Quantum supremacy refers to the point at which quantum computers outperform classical high-performance computing (HPC) systems in solving computations that are infeasible for conventional systems. Real-time optimization problems—such as traffic flow, financial modeling, logistics, portfolio balancing, cryptographic simulations, and supply chain design—require massive computation and near-instant decision-making, presenting a strong testbed for demonstrating true quantum advantage. This research evaluates quantum optimization performance using Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), comparing them against classical HPC techniques such as simulated annealing and gradient descent optimization. Benchmarking was conducted using real-time optimization datasets via IBM Quantum processors (127-qubit systems) and NVIDIA A100-based HPC clusters. Results indicate that quantum systems reduced computational complexity and required 54% less time to reach near-optimal solutions in large combinatorial problems involving more than 10¹⁷ possible permutations. Classical systems exhibited latency bottlenecks and exponential time growth, while quantum execution achieved polynomial-time convergence. This study validates quantum supremacy in selective optimization workloads and provides a roadmap for industrial adoption.
Quantum supremacy refers to the point at which quantum computers outperform classical high-performance computing (HPC) systems in solving computations that are infeasible for conventional systems. Real-time optimization problems—such as traffic flow, financial modeling, logistics, portfolio balancing, cryptographic simulations, and supply chain design—require massive computation and near-instant decision-making, presenting a strong testbed for demonstrating true quantum advantage. This research evaluates quantum optimization performance using Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), comparing them against classical HPC techniques such as simulated annealing and gradient descent optimization. Benchmarking was conducted using real-time optimization datasets via IBM Quantum processors (127-qubit systems) and NVIDIA A100-based HPC clusters. Results indicate that quantum systems reduced computational complexity and required 54% less time to reach near-optimal solutions in large combinatorial problems involving more than 10¹⁷ possible permutations. Classical systems exhibited latency bottlenecks and exponential time growth, while quantum execution achieved polynomial-time convergence. This study validates quantum supremacy in selective optimization workloads and provides a roadmap for industrial adoption.
Portfolio optimization is a fundamental problem in quantitative finance that seeks to maximize risk-adjusted returns while managing investment risk. With the advent of quantum computing, there is growing interest in applying quantum algorithms to solve complex optimization problems. This paper presents a comprehensive comparative study of quantum and classical approaches to portfolio optimization, specifically comparing the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and classical mean-variance optimization. We evaluate these methods on both real market data and synthetic datasets with varying correlation structures. Our experimental results show that while classical methods currently outperform quantum approaches on near-term quantum devices in terms of solution quality and computational efficiency, quantum algorithms demonstrate conceptual viability and promise for future applications as quantum hardware improves. The classical method achieves optimal Sharpe ratios of 1.2-1.8, while QAOA and VQE achieve 80-95% and 75-90% of classical performance respectively. This work provides insights into the current state and future potential of quantum computing for financial optimization.
Optimization problems are widely encountered in real-world domains such as energy systems, financial markets, transportation networks, supply chain logistics, manufacturing processes, and e-commerce platforms. Traditional classical computing methods often struggle to solve large combinatorial optimization problems due to the exponential growth of the solution space. Quantum computing has emerged as a promising approach to address these challenges by leveraging quantum mechanical principles such as superposition and entanglement. However, current quantum hardware is limited by noise, restricted qubit availability, and scalability issues, making fully quantum solutions impractical for large-scale applications. To overcome these limitations, this research proposes a hybrid quantum–classical supercomputing framework for solving complex optimization problems across multiple domains. The framework integrates classical data processing and optimization techniques with quantum-inspired algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), to efficiently explore large solution spaces. Real-world datasets from energy systems, supply chain logistics, financial markets, transportation networks, manufacturing processes, and e-commerce platforms are used for experimental evaluation. The results demonstrate that the hybrid optimization approach can generate higher-quality solutions and improved optimization accuracy compared to traditional classical methods. These findings highlight the potential of hybrid quantum–classical systems as an effective and scalable solution for solving complex optimization problems in modern computational applications.
In this work, we study the application of quantum computing to complex engineering simulations and decisions. Using these quantum algorithms like Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Support Vector Machine (QSVM) and Grover’s Algorithm, we will optimize computational efficiency, accuracy and diversity of solutions in engineering. It was performed with a comparative analysis with classical algorithms based on performance metrics such as accuracy of solution, execution time, and optimality of the decision. Analysis of experimental results showed QAOA produces a 92.3% solution accuracy, higher than that (88.6%) of classical simulated annealing and (85.4%) genetic algorithms. Similarly, VQE achieved 27% reduction in execution time versus the classical equivalent and QSVM achieved 94.1% classification accuracy on engineering pattern recognition tasks. Grover’s Algorithm also resulted in a 3.7x faster search efficiency in decision oriented simulation. Finally, the study shows that quantum inspired models can perform efficiently on large scale and highly complex engineering problems. These findings provide a major leap in realizing the capability to harness quantum technologies for real time, scalable decision making in structural analysis, lifecycle management and intelligent system design. Such results make way for future developments of hybrid quantum classical computing frameworks that could be deployed in industry.
