With the rapid development of information technology, the continuous expansion of data scale poses higher challenges to data analysis algorithms. This paper proposes a Quantum Computation Optimization Algorithm (QCOA), specifically a quantum firefly algorithm, which leverages quantum superposition, entanglement, and rotation gates to accelerate convergence and avoid local optima in optimization tasks. The proposed QCOA is experimentally tested on a large-scale IMDB dataset with one billion records using a high-performance quantum simulation environment based on Qiskit and Python. Key parameters include 50 fireflies, 32 qubits, 1000 iterations, and a random neighborhood search mechanism. The algorithm's performance is evaluated against classical baselines including SVM, KNN, and GBDT using metrics such as false positive rate, accuracy, and processing time. Results show QCOA achieves the lowest false positive rate (2.7%), highest accuracy (97.3%), and the shortest processing time (90 seconds), outperforming all classical baselines. These findings validate the practical advantages of QCOA and quantum computing in large-scale data processing and optimization, offering a promising approach for future data-intensive applications.
With the rapid development of information technology, the continuous expansion of data scale poses higher challenges to data analysis algorithms. This paper proposes a Quantum Computation Optimization Algorithm (QCOA), specifically a quantum firefly algorithm, which leverages quantum superposition, entanglement, and rotation gates to accelerate convergence and avoid local optima in optimization tasks. The proposed QCOA is experimentally tested on a large-scale IMDB dataset with one billion records using a high-performance quantum simulation environment based on Qiskit and Python. Key parameters include 50 fireflies, 32 qubits, 1000 iterations, and a random neighborhood search mechanism. The algorithm's performance is evaluated against classical baselines including SVM, KNN, and GBDT using metrics such as false positive rate, accuracy, and processing time. Results show QCOA achieves the lowest false positive rate (2.7%), highest accuracy (97.3%), and the shortest processing time (90 seconds), outperforming all classical baselines. These findings validate the practical advantages of QCOA and quantum computing in large-scale data processing and optimization, offering a promising approach for future data-intensive applications.
The exponential growth of data across various domains has necessitated novel computational paradigms capable of processing and optimizing large-scale datasets efficiently. Quantum computing offers a promising solution by leveraging principles such as superposition, entanglement, and quantum parallelism. This paper reviews quantum algorithms applicable to data analysis and optimization, including quantum approximate optimization algorithms (QAOA), Grover’s search, and quantum machine learning techniques. We explore their potential advantages over classical approaches in solving combinatorial optimization problems, big data analytics, and high-dimensional pattern recognition. Graphical analyses illustrate computational efficiency and accuracy improvements of quantum algorithms on benchmark datasets. Challenges in scalability, error correction, and algorithmic implementation are discussed, along with potential future directions for integrating quantum computing into practical large-scale data applications
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.
In the noisy intermediate-scale quantum (NISQ) era, as the scale of existing quantum hardware continues to expand, the demand for effective methods to schedule quantum gates and minimize the number of operations has become increasingly urgent. To address this demand, the Quantum Firefly Algorithm (QFA) has been designed by incorporating quantum information into the traditional firefly algorithm. This integration enables fireflies to explore multiple positions simultaneously, thereby increasing search space coverage and utilizing quantum tunneling effects to escape local optima. Through wave function evolution and collapse mechanisms described by the Schrödinger equation, a balance between exploring new solutions and exploiting known solutions is achieved by the QFA. Additionally, random perturbation steps are incorporated into the algorithm to enhance search diversity and prevent the algorithm from being trapped in local optima. In quantum circuit scheduling problems, the QFA optimizes quantum gate operation sequences by evaluating the fitness of scheduling schemes, reducing circuit depth and movement operations, while improving parallelism. Experimental results demonstrate that, compared to traditional algorithms, the QFA reduces SWAP gates by an average of 44% and CNOT gates by an average of 16%. When compared to modern algorithms, it reduces SWAP gates by an average of 7% and CNOT gates by an average of 12%.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics
This paper investigates the application of quantum-inspired concepts to enhance the performance of random walk algorithms for optimization problems. Classical random walk algorithms are susceptible to getting trapped in local optima, limiting their efficacy in tackling complex and high-dimensional search spaces. To address this limitation, we propose a novel approach that incorporates principles of quantum superposition and tunneling into the random walk process. The algorithm leverages these concepts to enable more extensive exploration of the search space, thereby increasing the probability of converging towards the global optimum. We present a detailed formulation of the algorithm, outlining the key parameters and their influence on the optimization process. The theoretical analysis demonstrates the potential advantages of this quantum-inspired approach compared to traditional random walk methods. The algorithm's ability to escape local minima is evaluated through simulations, showcasing its effectiveness in a variety of benchmark optimization problems. The results highlight the promise of quantum-inspired techniques in improving the robustness and efficiency of random walk algorithms, offering a viable strategy for solving challenging optimization tasks.
As modern data environments continue to scale in volume and dimensionality, classical metaheuristics like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) struggle to balance convergence efficiency with solution quality. The challenge is further amplified in big data optimization, where stagnation in local minima and prohibitive computation time impede real-time applicability. Existing methods fail to simultaneously address the need for global search intelligence and computational scalability, especially under high-dimensional constraints and distributed workloads. To bridge this gap, we propose QIESO—a Quantum-Inspired Evolutionary Swarm Optimizer that integrates quantum amplitude encoding, probabilistic gate rotation, swarm coordination, and tunnelingbased mutation to escape local traps and accelerate convergence. Unlike traditional optimizers, QIESO leverages quantum principles simulated classically to maintain coherence across the search space while reducing evaluation overhead. Empirical validation across datasets up to 100 million records shows QIESO achieves a 51.7% reduction in runtime over GA and 42.7% faster convergence than PSO, with a relative error margin consistently under 2.1%. The method exhibits sublinear scalability in time complexity due to dimensionality-agnostic encoding. This work significantly advances scalable optimization for high-throughput environments, making it ideal for applications in autonomous systems, real-time analytics, and next-gen intelligent infrastructures.