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arXiv:2105.08020

Optimizing the walk coin in the quantum random walk search algorithm through machine learning

Hristo Tonchev, Petar Danev

Published May 17, 2021• 5 citations
arXivHTMLPDF

Abstract

This paper examines the stability of the quantum random walk search algorithm, when the walk coin is constructed by generalized Householder reflection and additional phase shift, against inaccuracies in the phases used to construct the coin. The optimization of the algorithm is done by numerical methods - Monte Carlo, neural networks, and supervised machine learning. The results of numerical simulations show that, with such a construction of the Householder reflection, the algorithm is more stable to inaccuracies in the specific values of these phases, as long as it is possible to control the phase difference between the phase shift and the phase involved in the Householder reflection. This paper explicitly shows as an example, how achieving a properly designed phase difference would make quantum random walk search on a hypercube more stable for coin register consisting of one, two, and three qubits.

DOI
10.48550/arxiv.2105.08020

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This paper examines the stability of the quantum random walk search algorithm, when the walk coin is constructed by generalized Householder reflection and additional phase shift, against inaccuracies in the phases used to construct the coin. The optimization of the algorithm is done by numerical methods - Monte Carlo, neural networks, and supervised machine learning. The results of numerical simulations show that, with such a construction of the Householder reflection, the algorithm is more stable to inaccuracies in the specific values of these phases, as long as it is possible to control the phase difference between the phase shift and the phase involved in the Householder reflection. This paper explicitly shows as an example, how achieving a properly designed phase difference would make quantum random walk search on a hypercube more stable for coin register consisting of one, two, and three qubits.
This paper examines quantum random walk search algorithm with walk coin constructed by generalized Householder reflection and additional phase shift factor. The results from numerical simulations with neural network show that, with such a construction of the walk coin, the algorithm is more robust to inaccuracies in the specific values of its parameters, as long as it is possible to control the phase difference between the phase shift factor and the phase involved in the Householder reflection. This paper explicitly shows how achieving a properly designed phase difference would make quantum random walk search on a hypercube more robust for coin register consisting of one, two and three qubits.
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.
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.
In this work we obtain semi-empirical expressions for the probability to find solution of Quantum random walk search algorithm on hypercube when the traversing coin is constructed by generalized Householder reflection and an additional phase shift. Calculations are made for specific relations between coin phases (first from the reflection and second from the shift), that are obtained in our previous works. The quantum algorithm could be made more robust against phase errors, if an experimental implementation preserves those relations. The results from numerical simulations of quantum random walk search algorithm on hypercube are used to find an empirical formulas for algorithm’s probability to find solution. Those formulas, obtained by fitting data to a suitable function, are used to make prognosis about the algorithm’s robustness when dimension of the coin is too large to be simulated on classical computer. Here, we explicitly show all steps of the method used in the paper, while also discuss its advantages and limitations. The goal of this work is to help the experimental implementation of the quantum random walk search algorithm by giving evaluation of the algorithm’s robustness and the obtained probability to find a solution when our design of the walking coin is used.
This paper examines a way to simplify the circuit of quantum random walk search algorithm, when the traversing coin is constructed by both generalized Householder reflection and an additional phase multiplier. If an appropriate relation between corresponding parameters is realized, our algorithm becomes more robust to deviations in the phases. In this modification marking coin is not needed, and all advantages from above mentioned optimization to the stability, are preserved. It is shown explicitly how to construct such walk coin in order to obtain more robust quantum algorithm.
In this work we study the robustness of two modifications of quantum random walk search algorithm on hypercube. In the first previously suggested modification, on each even iteration only quantum walk is applied. And in the second, the closest neighbors of the solution are measured classically. In our approach the traversing coin is constructed by both generalized Householder reflection and an additional phase multiplier and we investigate the stability of the algorithm to deviations in those phases. We have shown that the unmodified algorithm becomes more robust when a certain relation between those phases is preserved. The first modification we study here does not lead to any change in the robustness of quantum random walk search algorithm. However, when a measurement of the first and second neighbors is included, there are some differences. The most important one, in view of our study of the robustness, is an increase in the stability of the algorithm, especially for large coin dimensions.