Lightweight structural design requires balancing conflicting objectives: minimizing mass while maximizing crashworthiness, energy absorption, and structural integrity. Conventional optimization techniques—such as gradient-based solvers, evolutionary algorithms, and surrogate modeling—often struggle with the nonlinear, multi-objective, and combinatorial nature of structural configurations, especially in applications like automotive chassis design, aerospace components, and protective systems. This study explores the use of quantum algorithms to accelerate and enhance the optimization of lightweight structures with respect to crash energy absorption. Leveraging quantum annealing, the Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantum–classical variational methods, the framework maps topology, material distribution, and structural geometry to quantum-encoded optimization landscapes. Early simulation results indicate that quantum-enabled solvers can identify higher-performing structural designs and navigate the trade-off between stiffness and crash energy dissipation more efficiently than classical algorithms. These findings highlight the potential of quantum computing to revolutionize structural engineering workflows by delivering faster, more robust, and more scalable optimization for next-generation lightweight designs.
Lightweight structural design requires balancing conflicting objectives: minimizing mass while maximizing crashworthiness, energy absorption, and structural integrity. Conventional optimization techniques—such as gradient-based solvers, evolutionary algorithms, and surrogate modeling—often struggle with the nonlinear, multi-objective, and combinatorial nature of structural configurations, especially in applications like automotive chassis design, aerospace components, and protective systems. This study explores the use of quantum algorithms to accelerate and enhance the optimization of lightweight structures with respect to crash energy absorption. Leveraging quantum annealing, the Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantum–classical variational methods, the framework maps topology, material distribution, and structural geometry to quantum-encoded optimization landscapes. Early simulation results indicate that quantum-enabled solvers can identify higher-performing structural designs and navigate the trade-off between stiffness and crash energy dissipation more efficiently than classical algorithms. These findings highlight the potential of quantum computing to revolutionize structural engineering workflows by delivering faster, more robust, and more scalable optimization for next-generation lightweight designs.
Next-generation mobility systems—ranging from electric vertical takeoff and landing (eVTOL) vehicles to hypersonic aircraft and high-efficiency autonomous drones—require advanced aerothermal and structural optimization techniques to meet demands for safety, performance, and sustainability. Traditional computational approaches, such as finite element analysis, computational fluid dynamics (CFD), and multi-physics simulation, face increasing computational burdens due to the nonlinear interactions among thermal loads, aerodynamic behavior, material deformation, and structural integrity. This study investigates the role of quantum computing in accelerating and improving aerothermal and structural optimization workflows. Leveraging quantum algorithms, including quantum annealing, variational quantum eigensolvers (VQE), quantum approximate optimization algorithms (QAOA), and quantum-inspired multi-objective solvers, the proposed framework enhances the exploration of high-dimensional design spaces and improves the computational efficiency of complex optimization tasks. Early experiments on benchmark aerothermal–structural models demonstrate improved convergence rates, superior Pareto-optimal solutions, and enhanced predictive accuracy compared to classical methods. The findings show that quantum computing has the potential to fundamentally transform mobility system design by enabling faster iteration cycles, better thermal–structural co-optimization, and more energy-efficient architectures suitable for sustainable transportation futures.
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.
The design and operation of renewable energy conversion systems—such as wind turbines, solar photovoltaic arrays, and hybrid storage networks—require balancing multiple, often conflicting objectives, including energy efficiency, cost minimization, environmental impact, and system reliability. Traditional optimization techniques, while effective for isolated parameters, struggle with the combinatorial complexity and nonlinear interdependencies inherent to integrated energy systems. This study explores the application of quantum optimization algorithms in multi-objective renewable energy design, emphasizing how quantum annealing, Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE) frameworks can accelerate solution discovery and improve design precision. By leveraging the principles of superposition and entanglement, quantum systems can process vast design spaces in parallel, enabling the rapid evaluation of trade-offs among competing objectives. A hybrid quantum–classical workflow is proposed to integrate quantum solvers into existing computational pipelines for system simulation, control optimization, and energy forecasting. Preliminary modeling results and conceptual simulations indicate that quantum-enhanced optimization not only reduces computation time but also achieves higher Pareto-optimal efficiency compared to conventional multi-objective genetic algorithms and particle swarm optimizers. This interdisciplinary approach demonstrates the transformative potential of quantum computation in sustainable energy system design, supporting the global transition toward carbon neutrality and energy equity. By embedding quantum optimization into renewable infrastructure development, the study provides a foundation for next-generation decision-making frameworks that harmonize performance, sustainability, and scalability.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems.This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm.Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks.The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals.This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.
• Quantum and classical algorithms are benchmarked for building performance optimization. • A surrogate model is trained on 15,000 IDA ICE simulations for prediction. • NSGA-II achieves the best trade-offs between energy use and thermal comfort. • Quantum methods deliver faster results but with reduced solution quality. • Quantum computing shows potential for future real-time building optimization. Optimizing building performance is essential for enhancing energy efficiency and occupant comfort. This study evaluates the applicability of quantum computing–based optimization methods in the Architecture, Engineering, and Construction (AEC) industry by comparing the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) with classical multi-objective optimization algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A dataset of 15,000 building simulations was used to train an Extreme Gradient Boosting (XGBoost) model for predicting total energy consumption (kWh/m 2 /year) and Predicted Percentage of Dissatisfied (PPD) occupants. These predictions were then used in the optimization phase. NSGA-II produced the best trade-offs, achieving energy consumption between 17.84 and 19.84 kWh/m 2 /year and PPD below 5.2 %, with strong diversity and convergence. QAOA executed faster (0.54 min) than NSGA-II (18.9 min) but resulted in higher energy values (31.85–55.62 kWh/m 2 /year) and weaker solution quality. Quantum Annealing completed in 0.37 min but returned clustered solutions near 45.88 kWh/m 2 /year. While the current limitations of quantum methods constrain their effectiveness, the findings indicate their potential as fast solvers in future building performance optimization workflows, particularly as hardware and algorithmic capabilities mature.