This paper examines quantum algorithms and their computational complexity through a unified framework combining mathematical modeling, system architecture, and empirical evaluation. Key complexity measures circuit depth, gate count, and query complexity are analyzed under NISQ constraints. A hybrid quantum classical optimization framework is introduced to improve efficiency and stability. Results show the full model achieves 94.2% accuracy with baseline runtime, while removing optimization lowers accuracy to 85.6% and increases runtime by 30%. Reducing qubits decreases cost but drops accuracy to 78.3%, and disabling error mitigation causes unstable performance at 70.1%. Comparative analysis indicates strong advantages of quantum algorithms for structured problems, especially in scalability and asymptotic complexity. However, performance remains sensitive to noise, limited qubits, and circuit depth, emphasizing the need for hardware–algorithm co-design.
Cloud Computing (CC) is the cornerstone of modern information technology that provides scalable, flexible, and cost-efficient services across diverse applications. Dynamic workloads and heterogeneous infrastructure face some difficulties in effective load balancing and resource provisioning which results in resource underutilization, overload and response time increases. This paper presents a comprehensive and comparative analysis of current methods addressing these issues. It also presents active resource provisioning frameworks, namely: probabilistic load balancing models, Machine Learning (ML)-based, Deep Learning (DL)-based, workload prediction techniques, genetic algorithms, Reinforcement Learning (RL) strategies, and hybrid meta-heuristic methods. Each method is analyzed in terms of methodology, advantages, limitations, and performance metrics, therefore providing an insight of their applicability in dynamic and large-scale cloud environments. A taxonomy architecture is presented to categorize the systematic comparison and research gaps. The comparative evaluation segment demonstrates enhancement in throughput, resource utilization, and cost efficiency, while also identifying limitations such as computational overhead and scalability constraints. The survey concludes by highlighting the necessity for intelligent, adaptive, and energy-aware solutions to confirm resilient and efficient cloud infrastructures.
Prasanna Mandala, S. Chandre· 2026 5th International Confe...· 0 citations