Quantum Machine Learning Models for Encrypted Big Data Processing in Cloud Computing

Abstract

The exponential growth of cloud-based data generation has intensified the demand for intelligent, secure, and computationally efficient mechanisms capable of processing encrypted big data without compromising privacy. Conventional machine learning approaches often require data decryption before analysis, thereby exposing sensitive information to potential security threats and increasing computational overhead. Recent advancements in quantum computing and quantum machine learning (QML) have introduced new opportunities for accelerating data analytics while preserving confidentiality through quantum-enhanced optimization, feature representation, and secure computation techniques. This paper investigates the integration of quantum machine learning models with encrypted big data processing frameworks deployed in cloud computing environments. The study explores the architectural design, computational workflow, quantum-enabled learning paradigms, encryption-aware data processing mechanisms, and performance optimization strategies suitable for large-scale cloud infrastructures. Furthermore, the paper discusses practical implementation challenges, scalability considerations, computational complexity, and future research directions associated with quantum-assisted secure analytics. The proposed framework aims to enhance prediction accuracy, reduce computational latency, strengthen data confidentiality, and improve resource utilization, thereby establishing a robust foundation for next-generation intelligent cloud computing systems capable of handling privacy-preserving big data analytics.