Quantum computing, leveraging quantum mechanics, promises to enhance optimization processes in artificial intelligence (AI) by addressing challenges faced by classical computing in solving complex problems. This study investigates the role of quantum computing in optimizing AI tasks, focusing on algorithms such as Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) for improved efficiency in optimization. A hybrid quantum-classical approach was used to evaluate the performance of quantum algorithms in optimization tasks, including combinatorial optimization and machine learning model tuning. QAOA and QA were applied to benchmark problems, such as Max-Cut and Traveling Salesman Problem (TSP). The study involved a comparative analysis between quantum algorithms and classical optimization techniques using performance metrics like solution quality, computational time, and scalability. Data were gathered through simulation on a quantum computing platform, and results were analyzed using statistical methods to assess improvements. The results showed that quantum computing significantly outperformed classical methods in solving large-scale optimization problems. QAOA demonstrated a 30% improvement in solution quality for Max-Cut problems, and QA showed a 25% faster convergence in TSP compared to classical algorithms. Quantum algorithms demonstrated scalability advantages as the problem size increased, with quantum methods achieving up to a 40% reduction in computational time for large instances. Furthermore, 70% of the tested cases showed significant improvements in solution accuracy, confirming the potential of quantum computing in real-world AI applications. Quantum computing, through QAOA and QA, offers substantial improvements in solving complex optimization problems in AI, presenting a transformative opportunity for the future of optimization algorithms.
Combinatorial optimization problems play a vital role in computer science and engineering, with applications in logistics, network design, finance, and resource allocation. However, many of these problems are NP-hard, making them computationally expensive to solve using classical optimization techniques, especially for large-scale instances. In recent years, quantum computing has emerged as a promising paradigm capable of addressing these limitations by leveraging quantum mechanical principles such as superposition and entanglement. This study investigates the application of quantum computing for solving combinatorial optimization problems, with a focus on algorithm design, complexity analysis, and real-world applicability. The research primarily employs the Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm to solve representative problems such as Max-Cut and the Knapsack problem. The performance of these algorithms is evaluated using key metrics including accuracy, execution time, and approximation ratio, and is compared with classical optimization techniques. The results demonstrate that QAOA is capable of generating near-optimal solutions, achieving accuracy levels of up to 93% for small problem instances. However, the study also highlights the challenges associated with current quantum systems, including noise, limited qubit availability, and increased execution time. Comparative analysis reveals that while classical methods remain more efficient for small-scale problems, quantum algorithms show significant potential for scalability and handling complex optimization tasks. The findings suggest that hybrid quantum-classical approaches offer a practical solution in the current Noisy Intermediate-Scale Quantum (NISQ) era. The study concludes that although quantum computing is still evolving, it holds strong potential for transforming combinatorial optimization in the future.
The increasing complexity of financial markets has heightened the demand for advanced computational methods capable of optimizing portfolios under multi-dimensional constraints involving risk, return, diversification, and liquidity. Traditional optimization frameworks, such as mean-variance analysis and heuristic meta-optimization, are limited by their scalability and sensitivity to non-convex, high-dimensional data structures. This study explores the application of quantum algorithms to portfolio optimization, demonstrating how Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, and Variational Quantum Eigensolver (VQE) can address computational bottlenecks inherent in classical approaches. Quantum computation offers the potential to encode complex financial systems as Hamiltonian functions, allowing parallel exploration of vast solution spaces through superposition and entanglement. This enables faster convergence toward optimal portfolio allocations while simultaneously accounting for multi-factor correlations and dynamic market conditions. The research further investigates the role of hybrid quantum–classical optimization pipelines, in which quantum solvers perform core optimization tasks while classical systems manage data preprocessing and constraint validation. Empirical modeling and simulation indicate that quantum-based approaches can outperform conventional methods in risk-adjusted return optimization, efficient frontier computation, and real-time portfolio rebalancing. By bridging the gap between quantum computing and financial engineering, this study contributes to the development of next-generation financial analytics systems, aligning with the broader goal of achieving more adaptive, transparent, and data-resilient financial decision-making.