While full-scale quantum computers remain years away from practical deployment, quantum-inspired algorithms executing on classical hardware have emerged as a promising approach for solving computationally intractable problems in large-scale data analytics. This paper presents a comprehensive framework for applying quantum-inspired tensor network algorithms to complex data problems including high-dimensional clustering, feature selection, and recommendation system matrix factorization. We introduce the Quantum-Inspired Algorithm Library (QIAL), a scalable implementation that seamlessly integrates with Apache Spark and other big data frameworks. Through extensive experimentation on datasets ranging from 1 million to$\mathbf{1 0 0}$million records, we demonstrate that our approach achieves 2.6x speedup compared to classical algorithms while maintaining superior accuracy (average improvement of 2.1% across five benchmark datasets) and reduced memory footprint (up to$\mathbf{5 2 \%}$reduction). Our tensor network-based clustering algorithm shows particular promise for NP-hard combinatorial optimization problems that are currently intractable for conventional big data systems. The proposed framework opens new avenues for practical quantum-inspired computing in enterprise-scale data analytics applications.
While full-scale quantum computers remain years away from practical deployment, quantum-inspired algorithms executing on classical hardware have emerged as a promising approach for solving computationally intractable problems in large-scale data analytics. This paper presents a comprehensive framework for applying quantum-inspired tensor network algorithms to complex data problems including high-dimensional clustering, feature selection, and recommendation system matrix factorization. We introduce the Quantum-Inspired Algorithm Library (QIAL), a scalable implementation that seamlessly integrates with Apache Spark and other big data frameworks. Through extensive experimentation on datasets ranging from 1 million to100million records, we demonstrate that our approach achieves 2.6x speedup compared to classical algorithms while maintaining superior accuracy (average improvement of 2.1% across five benchmark datasets) and reduced memory footprint (up to52%reduction). Our tensor network-based clustering algorithm shows particular promise for NP-hard combinatorial optimization problems that are currently intractable for conventional big data systems. The proposed framework opens new avenues for practical quantum-inspired computing in enterprise-scale data analytics 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
Modern businesses are facing more and more challenges in terms of Big Data warehousing and real-time data analytics due to the sheer volume of growing data. Traditional optimization techniques are increasingly ineffective when working with high-dimensional datasets, thus creating delays in resource allocation and query processing. Since there is a growing demand for faster solutions for data analytics, it is necessary to find new methods for solving optimization issues. In this respect, new emerging quantum-inspired computing techniques allow solving complex optimization problems by employing the principles of quantum mechanics—superposition and probability exploration. To enhance the performance of systems used for predictive analytics and data warehousing, a quantum-inspired optimization framework is proposed. This approach involves incorporating quantum-inspired techniques such as simulations of quantum annealing processes into frameworks used for distributed processing, thus resulting in the improvement of the areas of data indexing, query execution paths and load balancing of computers. The data processing machines are improved due to the advancements in feature selection and attainment of results in a faster way. Evaluation of the method on the big datasets showed that the query performance, and the accuracy of prediction is higher than that demonstrated by conventional techniques, and the architecture is efficient and can be scaled for processing the workloads for real-time analytics.
Ultra-high-dimensional data Ultra-high-dimensional Bigdata In the context of information, ultra-high-dimensional data refers to the explosive growth of data in wide-ranging fields like healthcare, finance, and scientific research and the increasing problem associated with being able to deal with such data using traditional big data analytics. Scalability of traditional machine learning and statistical methods is limited by bottlenecks and high computational costs in addition to lower accuracy when processing such datasets. In such cases, quantum computing with built-in propensity to leverage on the notion of superposition and entanglement presents a transformational willpower paradigm to quicken analytics. In this paper, a concept of a Quantum-Assisted Big Data Analytics (QABDA) framework was proposed that combines quantum algorithms with classical big data pipelines and thus allows scaling efficient feature selection, dimensionality reduction, and pattern recognition over ultra-high-dimensional settings. The suggested strategy is based on quantum-enhanced methods on linear algebra, hybrid quantum-classical optimization, and variational algorithms to increase computational efficiency and correctness. Compared to classical baseline, results show better performance regarding the lowering execution time, scalability, and accuracy using synthetic and real-world data. In addition, the workflow demonstrates the flexibility of hybrid quantum-classical workflows in near-term noisy intermediate-scale quantum (NISQ) devices, which leads to the possibility of applying quantum-enhanced analytics in big data ecosystems in practice.
