• Priyadharshini R
• Raja P
• Sukran D
• Roselin Vinesha A S

Efficient resource allocation in cloud computing environments is critical to ensuring optimal system performance, cost-effectiveness, and service quality. Traditional and AI-based approaches often fall short are addressing the complexities of high-dimensional, real-time optimization problems inherent in dynamic cloud systems. This research introduces a Quantum-AI Hybrid Optimization Model that synergistically combines Long Short-Term Memory (LSTM) networks for demand forecasting with the Quantum Approximate Optimization Algorithm (QAOA) for optimal resource distribution. The AI module predicts short-term resource demands based on historical workload data, in contrast, the quantum module formulates the resource allocation problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solves it using QAOA. A decision controller integrates both outputs to determine the most efficient allocation strategy. The model was evaluated using Google Cluster Workload Traces in a simulated cloud environment. Results show that the proposed hybrid system achieved superior performance over traditional heuristics and AI-only models, improving resource utilization by up to 10%, reducing latency by 25%, and lowering operational costs by 30%. The study highlights the potential of quantum-classical hybrid systems in cloud computing and demonstrates their applicability in solving large-scale, time-sensitive optimization problems. Future work will focus on deploying the model on real quantum hardware and expanding its scalability and fault tolerance in production-scale cloud infrastructures.

Cloud Resource Allocation, Quantum Approximate Optimization Algorithm (QAOA), Hybrid AI-Quantum Model, LSTM Forecasting, Cloud Computing Optimization, Quantum Machine Learning.

"Quantum-AI Hybrid Optimization Model for Cloud Resource Allocation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.i630-i633, June-2025, Available :http://www.jetir.org/papers/JETIR2506879.pdf

"Quantum-AI Hybrid Optimization Model for Cloud Resource Allocation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppi630-i633, June-2025, Available at : http://www.jetir.org/papers/JETIR2506879.pdf