Spectral quantum algorithm for passive scalar transport in shear flows

TL;DR

This paper introduces a spectral quantum algorithm for simulating scalar mixing in fluid flows, leveraging quantum computing to solve the advection-diffusion equation with potential advantages in complex, multi-dimensional scenarios.

Contribution

It develops a quantum spectral method for fluid scalar mixing, including operator decompositions and circuit construction for arbitrary velocity profiles in multiple dimensions.

Findings

Validated with statevector simulations of classical flows

Compared quantum simulation results with real quantum hardware

Demonstrated scalability with grid points and polynomial order

Abstract

The mixing of scalar substances in fluid flows by stirring and diffusion is ubiquitous in natural flows, chemical engineering, and microfluidic drug delivery. Here, we present a spectral quantum algorithm for scalar mixing by solving the advection-diffusion equation in a quantum computational fluid dynamics framework. The exact gate decompositions of the advection and diffusion operators in spectral space are derived. For all but the simplest one-dimensional flows, these operators do not commute. Therefore, we use operator splitting to construct quantum circuits capable of simulating arbitrary polynomial velocity profiles in multiple dimensions, such as the Blasius profile of a laminar boundary layer. Periodic, Neumann, and Dirichlet boundary conditions can be imposed with the appropriate quantum spectral transform. We evaluate the approach in statevector simulations of a Couette flow, plane Poiseuille flow, and a polynomial Blasius profile approximation. For an advection-diffusion problem in one dimension, we compare the time evolution of an ideal quantum simulation with those of real quantum computers with superconducting and trapped-ion qubits. The required number of two-qubit gates grows with the logarithm of the number of grid points raised to one higher power than the order of the polynomial velocity profile.