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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

A Quantum-Inspired Computational Architecture for Efficient High-Dimensional Data Processing

DOI https://doi.org/10.55640/ijidml-v03i10-02
Abdullah Khalid Al-Harbi 1 , Noor Fahad Al-Qahtani 1 ,
4 Department of Computer Science, Arabian Institute of Technology, Riyadh, Saudi Arabia
4 Department of Artificial Intelligence and Data Science, Gulf Research University, Jeddah, Saudi Arabia

Abstract

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.

Keywords

Quantum-inspired computing, high-dimensional data, machine learning

References

Bellman R. Adaptive Control Processes: A Guided Tour. Princeton University Press; 1961. p. 94-115.
Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature. 2017;549(7671):195-202.
Chia N-H, Gilyén A, Li T, Lin HH, Tang E, Wang C. Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning. J ACM. 2020;67(6):1-45.
Dunjko V, Briegel HJ. Machine learning & artificial intelligence in the quantum domain: A review of recent progress. Rep Prog Phys. 2018;81(7):074001.
Harrow AW, Hassidim A, Lloyd S. Quantum algorithm for linear systems of equations. Phys Rev Lett. 2009;103(15):150502.
Huang HY, Kueng R, Preskill J. Predicting many properties of a quantum system from very few measurements. Nat Phys. 2020;16(10):1050-1057.
Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Philos Trans R Soc A. 2016;374(2065):20150202.
Kerenidis I, Prakash A. Quantum recommendation systems. Proceedings of the 8th Innovations in Theoretical Computer Science Conference (ITCS). 2017;49:1-21.
Levine Y, Yakira D, Cohen N, Shashua A. Deep learning and quantum entanglement: Fundamental connections with implications to network design. Phys Rev Lett. 2019;122(6):065301.
Lloyd S, Mohseni M, Rebentrost P. Quantum principal component analysis. Nat Phys. 2014;10(9):631-633.
Lloyd S, Weedbrook C. Quantum generative adversarial learning. Phys Rev Lett. 2018;121(4):040502.
Rebentrost P, Mohseni M, Lloyd S. Quantum support vector machine for big data classification. Phys Rev Lett. 2014;113(13):130503.
Schuld M, Killoran N. Quantum machine learning in feature Hilbert spaces. Phys Rev Lett. 2019;122(4):040504.
Schuld M, Petruccione F. Supervised Learning with Quantum Computers. Springer; 2018. p. 55-80.
Stoudenmire EM, Schwab DJ. Supervised learning with tensor networks. Adv Neural Inf Process Syst. 2016;29:4799-4807.
Tang E. Quantum-inspired classical algorithms for principal component analysis and recommendation systems. Proceedings of the 51st Annual ACM Symposium on Theory of Computing (STOC). 2019:217-228.
Tenenbaum JB, de Silva V, Langford JC. A global geometric framework for nonlinear dimensionality reduction. Science. 2000;290(5500):2319-2323.
Wiebe N, Kapoor A, Svore KM. Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning. Quantum Inf Comput. 2015;15(3-4):0318-0358.

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