ABSTRACT
Despite ongoing advancements, certain complex computational tasks still face challenges in scalability and performance within the existing cloud computing paradigm. This study investigates the integration of Quantum Monte Carlo (QMC) and quantum machine learning (QML) methodologies into cloud architectures. Quantum Monte Carlo, a probabilistic methodology, leverages quantum principles to effectively and precisely address intricate systems. Quantum machine learning (QML) leverages principles from quantum physics to enhance the computational efficiency of machine learning algorithms, leading to substantial reductions in processing time and enhanced predictive accuracy. By integrating these quantum algorithms into cloud systems, we are able to demonstrate enhanced scalability and resilient performance, even when subjected to substantial workloads. In order to address the existing limitations of conventional cloud systems and pave the path for future advancements in the integration of quantum computing with cloud technologies, a framework known as quantum cloud computing was proposed. Initial trials demonstrate potential, instilling optimism that quantum cloud computing could provide a novel epoch of expeditious digital metamorphosis and enhanced computational capacities spanning many domains.
