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Arxiv
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A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads Prediction
- Researchers propose a Comprehensively Adaptive Architectural Optimization-based Variable Quantum Neural Network (CA-QNN) model for accurate cloud workload prediction and resource reservation.
- The CA-QNN model integrates quantum computing principles with structural and parametric learning to address challenges faced by traditional neural networks and deep learning models in handling dynamic cloud workloads.
- Workload data is converted into qubits and processed through qubit neurons with Controlled NOT-gated activation functions to enhance pattern recognition.
- The CA-QNN model outperforms existing methods, achieving significant reductions in prediction errors up to 93.40% and 91.27% when evaluated on heterogeneous cloud workload datasets.
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