Analysis of Optimization Task Running Results on D-Wave and IBM Quantum Computers

This paper explores the transformative capabilities of quantum computers in optimizing complex tasks. Specifically, it examines the runtime efficiency of a Python program performing k-means clustering on two quantum computer variants: D-Wave annealing and IBM universal. The primary objective is to evaluate their effectiveness in light of their distinct operational principles. Quantum computing offers unparalleled potential for tackling computationally demanding tasks, marking a substantial leap beyond conventional Neumann-based architectures. By scrutinizing the runtime performance of k-means clustering on these quantum platforms, I aim to discern their comparative advantages and limitations. This investigation sheds light on the practical implications of leveraging different types of quantum computers for optimization tasks, informing future advancements in quantum computing research and application development.