Work

A Machine Learning Study of High Robustness Quantum Walk Search Algorithm with Qudit Householder Coins

Year: 2023

Type: article

Abstract: In this work, the quantum random walk search algorithm with a walk coin constructed by generalized Householder reflection and phase multiplier has been studied. The coin register is one qudit with an arbitrary dimension. Monte Carlo simulations, in combination with supervised machine learning, are used to find walk coins that make the quantum algorithm more robust to deviations in the coin’s parameters. This is achieved by introducing functional dependence between these parameters. The functions that give the best performance of the algorithm are studied in detail by numerical statistical methods. A thorough comparison between our modification and an algorithm, with coins made using only Householder reflection, shows significant advantages of the former. By applying a deep neural network, we make a prediction for the parameters of an optimal coin with an arbitrary size and estimate the algorithm’s stability for such a coin.

Source: Algorithms

DOI: 10.3390/a16030150

Authors: Hristo Tonchev, Petar Danev

Institutions: Bulgarian Academy of Sciences, Georgi Nadjakov Institute of Solid State Physics, Institute for Nuclear Research and Nuclear Energy

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,779

Funder: Bulgarian National Science Fund