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Enhancing Data Compatibility Through Quantum Computing and AI: A Hybridized Strategy

Year: 2025

Type: article

Abstract: The exponential increase in data generation across industries necessitates innovative strategies for ensuring seamless integration and interoperability. Conventional computing techniques struggle to cope with the complexities of heterogeneous data formats. This study introduces a hybrid methodology that synergizes Quantum Computing and Artificial Intelligence (AI) to streamline data compatibility. The proposed system employs Quantum Machine Learning (QML) for accelerated data processing and AI-driven adaptive algorithms for precise transformations, reducing computational overhead while enhancing accuracy. The results demonstrate improved efficiency in large-scale data integration scenarios. Key Words: Quantum Computing, Artificial Intelligence, Data Compatibility, Quantum Machine Learning, Interoperability

Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

DOI: 10.55041/ijsrem42474

Author: Devasane Mallesham

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4408497628

FWCI: 0.427

Cites:

Cited by: 1

Related to: 10