Quantum radial basis function methods for scattered data fitting

TL;DR

This paper introduces two quantum algorithms for scattered data fitting using radial basis functions, achieving significant speedups over classical methods in data processing and matrix operations.

Contribution

The paper presents novel quantum algorithms for scattered data fitting with RBFs, offering quadratic and exponential speedups over classical approaches.

Findings

Quadratic speedup in data handling for globally supported RBFs.

Exponential speedup in data fitting with compactly supported RBFs.

Efficient quantum procedures leveraging coherent states and HHL algorithm.

Abstract

Scattered data fitting is a frequently encountered problem for reconstructing an unknown function from given scattered data. Radial basis function (RBF) methods have proven to be highly useful to deal with this problem. We describe two quantum algorithms to efficiently fit scattered data based on globally and compactly supported RBFs respectively. For the globally supported RBF method, the core of the quantum algorithm relies on using coherent states to calculate the radial functions and a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion. A quadratic speedup is achieved in the number of data over the classical algorithms. For the compactly supported RBF method, we mainly use the HHL algorithm as a subroutine to design an efficient quantum procedure that runs in time logarithmic in the number of data, achieving an exponential improvement over the classical methods.