Quantum-assisted tracer dispersion in turbulent shear flow

• URL: http://arxiv.org/abs/2506.14586v1
• Date: Tue, 17 Jun 2025 14:43:54 GMT
• Title: Quantum-assisted tracer dispersion in turbulent shear flow
• Authors: Julia Ingelmann, Fabian Schindler, Jörg Schumacher,
• Abstract summary: We present a quantum-assisted generative algorithm for synthetic tracks of Lagrangian tracer particles in a turbulent shear flow.<n>The generation of the joint shear probability density function is also tested on a real quantum device, the 20-qubit IQM Resonance quantum computing platform for cases of up to 10 qubits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a quantum-assisted generative algorithm for synthetic tracks of Lagrangian tracer particles in a turbulent shear flow. The parallelism and sampling properties of quantum algorithms are used to build and optimize a parametric quantum circuit, which generates a quantum state that corresponds to the joint probability density function of the classical turbulent velocity components, p(u_1^{\prime}, u_2^{\prime}, u_3^{\prime}). Velocity samples are drawn by one-shot measurements on the quantum circuit. The hybrid quantum-classical algorithm is validated with two classical methods, a standard stochastic Lagrangian model and a classical sampling scheme in the form of a Markov-chain Monte Carlo approach. We consider a homogeneous turbulent shear flow with a constant shear rate S as a proof of concept for which the velocity fluctuations are Gaussian. The generation of the joint probability density function is also tested on a real quantum device, the 20-qubit IQM Resonance quantum computing platform for cases of up to 10 qubits. Our study paves the way to applications of Lagrangian small-scale parameterizations of turbulent transport in complex turbulent flows by quantum computers.

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