The exponential growth of cloud computing within the last decade has led to a severe consideration about the power requirement of the cloud data center. However, variations in the computing resource demands and fixed size of virtual machines (VMs) lead to a poor resource utilization, load imbalance, and excessive energy consumption within the data center. Dynamic VM consolidation effectively handles these problems, where VMs are placed on as few physical machines (PMs) as possible. Concurrently, VM allocation must be done strategically by considering various factors of the computing resources for minimal exploitation of them. The high frequency of VM consolidation may lead to the degradation of the system’s reliability by assigning VMs to unreliable PMs. A novel Quantum Machine learning driven Reliable Resource Allocation (QM-RRA) model is proposed to address these problems and enhance the data center’s reliability. We handle the problem by applying an Evolutionary Quantum C-Not Neural Network (QCNN) unit to forecast future resource usage at the cloud data center. In addition, Quantum Evolutionary Adaptive Algorithm (QEAA) is utilized to optimize qubit network weights. It offers energy saving by minimizing the number of active PMs, maximizing resource utilization, and enhancing the reliability of the overall data center. The QM-RRA model is evaluated by performing experiments on the Google Cluster Data set (GCD). The observed results are compared with the state-of-art methods and shows its supremacy in terms of different performance parameters like resource utilization, power consumption, and the number of active PMs. There is a significant reduction in power consumption and the number of active PMs up to 31.38%  and 44.93%, respectively. The enhancement in resource utilization is up to 25.61%, and overall system reliability is 63.35%.