Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments

Engelsberger, Alexander; Villmann, Thomas

doi:10.3390/e25030540

Cited by 3 publications

(2 citation statements)

References 100 publications

(131 reference statements)

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“…Inside the python quantum framework Qiskit [15], there is a package for optimization, which implements the necessary steps to transform constrained integer problems into QUBO. With the transformed problem at hand, there are different proposed quantum heuristics (see [7], Section 4.7).…”

Section: Get Quantum Readymentioning

confidence: 99%

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Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Engelsberger,

Villmann

2023

ESANN 2023 Proceesdings

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Quantum Computing Research proposed strategies to solve binary optimization problems. Application on current and near-term generation Hardware is possible. Even if computational benefits of the strategies are yet to be shown, we want to explore connections to prototype learning schemes. We examine cost functions for vector quantization based on data point selection and how they can be transformed into a common quadratic unconstrained binary optimization formulation (QUBO). There are different approaches for solving QUBO problems using quantum computer or quantum annealer hardware. We look at their current limits and how they might change.

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Section: Get Quantum Readymentioning

confidence: 99%

“…Quantum annealers still have to prove their potential, that is claimed [12] by their creators. In [7] we introduced a connection between vector quantization and optimization problems, along other connections. More precise, we used a set cover problem for VQ [2], that is compatible with quantum computing, especially quantum annealing.…”

Section: Introductionmentioning

confidence: 99%

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Engelsberger,

Villmann

2023

ESANN 2023 Proceesdings

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Machine Learning Enabled Image Classification Using K-Nearest Neighbour and Learning Vector Quantization

Akinsola,

Onipede,

Olajubu

et al. 2024

Soft Computing and Its Engineering Applications

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Quantum computing and machine learning for Arabic language sentiment classification in social media

Omar,

Abd El-Hafeez

2023

Sci Rep

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With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data.

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Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments

Cited by 3 publications

References 100 publications

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Machine Learning Enabled Image Classification Using K-Nearest Neighbour and Learning Vector Quantization

Quantum computing and machine learning for Arabic language sentiment classification in social media

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