Local Approach to Quantum-inspired Classification

Blanzieri, Enrico; Leporini, Roberto; Pastorello, Davide

doi:10.1007/s10773-022-05263-y

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…[35] Blanzieri et al investigated the classification problem by combining the KNN algorithm with a quantum-inspired classifier. [36] Quezada et al proposed a KNN algorithm based on quantum sorting, which is suitable for quantum computers with limited circuit depth and meets the requirements of adaptability. [37] The speeds of all the above QKNN algorithms are faster than those of the traditional KNN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Quantum K‐Nearest Neighbor Classification Algorithm via a Divide‐and‐Conquer Strategy

Gong,

Ding,

et al. 2024

Adv Quantum Tech

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The K‐nearest neighbor algorithm is one of the most frequently applied supervised machine learning algorithms. Similarity computing is considered to be the most crucial and time‐consuming step among the classical K‐nearest neighbor algorithm. A quantum K‐nearest neighbor algorithm is proposed based on the divide‐and‐conquer strategy. A quantum circuit is designed to calculate the fidelity between the test sample and each feature vector of the training dataset. The quantum K‐nearest neighbor algorithm has higher classification efficiency in high‐dimensional data processing. The classification accuracy of the proposed algorithm is equivalent to that of the classical K‐nearest neighbor algorithm under the IRIS dataset. In addition, compared with the typical quantum K‐nearest neighbor algorithms, the proposed classification method possesses higher classification accuracy with less calculation time, which has wide applications in the industrial field.

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Section: Introductionmentioning

confidence: 99%

Quantum K‐Nearest Neighbor Classification Algorithm via a Divide‐and‐Conquer Strategy

Gong,

Ding,

et al. 2024

Adv Quantum Tech

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Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy

Rath,

Date

2024

EPJ Quantum Technol.

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Quantum-like Data Modeling in Applied Sciences: Review

Lipovetsky¹

2023

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This work presents a brief review on the modern approaches to data modeling by the methods developed in the quantum physics during the last one hundred years. Quantum computers and computations have already been widely investigated theoretically and attempted in some practical implementations, but methods of quantum data modeling are not yet sufficiently established. A vast range of concepts and methods of quantum mechanics have been tried in many fields of information and behavior sciences, including communications and artificial intelligence, cognition and decision making, sociology and psychology, biology and economics, financial and political studies. The application of quantum methods in areas other than physics is called the quantum-like paradigm, meaning that such approaches may not be related to the physical processes but rather correspond to data modeling by the methods designed for operating in conditions of uncertainty. This review aims to attract attention to the possibilities of these methods of data modeling that can enrich theoretical consideration and be useful for practical purposes in various sciences and applications.

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Local Approach to Quantum-inspired Classification

Cited by 3 publications

References 12 publications

Quantum K‐Nearest Neighbor Classification Algorithm via a Divide‐and‐Conquer Strategy

Quantum K‐Nearest Neighbor Classification Algorithm via a Divide‐and‐Conquer Strategy

Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy

Quantum-like Data Modeling in Applied Sciences: Review

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