Quantum-Accelerated Cloud BI Query Optimization

Abstract

As the cloud BI platforms (business intelligence) are rapidly evolving, the need to process queries more efficiently, especially when handling large and complex data has also increased. The classic optimization approaches can be scalability issues and may lead to increased latency and resources consumption. This work's idea on cloud BI is the hybrid model using quantum computing algorithms related to classical query execution pipelines to make it more efficient in decision making and improve the performance of the cloud query execution. The proposed framework is quantum inspired search query execution plan evaluation and quantum inspired parallelism searching which can search out the cost effective query execution plan construction for evaluation in a short time interval and is transparent in preserving the accuracy. It consists of three major components: Quantum Query Analyser, which is used to convert queries from complex SQL to representations that can be executed by the quantum algorithms; Hybrid Optimisation Engine which uses quantum algorithms and classical heuristics to suggest on how to optimise the query; Adaptive Resource Manager which dynamically allocates cloud resources based on the output from the optimisation step. A benchmark of experiments on standard data sets demonstrates that the query throughput can be improved by up to 40%–55% with reduced memory and CPU use with the quantum-accelerated approach when compared to state-of-the-art classical optimizers. It can also be configured as a multi-tenant cloud setup, ensuring security and scalability with minimal interruptions between different workloads. These outcomes demonstrate that the algorithms used in quantum computing can also be employed with current cloud based BI systems and suggests that quantum computing may be able to become a useful means to more quickly and efficiently perform analytics in Enterprise systems.