Optical homodyne tomography with polynomial series expansion

TL;DR

This paper introduces a polynomial series expansion method for optical homodyne tomography, improving reconstruction quality and noise resilience over traditional kernel deconvolution techniques.

Contribution

The paper presents a novel polynomial series expansion approach for optical homodyne tomography that addresses technical challenges and enhances reconstruction accuracy.

Findings

Reconstructs smoother and more accurate Wigner functions.

Improves noise handling and statistical error resilience.

Provides estimators for reconstruction errors.

Abstract

We present and demonstrate a method for optical homodyne tomography based on the inverse Radon transform. Different from the usual filtered back-projection algorithm, this method uses an appropriate polynomial series to expand the Wigner function and the marginal distribution and discretize Fourier space. We show that this technique solves most technical difficulties encountered with kernel deconvolution based methods and reconstructs overall better and smoother Wigner functions. We also give estimators of the reconstruction errors for both methods and show improvement in noise handling properties and resilience to statistical errors.