International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Bridging the Gap: A Critical Analysis of Quantum Computational Scalability in Big Data Analytics

Author(s) Ms. KM RASHMI SINGH, Dr. Vipin Saini, Dr. Kamal Kant Verma
Country India
Abstract Abstract
As the volume of global data approaches the yottabyte scale, classical computational architectures are encountering insurmountable bottlenecks in processing efficiency. Quantum Computing (QC) presents a theoretical solution by offering exponential speedups for processing high-dimensional, complex datasets. However, a significant disparity remains between these algorithmic potentials and the current state of physical hardware. This paper investigates the "scalability tension" by evaluating primary Quantum Machine Learning (QML) algorithms—specifically Quantum Principal Component Analysis (QPCA), Quantum Support Vector Machines (QSVM), and Variational Quantum Eigensolvers (VQE)—against the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. Utilizing the Resource-Utility Framework (RUF), we analyze error rates and qubit decoherence through simulations. Our findings reveal a "fidelity collapse" in QSVM for datasets exceeding 50 dimensions, where output becomes indistinguishable from white noise due to gate-depth requirements exceeding coherence times. We conclude that while theoretical speedups are robust, practical deployment is currently hindered by the overhead of Quantum Error Correction (QEC) and the "data loading problem." We propose hybrid quantum-classical frameworks as a necessary intermediary step toward achieving practical quantum advantage.
Keywords Quantum Computing, Big Data Analytics, Quantum Machine Learning, NISQ Constraints, Scalability, Quantum Error Correction, Hybrid Algorithms
Field Computer Applications
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-23
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.66864
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E-ISSN 2582-2160
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