On quantum methods for machine learning problems part II: Quantum classification algorithms

Ablayev, Farid; Ablayev, Marat; Huang, Joshua Zhexue; Khadiev, Kamil; Salikhova, Nailya; Wu, Dingming

doi:10.26599/bdma.2019.9020018

Cited by 26 publications

(14 citation statements)

References 11 publications

(22 reference statements)

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“…Communication-efficient distributed learning methods have gained popularity recently [10][11][12][13][14] . We briefly review two kinds of related work.…”

Section: Related Workmentioning

confidence: 99%

SignGD with error feedback meets lazily aggregated technique: Communication-efficient algorithms for distributed learning

Deng

Sun

Liu

et al. 2022

Tsinghua Sci. Technol.

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The proliferation of massive datasets has led to significant interests in distributed algorithms for solving large-scale machine learning problems. However, the communication overhead is a major bottleneck that hampers the scalability of distributed machine learning systems. In this paper, we design two communication-efficient algorithms for distributed learning tasks. The first one is named EF-SIGNGD, in which we use the 1-bit (sign-based) gradient quantization method to save the communication bits. Moreover, the error feedback technique, i.e., incorporating the error made by the compression operator into the next step, is employed for the convergence guarantee. The second algorithm is called LE-SIGNGD, in which we introduce a well-designed lazy gradient aggregation rule to EF-SIGNGD that can detect the gradients with small changes and reuse the outdated information. LE-SIGNGD saves communication costs both in transmitted bits and communication rounds. Furthermore, we show that LE-SIGNGD is convergent under some mild assumptions. The effectiveness of the two proposed algorithms is demonstrated through experiments on both real and synthetic data.

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“…Communication-efficient distributed learning methods have gained popularity recently [10][11][12][13][14] . We briefly review two kinds of related work.…”

Section: Related Workmentioning

confidence: 99%

SignGD with error feedback meets lazily aggregated technique: Communication-efficient algorithms for distributed learning

Deng

Sun

Liu

et al. 2022

Tsinghua Sci. Technol.

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“…Hence, the quantum-enhanced machine learning domain is found to be one of these promising approaches. However, the interest to implement these techniques through quantum computation paves the way to quantum-enhanced machine learning [20].…”

Section: Process Of Traditional Machine Learning and Quantum-enhanced Machine Learningmentioning

confidence: 99%

“…Quantum-enhanced machine learning is a subfield 3 Wireless Communications and Mobile Computing of quantum information processing research [3] that focuses on developing machine learning algorithms capable of learning from data. Quantum computers compute information using the principles of quantum theory, and quantum algorithms are a collection of assertions that execute on these systems [20]. Many quantum algorithms have been designed for machine learning techniques such as neural networks and graphical models.…”

Section: Process Of Traditional Machine Learning and Quantum-enhanced Machine Learningmentioning

confidence: 99%

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Kumar

Koul

Sisodia

et al. 2021

Wireless Communications and Mobile Computing

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Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

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“…As a result, investigating an effective unbalanced data classification technology [7] is extremely important. K-nearest neighbor (KNN) method has become one of the most famous algorithms in the field of pattern recognition [8][9][10] and statistics because of its simple algorithm, easy realization, no need to estimate parameters, and high classification accuracy, and it is also one of the earliest nonparametric algorithms applied to automatic text classification in machine learning [11,12]. However, KNN method needs to store all the training sample data in the process of calculating the nearest neighbor of each sample to be tested, which leads to a large number of similarity calculations for classification and significantly increases the complexity of classification calculation with the increase of sample data set [13], thus reducing the classification efficiency.…”

Section: Introductionmentioning

confidence: 99%

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

Ji¹

2022

Scientific Programming

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The Chinese language is a nation’s symbol, the accumulation of a country’s magnificent culture, and the pearl precipitated in the long history’s washing. The Chinese language is rich and complex, and there are still many topics and issues that merit repeated exchanges and discussions in academic circles. This study proposes a classification method of emotion polarity based on reliability analysis in order to identify the tendency of literary emotion in Chinese language. Support vector machine (SVM), class center, and KNN (K-nearest neighbor) are included in the combined classifier, which effectively improves the accuracy and efficiency of emotion polarity classification. A Chinese literary emotion analysis model based on the method of UKNNC (unbalanced K-nearest neighbor classification) is proposed at the same time by analysing the characteristics of text structure and emotion expression. The experimental results show that, when compared to traditional machine methods, the UKNNC method can analyse text sentiment in fine-grained and multilevel ways, while also improving the accuracy of Chinese literary sentiment analysis.

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On quantum methods for machine learning problems part II: Quantum classification algorithms

Cited by 26 publications

References 11 publications

SignGD with error feedback meets lazily aggregated technique: Communication-efficient algorithms for distributed learning

SignGD with error feedback meets lazily aggregated technique: Communication-efficient algorithms for distributed learning

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

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