Application of Quantum k-NN and Grover’s Algorithms for Recommendation Big-Data System

In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in an unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows the main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps in the recommendation system is also indicated. The verification of the correctness of the proposed system is analysed as well, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.

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“…It should be noted that this article is an extension of a previous publication (Sawerwain and Wróblewski, 2019).…”

Section: Introductionmentioning

confidence: 90%

Recommendation systems with the quantum k–NN and Grover algorithms for data processing

Sawerwain

Wróblewski

2019

International Journal of Applied Mathematics and Computer Science

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“…It should be noted that this article is an extension of a publication [19]. This article presents more precisely the structure of the quantum register and two numerical examples showing the behaviour of the recommending system.…”

Section: Introductionmentioning

confidence: 99%

Recommendation systems with quantum k-NN and Grover's algorithms for data processing

Sawerwain,

Wróblewski

2018

Preprint

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In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows a main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps during recommendation system was also indicated. The verification correctness of a proposed recommendation system was also analysed, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.

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Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation System

Li,

Shi,

Zhang

et al. 2024

ACM Trans. Knowl. Discov. Data

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Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect to receive recommendations for products of interest in the shortest possible time. To address this challenge, we propose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantum computing circuits based on data reconstruction. This approach allows for the rapid identification of users similar to the target user, thereby improving recommendation speed. In our method, we utilize the information of known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, we employ \(l_{2,1}-\) norm and \(l_{1}-\) norm to sparsely constrain the relationship matrix, deducing the weight of each known user. The final step involves providing similar recommendations to target users based on these weights. Furthermore, we implement the proposed algorithm using a quantum circuit, enabling exponential acceleration. The final weight matrix is derived from the quantum state outputted by the circuit. The speed of this process is theoretically demonstrated in detail. Experimental results indicate that our algorithm outperforms state-of-the-art methods in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Normalized Discounted Cumulative Gain (NDCG). Compared to state-of-the-art comparison algorithms, the proposed algorithm achieves the fastest recommendation speed across eight public datasets.

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Application of Quantum k-NN and Grover’s Algorithms for Recommendation Big-Data System

Cited by 4 publications

References 13 publications

Recommendation systems with the quantum k–NN and Grover algorithms for data processing

Recommendation systems with the quantum k–NN and Grover algorithms for data processing

Recommendation systems with quantum k-NN and Grover's algorithms for data processing

Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation System

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