Quantum-assisted tracer dispersion in turbulent shear flow

TL;DR

This paper introduces a quantum-assisted algorithm to generate synthetic Lagrangian tracer particle tracks in turbulent shear flow, leveraging quantum parallelism to model joint velocity distributions.

Contribution

It presents a novel hybrid quantum-classical method for sampling turbulent velocity fields, validated on classical models and a real quantum device, advancing quantum applications in turbulence modeling.

Findings

Successfully generated joint velocity PDFs using quantum circuits.

Validated quantum approach against classical stochastic models.

Demonstrated feasibility on a 20-qubit quantum device.

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.