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Hybrid Quantum-Classical Architectures for Scalable Artificial Intelligence

Wahab Abdul, Abdullah, Daniyal

Published May 26, 2026• 0 citations
Publisher

Abstract

Artificial Intelligence systems are rapidly increasing in complexity and computational demand. This paper explores the integration of quantum computing withclassical AI systems through hybrid architectures. The study discusses optimization, scalability, applications, limitations, and future implications of Quantum AI.
DOI
10.5281/zenodo.20393926

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Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
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