Work

High robustness quantum walk search algorithm with qudit Householder traversing coin, machine learning study

Year: 2021

Type: preprint

Abstract: In this work the quantum random walk search algorithm with walk coin constructed by generalized Householder reflection and phase multiplier has been studied. The coin register is one qudit with arbitrary dimension. Monte Carlo simulations, in combination with supervised machine learning, are used to find walk coins making the quantum algorithm more robust to deviations in the coin's parameters. By applying deep neural network we make prediction for the parameters of an optimal coin with arbitrary size and estimate the stability for such coin.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2111.10926

Authors: Tonchev, Hristo, Danev, Petar

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: green

Funder: Bulgarian National Science Fund

OpenAlex ID: W3217584805

Cites: 31

Cited by: 2

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