Quantum-Assisted AI for Speeding up Large-Scale Data Processing

Abstract

The emerging intersection of quantum computing and large scale data processing is generally referred to an early research field named as Quantum-Assisted Artificial Intelligence (QAAI). The goal of this research is to integrate quantum algorithms with commonly used AI models, so as to tackle computational bottlenecks in dealing with large amounts of data. Some well known quantum algorithms, for the classification problem for example one can mention Quantum Support Vector Machines (QSVM) and Quantum Nearest Neighbor Methods can offer improvement in the classification accuracy at the cost of having a substantially smaller computational complexity. This is achieved with the proposed methodology of using variational quantum circuits for optimizing learning parameters and hybrid quantum classical architectures for efficient training of the model. Experiment results show that the complexity of datasets could be accelerated by 30–40% in training speed while comparable accuracy could be retained compared with its traditional AI counterpart. Furthermore, randomized measurement protocol is implemented to improve the system’s performance in dealing with high dimensional data with low resource overhead. In addition, the integration of QAAI models includes greater resilience in the presence of noisy input data. This enabled us to develop providing more effective solutions for big data analytics, financial forecasting, and real time decision making system. This, however, still remains to be done, as integrating quantum–enhanced data encoding strategies for running quantum algorithms and exploring new frameworks for quantum assisted learning can make QAAI solutions an effective way to tackle the aforementioned scaling issues that all classical AI systems have.