Quantum Counting in a Distributed System
Abstract
This project delves into the implementation of the quantum counting algorithm within a distributed system, focusing on its efficacy in handling extensive datasets. The primary objectives revolve around reducing both execution time and qubit usage, critical factors in quantum algorithm performance. Experimental testing reveals that the Quantum Counting (QC) algorithm not only achieves an 85.0% reduction in execution time but also reduces qubit usage by 12.5% compared to Grover's algorithm. This reduction in qubit demand presents an opportunity for enhanced scalability in quantum algorithms, enabling their application to more extensive and intricate problem domains. The significance of decreasing qubit requirements extends beyond scalability challenges; it addresses the technical complexities of maintaining and controlling a large number of qubits. By optimizing qubit usage for specific computations, this approach simplifies physical implementation and lowers technological barriers in achieving practical quantum computers. Moreover, the judicious use of limited quantum resources, such as qubits, gate fidelities, and coherence times, allows for more efficient utilization within the constraints of existing quantum hardware. While the experiments showcased promising outcomes, there remain areas for improvement. Expanding the dataset volume and range to larger extents would necessitate computational systems with increased memory and processing capacity. Furthermore, conducting a comprehensive comparative analysis with other algorithms stands as an essential avenue for future exploration, offering insights into potential advancements and areas for further research.
Subject Area
Computer science|Computer Engineering|Information science
Recommended Citation
Du, Zefan, "Quantum Counting in a Distributed System" (2023). ETD Collection for Fordham University. AAI30820448.
https://research.library.fordham.edu/dissertations/AAI30820448
