Technological advancement is accelerating rapidly. Technology continues to evolve through diffusion and convergence, making the development of objective data-driven methodologies based on patents to compare temporal patterns of technology diffusion and convergence essential. This paper proposes a methodology for comparing technology diffusion and convergence patterns using patent citation indices and co-classification information. The proposed method applies patent citation data and co-classification information to a logistic diffusion-convergence model to measure the peak diffusion time and saturation threshold of a given technology. For empirical validation, 7,173 patent records from the field of quantum computing were analyzed. The experimental results indicated that the peak diffusion and convergence times were calculated as 11.58 and 17.98 years, respectively, whereas the diffusion and convergence saturation thresholds were determined to be 46.21 and 35.45 years, respectively. This implies that in quantum computing, technology diffusion reaches its peak earlier than convergence, whereas the saturation threshold is reached earlier for convergence than for diffusion. Through this empirical analysis, we demonstrated that applying the proposed methodology enables the identification and comparison of diffusion and convergence patterns across technologies. Consequently, R&D policymakers across various technological domains can leverage this methodology to obtain objective insights into the direction of technological advancement. Additionally, by distinguishing between fundamental scientific technologies and market-driven convergent technologies, policymakers can strategically design investment directions and prioritize R&D funding more effectively at different stages of technological development.