Input-output correlations existing in the circuit are inferred using both the graph and the Boolean representation. The thesis extends the TQC state-of-the-art by recognising the importance of correlations in the validation process: correlation construction is used as a sub-routine for TQC circuit validation. The presented cross-layer validation procedure is useful when investigating both the QECC and the circuit, while a second proposed method is QECC-independent. Both methods are scalable and applicable even to very large circuits. The thesis completes with the analysis of TQC circuit identities, where the developed Boolean formalism is used. The proofs of former known circuit identities were either missing or complex, and the presented approach reduces the length of the proofs and represents a first step towards standardising them. A new identity is developed and detailed during the process of illustrating the known circuit identities. Reliable quantum circuits are a necessity for quantum computing to become reality, and specialised design methods are required to support the quest for scalable quantum computers. This thesis used a twofold approach towards this target: firstly by focusing on the probabilistic behaviour of quantum circuits, and secondly by considering the requirements of a promising quantum computing architecture, namely TQC. Both approaches resulted in a set of design methods enabling the investigation of reliable quantum circuits. The thesis contributes with the proposal of a new quantum simulation technique, no
Quantum computing has become a pivotal innovation in computational science, offering novel avenues for tackling the increasingly complex and high-dimensional optimization challenges inherent in engineering design. This paradigm shift is particularly pertinent in the domain of structural optimization, where the intricate interplay of design variables and constraints necessitates advanced computational strategies. In this vein, the gate-based variational quantum algorithm utilizes quantum superposition and entanglement to improve search efficiency in large solution spaces. This paper delves into the gate-based variational quantum algorithm for the discrete variable truss structure size optimization problem. By reformulating this optimization challenge into a quadratic, unconstrained binary optimization framework, we bridge the gap between the discrete nature of engineering optimization tasks and the quantum computational paradigm. A detailed algorithm is outlined, encompassing the translation of the truss optimization problem into the quantum problem, the initialization and iterative evolution of a quantum circuit tailored to this problem, and the integration of classical optimization techniques for parameter tuning. The proposed approach demonstrates the feasibility and potential of quantum computing to transform engineering design and optimization, with numerical experiments validating the effectiveness of the method and paving the way for future explorations in quantum-assisted engineering optimizations.
Structural analysis in high-performance computing (HPC) faces challenges related to computational complexity, energy efficiency, and solution accuracy. This research explores Quantum-Inspired Algorithms (QIAs) as an innovative approach to enhance computational efficiency and accuracy in large-scale structural simulations. The proposed methodology integrates a Quantum-Inspired Evolutionary Algorithm (QIEA) with a Hybrid Quantum-Inspired Neural Network (HQINN) for improved structural performance prediction. The study evaluates QIAs on three benchmark structural problems: Bridge Load Distribution Analysis – Achieves a computational speed-up of 45% compared to classical solvers while maintaining an error rate of <0.5%. The Quantum-Inspired Variational Monte Carlo (QIVMC) method is applied to solve complex eigenvalue problems, achieving an 8× acceleration in solving large-scale stiffness matrices compared to traditional iterative solvers. Experimental validation on a high-performance computing cluster using 1,024 cores demonstrates a 55% improvement in processing speed and a 37% reduction in energy consumption. Results confirm that Quantum-Inspired Algorithms significantly outperform traditional numerical methods in structural analysis, paving the way for their adoption in next-generation engineering simulations. Future work will focus on hybrid quantum-classical frameworks and their real-world applications in civil, aerospace, and automotive engineering.
Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems.