Designing a resilient cloud network fulfilled by quantum machine learning

Next-generation digital services require resilient, energy-conscious cloud networks, but current optimization techniques are unable to quickly reconfigure infrastructures when a failure occurs. To address real-time service migration, this paper presents a quantum machine learning (QML) architecture that concurrently maximize quality of service (QoS) and minimizes migration cost while taking capacity, energy, and jitter restrictions into account. Thousands of migration methods are evaluated in parallel using a parameterized quantum neural network to solve the model. In comparison to the genetic algorithm, the QML optimizer reduces peak CPU load by 45%, while maintaining contractual QoS during cyberattacks, according to an experiment conducted on a real case study. The quantum solution offers noticeably smoother resource use, according to several assessments. These results establish QML as a promising facilitator for responsive cloud resilience by proving that quantum search may unleash fault-tolerant reconfiguration that is not possible with classical methodologies. The deployment is limited to medium-sized networks due to the size and noise of current quantum hardware; however, implementing new error-mitigation strategies provides viable routes to commercial use. This study establishes a research agenda for scalable quantum optimization in resilient networks in digital infrastructures by combining quantum computing with cloud-network engineering.