The need for efficient and scalable big data analytics frameworks is driven by the exponential growth of data in areas such as social media, the internet of things (IoT), finance, healthcare, and scientific simulations. While powerful, conventional HPC and distributed processing systems are illequipped to optimize and discover patterns in complicated, large datasets. The use of superposition and entanglement in quantum computing alters optimization and data processing. This research develops a hybrid quantum-classical paradigm for big data analytics by combining machine learning with quantum-assisted optimization. By integrating quantum algorithms such as variational optimization and quantum search with conventional distributed storage and preprocessing systems, we can enhance clustering, feature selection, and predictive modeling. Effective use of quantum-classical interfaces has been demonstrated for data encoding, parameter tuning, and iterative optimization. Processing is simplified, convergence is accelerated, and analytical accuracy for highdimensional datasets is enhanced by the suggested architecture. According to performance simulations, classical methods might not be as effective as combinatorial optimization and large-scale pattern recognition. According to this research, next-gen data-driven businesses can benefit greatly from quantum-enhanced analytics’ scalable, intelligent, and quick optimization mechanisms, which can completely transform big data processing.
High-dimensional data processing presents substantial computational challenges because increasing feature dimensionality often leads to higher memory requirements, complex similarity calculations, redundant information, and increased learning costs. Conventional dimensionality-reduction and machine-learning approaches remain effective, but their scalability can become constrained when datasets contain large numbers of correlated or heterogeneous attributes. This paper develops a conceptual quantum-inspired computational architecture for efficient high-dimensional data processing by integrating principles associated with quantum machine learning, dimensionality reduction, low-rank computation, tensor representations, and quantum-inspired classical algorithms. The proposed architecture is organized around five functional stages: data encoding and normalization, structure-aware dimensionality reduction, quantum-inspired feature representation, efficient learning, and adaptive output analysis. Principal component analysis provides a theoretical foundation for identifying informative directions while reducing redundant dimensions (Jolliffe, 2016). Quantum machine-learning studies further motivate the use of Hilbert-space representations, quantum-inspired optimization, and compact computational representations (Biamonte et al., 2017; Schuld and Killoran, 2019). The architecture is designed as a hybrid conceptual framework rather than a claim of universal quantum advantage. Its principal contribution is the systematic integration of quantum-inspired computational mechanisms with established classical data-processing methods. The analysis indicates that such an architecture can potentially reduce computational redundancy, improve representation efficiency, and provide a scalable pathway for high-dimensional classification, recommendation, and predictive analytics. Limitations concerning data encoding, computational overhead, hardware dependence, and the distinction between genuine quantum advantage and classical quantum-inspired methods are also examined.
High-dimensional medical datasets pose a persistent challenge for artificial intelligence because traditional classification algorithms often incur escalating computational costs and reduced predictive accuracy. As healthcare systems generate increasingly complex clinical records, imaging outputs, and genomic profiles, scalable analytic methods that balance precision and efficiency are critical. This study proposes a Quantum-Inspired Optimization (QIO) framework for efficient and accurate classification of high-dimensional healthcare data. Leveraging the exploratory power of variational quantum algorithms, specifically techniques analogous to the Quantum Approximate Optimization Algorithm, the framework integrates quantum-style search strategies with classical computation to achieve global optimization and numerical stability. Publicly available medical datasets with hundreds of features were used to evaluate the approach. Classification models were trained and tested across varying feature dimensionalities, and performance was assessed using accuracy, runtime, and scalability metrics. Empirical results demonstrate that QIO achieves up to 95.4% classification accuracy and reduces computational time by 40% compared with state-of-the-art classical baselines. The method demonstrates stable convergence and clear decision boundaries even as feature dimensionality grows, highlighting its resilience to the curse of dimensionality. These results indicate that QIO can enable fast and reliable healthcare analytics in data-rich clinical environments. Future research may examine domain-specific adaptations, real-time deployment, and integration with emerging quantum hardware to enhance the impact of quantum-inspired artificial intelligence further.