Quantum Computing to Study Cloud Turbulence Properties

The analysis and investigation of the data obtained from Direct Numerical (DNS) simulation droplet dynamics in cloud turbulence is a complex time-consuming task when performaed on traditional computers. DNS generally have, high spatial resolution ≈ 1 mm require considerable space to store. It tedious find specific features this data, such as locating low vortex areas using machine learning algorithms. In research, we employ quantum computing examine analyze present supervised algorithm, namely, support vector (SVM) segregate regions investigate characteristics those regions. result show that use computers can accelerate entire process, mechanics tools, kernels circuits better manage nature than methods.