Quantum-Enhanced AI for Enterprise Analytics using Cloud-Based Hybrid Computing Architectures
DOI:
https://doi.org/10.15662/IJEETR.2026.0804003Keywords:
Quantum Computing, Artificial Intelligence, Enterprise Analytics, Hybrid Computing, Cloud Computing, Quantum Machine Learning, Quantum Optimization, Business Intelligence, Quantum AI, Enterprise Decision Support, Hybrid Quantum-Classical Architecture, Cloud AnalyticsAbstract
Quantum computing is emerging as a potential technological foundation for advancing enterprise analytics by addressing selected computational problems that are difficult to solve efficiently using conventional computing alone. When combined with artificial intelligence and cloud computing, quantum technologies can contribute to a hybrid analytical environment in which classical and quantum processors work together according to the requirements of specific workloads. This development is particularly relevant to enterprises dealing with complex optimization, forecasting, risk analysis, portfolio management, supply-chain planning, fraud detection, and high-dimensional data. Cloud-based quantum computing makes quantum resources accessible without requiring organizations to own and maintain quantum hardware, while hybrid architectures allow classical cloud infrastructure to manage data preparation, machine-learning operations, orchestration, and post-processing. Quantum-enhanced AI can potentially improve selected computational processes through quantum machine-learning algorithms, quantum optimization, quantum-enhanced sampling, and hybrid variational methods. However, current quantum hardware remains constrained by noise, limited qubit capacity, error rates, and significant data-transfer and algorithm-design challenges. Therefore, practical enterprise adoption requires architectures that integrate quantum resources with established cloud AI and analytics platforms rather than treating quantum computers as standalone replacements for classical systems. This study examines a cloud-based hybrid computing architecture for quantum-enhanced enterprise analytics and proposes a methodology for evaluating computational performance, analytical accuracy, scalability, cost, security, and business value. The research emphasizes realistic near-term hybrid quantum-classical approaches and identifies the technological and organizational conditions required for responsible enterprise adoption.
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