Explicit Block Encoding of Difference-of-Gaussian Operators on a Periodic Grid

TL;DR

This paper presents an explicit quantum block encoding of the Difference-of-Gaussian operator on a periodic grid, enabling efficient quantum processing for image analysis and related applications without complex or black-box subroutines.

Contribution

It introduces a novel explicit construction of the DoG operator's quantum block encoding that is efficient, scalable, and leverages its probabilistic structure and Fourier diagonalization.

Findings

Achieves a constant subnormalization factor independent of grid size and dimension.

Provides an exact expression for the block-encoding success probability based on the input spectrum.

Demonstrates the encoding's accuracy scales as $O(h^4)$ with grid refinement.

Abstract

The Difference-of-Gaussian (DoG) is a widely used operator across applications, including image processing (feature and edge detection), quantum machine learning, and finite-difference methods (approximations of the Laplacian-of-Gaussian). In this paper, we construct an explicit quantum block encoding of the DoG operator on a periodic grid, exploiting its natural probabilistic structure. The central observation is that the DoG admits a natural decomposition to two normalized Gaussian distributions, each preparable by explicit and efficient circuits, with the negation encoded using a single Pauli-$Z$ gate on a branch-indicator qubit. This enables the operator's block encoding to be directly mapped to the Linear Combination of Unitaries framework without requiring signed amplitude loading, quantum random-access memory, or any other black-box oracles. The proposed method achieves a constant subnormalization factor $\lambda = 2$ independent of the grid size $N$, the spatial dimension $D$, and the stencil width. Additionally, we show that the DoG operator is diagonalized by the discrete Fourier basis, which allows us to derive an exact closed-form expression for the block-encoding success probability in terms of the input signal's power spectrum, weighted by the operator's transfer function. Finally, we prove that the expression reduces to $O(h^4)$ scaling with respect to grid spacing $h$ as the periodic grid becomes finer. This implementation provides an explicit construction method for a tunable, wide-stencil bandpass filter whose frequency response is controlled by two Gaussian scale parameters.