Quantum-assisted tracer dispersion in turbulent shear flow
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916920168546304 |
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| author | Ingelmann, Julia Schindler, Fabian Schumacher, Jörg |
| author_facet | Ingelmann, Julia Schindler, Fabian Schumacher, Jörg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-assisted tracer dispersion in turbulent shear flow Ingelmann, Julia Schindler, Fabian Schumacher, Jörg Fluid Dynamics Quantum Physics 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. |
| title | Quantum-assisted tracer dispersion in turbulent shear flow |
| topic | Fluid Dynamics Quantum Physics |
| url | https://arxiv.org/abs/2506.14586 |