arXiv subjects

Kazumasa Ueno

Publications and source records attributed to Kazumasa Ueno.

4 recordsLinked to original sources

arXiv2026

Quantum algorithm for the collision-coalescence of cloud droplets

Quantum computing may help reduce computational costs of simulating large and nonlinear systems, but research into the use of quantum computers in atmospheric and oceanic sciences is still at an early stage. This study explores the use of quantum computing for calculating the collision-coalescence process that dominates the size growth of liquid particles in cloud microphysics. Inspired by the quantum algorithms developed in the field of financial engineering, we propose a new algorithm based on a master equation that describes the time evolution of the droplet mass distribution. Our algorithm uses the quantum amplitudes to encode the probability distribution of droplet mass and calculates the expected number of droplets via the quantum amplitude estimation. The key contribution is an encoding strategy that maps the multivariate collision-coalescence problem onto a quantum computation by recording only the transition history rather than the full mass distributions at every time step. This approach reduces the per-step qubit cost from $O(\sqrt{N})$ to $O(\log N)$, where $N$ is the number of mass bins. Our resource analysis shows that the number of T gates (T-count) scales as $\widetilde{O}(N^2)$ per mass bin in $N$, for a fixed number of time steps and a given target accuracy, where the $\widetilde{O}$ notation suppresses polylogarithmic factors in $N$. This is a substantial improvement over classical master-equation methods, whose cost grows super-polynomially with $N$ as the number of possible states increases. Our results suggest that the collision-coalescence process is one of the promising targets of quantum computing in the field of atmospheric science.

quant-ph↗

arXiv2026

Explicit Quantum Circuit Simulation of Nonlinear 1-Dimensional Fluid with Carleman-linearized Boltzmann Method

Quantum computation of fluid dynamics has attracted growing attention as a key application of fault-tolerant quantum computers anticipated in the coming decade, with lattice Boltzmann methods emerging as a particularly promising approach. Explicit and efficient elementary-gate-level circuit simulations, however, have so far been demonstrated only in the linear case. Here we include the leading nonlinearity through second-order Carleman linearization of the one-dimensional Boltzmann equation, and demonstrate, via explicit quantum-circuit simulation, the preparation of the final-time state using a Taylor-expansion-based ODE solver based on the quantum singular value transformation. With this construction, we analyze the gate and qubit complexities, which scale logarithmically with the grid size, the nonlinearity captured by the higher-order Carleman linearization, and the practical utility of higher-order expansions in the Taylor ODE solver. The construction provides a concrete baseline for computational cost reduction and further developments such as extensions to higher dimensions, complex geometries, and the extraction of physical quantities, towards industrially useful quantum CFD.

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arXiv2026

A Demonstration of Quantum Circuit Implementation for Obstacle Flow Using Carleman-Linearized Lattice Boltzmann Method

Fluid simulations, especially at high Reynolds numbers, are computationally expensive on classical computers, making them promising application targets for quantum computing. Recent studies have combined the lattice Boltzmann method (LBM) with Carleman linearization to design quantum algorithms for computational fluid dynamics (CFD). However, practical quantum-circuit implementations of these algorithms that incorporate non-periodic boundary conditions have not been fully explored. In this work, we implement a quantum algorithm for two-dimensional linearized fluid flow around an obstacle, using block-encoding of the linear-system matrix and quantum singular value transformation (QSVT) to solve it. Inflow, outflow, and no-slip boundary conditions are formulated as sparse matrix operations and efficiently embedded into quantum circuits using index-value encoding. We demonstrate logarithmic scaling of the required numbers of qubits and gates with respect to the number of lattice points, suggesting the potential feasibility of quantum-computational fluid dynamics simulations.

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arXiv2024

Quantum Algorithm for a Stochastic Multicloud Model

Quantum computers have attracted much attention in recent years. This is because the development of the actual quantum machine is accelerating. Research on how to use quantum computers is active in the fields such as quantum chemistry and machine learning, where vast amounts of computation are required. However, in weather and climate simulations, less research has been done despite similar computational demands. In this study, a quantum computing algorithm is applied to a problem of the atmospheric science. The effectiveness of the proposed algorithm is evaluated using a quantum simulator. The results show that it can achieve the same simulations as a conventional algorithm designed for classical computers. More specifically, the stochastically fluctuating behavior of a multi-cloud model was obtained using classical Monte Carlo method, and comparable results are also achieved by utilizing probabilistic outputs of computed quantum states. Our results show that quantum computers have a potential to be useful for the atmospheric and oceanic science, in which stochasticity is widely inherent.

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