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

Sawerwain, Marek; Wróblewski, Marek

doi:10.2478/amcs-2019-0011

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…Another paper [17] discusses the impact of Grover's algorithm, which is able to search in a non-structured database in sub-linear time, and its possible applications in recommender systems. Lastly, in [18] it is proposed to combine a quantum-based KNN with the Grover algorithm. Other articles propose to use, instead, the adiabatic model.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection for Recommender Systems with Quantum Computing

Nembrini

Dacrema

Cremonesi

2021

Entropy

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The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular,quantum annealing, can be used to sample optimal solutions for a number of NP-hard optimization problems represented with classical operations research tools, providing an easy access to the potential of this emerging technology. One of the tasks that most naturally fits in this mathematical formulation is feature selection. In this paper, we investigate how to design a hybrid feature selection algorithm for recommender systems that leverages the domain knowledge and behavior hidden in the user interactions data. We represent the feature selection as an optimization problem and solve it on a real quantum computer, provided by D-Wave. The results indicate that the proposed approach is effective in selecting a limited set of important features and that quantum computers are becoming powerful enough to enter the wider realm of applied science.

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Section: Related Workmentioning

confidence: 99%

Feature Selection for Recommender Systems with Quantum Computing

Nembrini

Dacrema

Cremonesi

2021

Entropy

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“…This work describes a novel Quantum K-Nearest Neighbor (QK-NN) algorithm based on data stored in a Quantum Random Access Memory (QRAM) [26][27][28]. The QRAM incorporates address and data qubits, leveraging quantum superposition.…”

Section: Introductionmentioning

confidence: 99%

“…Our method computes the required distance metric based on the SWAP-Test, which offers a similarity metric for comparing two quantum states. We use Grover's algorithm [26,[29][30][31][32] to efficiently identify states with high similarity scores by utilizing an oracle. Quantum states from the labeled dataset are analyzed using the SWAP-Test [33,34] via a mid-circuit measurement.…”

Section: Introductionmentioning

confidence: 99%

Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance

Maldonado-Romo,

Montiel-Pérez,

Onofre

et al. 2024

Mathematics

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This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with O(log(n)) space complexity. It incorporates Grover’s algorithm and the quantum SWAP-Test to identify similar states and determine the nearest neighbors with high probability, achieving Om search complexity, where m is the qubit address. We implement a simulation of the algorithm using IBM’s Qiskit with GPU support, applying it to the Iris and MNIST datasets with two different angle encodings. The experiments employ multiple QRAM cell sizes (8, 16, 32, 64, 128) and perform ten trials per size. According to the performance, accuracy values in the Iris dataset range from 89.3 ± 5.78% to 94.0 ± 1.56%. The MNIST dataset’s mean binary accuracy values range from 79.45 ± 18.84% to 94.00 ± 2.11% for classes 0 and 1. Additionally, a comparison of the results of this proposed approach with different state-of-the-art versions of QK-NN and the classical K-NN using Scikit-learn. This method achieves a 96.4 ± 2.22% accuracy in the Iris dataset. Finally, this proposal contributes an experimental result to the state of the art for the MNIST dataset, achieving an accuracy of 96.55 ± 2.00%. This work presents a new implementation proposal for QK-NN and conducts multiple experiments that yield more robust results than previous implementations. Although our average performance approaches still need to surpass the classic results, an experimental increase in the size of QRAM or the amount of data to encode is not achieved due to limitations. However, our results show promising improvement when considering working with more feature numbers and accommodating more data in the QRAM.

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“…Actually, the simplest and most effective locality technique is represented precisely by the k -NN, which picks out the elements closest to the target one according to a given metric. Different quantum variants of the k -NN algorithm have been proposed; moreover, an interesting application of a quantum k -NN version in combination with the Grover search algorithm [ 14 ] has also been presented by Sawerwain and Wróblewski [ 15 ], namely, a quantum recommendation system.…”

Section: Introductionmentioning

confidence: 99%

Implementation and empirical evaluation of a quantum machine learning pipeline for local classification

Zardini,

Blanzieri,

Pastorello

2023

PLoS ONE

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In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. A well-known locality technique is the k-nearest neighbors (k-NN) algorithm, of which several quantum variants have been proposed; nevertheless, they have not been employed yet as a preliminary step of other QML models. Instead, for the classical counterpart, a performance enhancement with respect to the base models has already been proven. In this paper, we propose and evaluate the idea of exploiting a quantum locality technique to reduce the size and improve the performance of QML models. In detail, we provide (i) an implementation in Python of a QML pipeline for local classification and (ii) its extensive empirical evaluation. Regarding the quantum pipeline, it has been developed using Qiskit, and it consists of a quantum k-NN and a quantum binary classifier, both already available in the literature. The results have shown the quantum pipeline’s equivalence (in terms of accuracy) to its classical counterpart in the ideal case, the validity of locality’s application to the QML realm, but also the strong sensitivity of the chosen quantum k-NN to probability fluctuations and the better performance of classical baseline methods like the random forest.

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

Cited by 8 publications

References 33 publications

Feature Selection for Recommender Systems with Quantum Computing

Feature Selection for Recommender Systems with Quantum Computing

Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance

Implementation and empirical evaluation of a quantum machine learning pipeline for local classification

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