9:15 AM - 9:30 AM
[AAS08-02] Application of Quantum Computing for Collision-Coalescence Process of Cloud Droplets
★Invited Papers
Keywords:Quantum Computing, Cloud Collision-Coalescence Process, Master Equation, Stochastic Process
Quantum computers are based on quantum mechanics and are expected to enable vast computations that are infeasible for classical computers. A key aspect of quantum computing is the efficient utilization of quantum properties such as superposition, interference, and entanglement. However, to effectively use quantum computers, it is necessary to address challenges related to linearity and readout. One promising approach to overcoming the linearity issue is solving the time evolution of a distribution function. This approach is viable because, even if the target process itself is nonlinear, its distribution evolution can be described linearly. Furthermore, the quantities of interest in computation are not the distribution itself, but rather statistical measures such as expectations and variances in many cases. Since only a small number of variables need to be read out to extract useful information, this approach also provides an efficient way to address the readout problem.
In this study, we propose a quantum algorithm for solving the master equation that describes the evolution of the cloud droplet collision-coalescence process. When considering a large variety of cloud droplets, the number of possible states becomes enormous and direct solutions of the master equation are computationally infeasible. Alfonso et al. (2015) managed to make computations feasible by leveraging the fact that only a limited number of states are realized in practice, but their approach was restricted to a maximum of 40 cloud droplet types. In this work, we develop a quantum algorithm, implement it on the Qiskit Aer simulator, and estimate its computational cost. Our results show that quantum computation provides outcomes equivalent to those obtained through direct classical computation. Additionally, while the computational cost of classical methods increases exponentially with the number of cloud droplet types, the quantum computation exhibits only a quadratic growth. These results highlight the potential of quantum computing for computing distribution functions in atmospheric science, demonstrating a promising application in the field.
In this study, we propose a quantum algorithm for solving the master equation that describes the evolution of the cloud droplet collision-coalescence process. When considering a large variety of cloud droplets, the number of possible states becomes enormous and direct solutions of the master equation are computationally infeasible. Alfonso et al. (2015) managed to make computations feasible by leveraging the fact that only a limited number of states are realized in practice, but their approach was restricted to a maximum of 40 cloud droplet types. In this work, we develop a quantum algorithm, implement it on the Qiskit Aer simulator, and estimate its computational cost. Our results show that quantum computation provides outcomes equivalent to those obtained through direct classical computation. Additionally, while the computational cost of classical methods increases exponentially with the number of cloud droplet types, the quantum computation exhibits only a quadratic growth. These results highlight the potential of quantum computing for computing distribution functions in atmospheric science, demonstrating a promising application in the field.