Quantum Fourier Networks for Solving Parametric PDEs

TL;DR

This paper introduces quantum Fourier neural networks inspired by classical FNOs for solving parametric PDEs, achieving potentially exponential speedups with comparable accuracy, and demonstrating applicability to image classification.

Contribution

It proposes quantum algorithms for PDE solving based on Fourier neural operators, achieving logarithmic time complexity and showing competitive performance with classical methods.

Findings

Quantum FNOs are comparable to classical FNOs in PDE tasks.

Quantum algorithms perform similarly to classical CNNs in image classification.

Proposed quantum methods have potential for exponential speedup in evaluations.

Abstract

Many real-world problems, like modelling environment dynamics, physical processes, time series etc., involve solving Partial Differential Equations (PDEs) parameterised by problem-specific conditions. Recently, a deep learning architecture called Fourier Neural Operator (FNO) proved to be capable of learning solutions of given PDE families for any initial conditions as input. However, it results in a time complexity linear in the number of evaluations of the PDEs while testing. Given the advancements in quantum hardware and the recent results in quantum machine learning methods, we exploit the running efficiency offered by these and propose quantum algorithms inspired by the classical FNO, which result in time complexity logarithmic in the number of evaluations and are, therefore, expected to be substantially faster than their classical counterpart. At their core, we use the unary encoding paradigm and orthogonal quantum layers and introduce a circuit to perform quantum Fourier transform in the unary basis. We propose three different quantum circuits to perform a quantum FNO. The proposals differ in their depth and their similarity to the classical FNO. We also benchmark our proposed algorithms on three PDE families, namely Burgers' equation, Darcy's flow equation and the Navier-Stokes equation. The results show that our quantum methods are comparable in performance to the classical FNO. We also perform an analysis on small-scale image classification tasks where our proposed algorithms are at par with the performance of classical CNNs, proving their applicability to other domains as well.