Impact of Bivariate Gaussian Potentials on Quantum Walks for Spatial Search

TL;DR

This paper investigates how bivariate Gaussian disorder potentials affect the performance and robustness of quantum walk-based spatial search algorithms, revealing their sensitivity to noise and potential for modeling noisy oracles.

Contribution

It introduces a model incorporating bivariate Gaussian potentials into quantum walks for spatial search, analyzing their impact on algorithm robustness and performance.

Findings

Success probability decreases with increasing Gaussian standard deviation.

Quantum walks with small standard deviation closely follow the AKR model.

The Gaussian potential effectively models noisy oracles in quantum search.

Abstract

Quantum search algorithms are crucial for exploring large solution spaces, but their robustness to environmental perturbations, such as noise or disorder, remains a critical challenge. We examine the impact of biased disorder potentials modeled by a bivariate Gaussian distribution function on the dynamics of quantum walks in spatial search problems. Building on the Ambainis-Kempe-Rivosh (AKR) model for searching on a two-dimensional grid, we incorporate potential fields to investigate how changes in standard deviation and normalization of the bivariate Gaussian function impact the performance of the search algorithm. Our results show that the quantum walk closely mirrors the AKR algorithm when the standard deviation is small but exhibits a rapid decay in success probability as the standard deviation increases. This behavior demonstrates how the bivariate Gaussian can effectively model a noisy oracle within the AKR algorithm. Additionally, we compare the AKR-based model with an alternative quantum walk model using a Hadamard coin and standard shift. These findings contribute to understanding the robustness of quantum walk search algorithms, and provide insights into how quantum walks can be applied to optimization algorithms.