# A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

arXiv: https://arxiv.org/abs/2211.14619 · HTML: https://mlsanity.com/paper/2211.14619/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2022.

## Built on by (most cited first, 6 of 6)

- [Federated Learning for distribution skewed data using sample weights](https://mlsanity.com/paper/2401.02586/) (2024)
- [MAIDS: Malicious Agent Identification-based Data Security Model for Cloud Environments](https://mlsanity.com/paper/2412.14490/) (2024)
- [A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads Prediction](https://mlsanity.com/paper/2507.08317/) (2025)
- [A smart resource management mechanism with trust access control for cloud computing environment](https://mlsanity.com/paper/2212.05319/) (2022)
- [dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network](https://mlsanity.com/paper/2402.00874/) (2024)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)

## Abstract (arXiv)

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects of the Controlled-NOT (C-NOT) gate serve activation function at the hidden and output layers to adjust the qubit weights. In addition, a Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize qubit network weights. The accuracy of the EQNN prediction model is extensively evaluated and compared with seven state-of-the-art methods using eight real world benchmark datasets of three different categories. Experimental results reveal that the use of the quantum approach to evolutionary neural network substantially improves the prediction accuracy up to 91.6% over the existing approaches.