Despite its long history, classification is still a subject of extensive research because new application domains require more effective algorithms than the state-of-the-art classification algorithms, which rely on the logical theory of sets, the theory of probability and the algebra of vector spaces. The combination of distinct theoretical frameworks may be the key to making an important step forward toward a stable and significant improvement in classification effectiveness and, to the same extent improved Quantum Mechanics (QM) signal detection. QM may give rise to a new theoretical framework for classification, since it essentially moves the optimal bound of effectiveness beyond the levels made possible by the state-of-the-art classification algorithms. In this paper, we propose a binary classification model inspired by quantum detection theory in an effort to investigate how much benefit it brings as compared to classical models. Our experiments suggest that the improvement in classification effectiveness can be obtained, although the potential of quantum detection can only be partially exploited.
Despite its long history, classification is still a subject of extensive research because new application domains require more effective algorithms than the state-of-the-art classification algorithms, which rely on the logical theory of sets, the theory of probability and the algebra of vector spaces. The combination of distinct theoretical frameworks may be the key to making an important step forward toward a stable and significant improvement in classification effectiveness and, to the same extent improved Quantum Mechanics (QM) signal detection. QM may give rise to a new theoretical framework for classification, since it essentially moves the optimal bound of effectiveness beyond the levels made possible by the state-of-the-art classification algorithms. In this paper, we propose a binary classification model inspired by quantum detection theory in an effort to investigate how much benefit it brings as compared to classical models. Our experiments suggest that the improvement in classification effectiveness can be obtained, although the potential of quantum detection can only be partially exploited.
Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input. Several research works have demonstrated the effectiveness of machine learning algorithms but the state-of-the-art algorithms are based on the classical theories of probability and logic. Quantum Mechanics (QM) has already shown its effectiveness in many fields and researchers have proposed several interesting results which cannot be obtained through classical theory. In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms. It is believed that the theory of QM may also inspire an effective algorithm if it is implemented properly. From this inspiration, we propose the quantum-inspired binary classifier, which is based on quantum detection theory. We used text corpora and image corpora to explore the effect of our proposed model. Our proposed model outperforms the state-of-the-art models in terms of precision, recall, and F-measure for several topics (categories) in the 20 newsgroup text corpora. Our proposed model outperformed all the baselines in terms of recall when the MNIST handwritten image dataset was used; F-measure is also higher for most of the categories and precision is also higher for some categories. Our proposed model suggests that binary classification effectiveness can be achieved by using quantum detection theory. In particular, we found that our Quantum-Inspired Binary Classifier can increase the precision, recall, and F-measure of classification where the state-of-the-art methods cannot.
Machine Learning models learn the relationship between input and output by examples and then apply the learned models to relate unseen input. Although ML has successfully been used in almost every field, there is always room for improvement. To this end, researchers have recently been trying to implement Quantum Mechanics(QM) in ML, since it is believed that quantum-inspired ML can enhance learning rate and effectiveness. In this paper, we address a specific task of ML and present a binary classification model inspired by the quantum detection framework. We compared the model to the state of the art. Our experimental results suggest that the use of the quantum detection framework in binary classification can improve effectiveness for a number of topics of the RCV-1 test collection and that it may still provide ways to improve effectiveness for the other topics.
Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input. Quantum Mechanics (QM) has already shown its effectiveness in many fields and researchers have proposed several interesting results which cannot be obtained through classical theory. In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms. It is believed that the theory of QM may also inspire an effective algorithm if it is implemented properly. From this inspiration, we propose the quantum-inspired binary classifier.
Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.
Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.
Quantum classification is a rapidly developing area in the field of quantum computing that seeks to leverage the unique properties of quantum mechanics to improve the accuracy and efficiency of classification tasks. This chapter provides an in-depth exploration of the key concepts, algorithms, and applications of quantum classification. It begins with an introduction to the basic principles of quantum mechanics underlying quantum classification, including qubits, quantum gates, and quantum circuits. It then moves on to various quantum classification algorithms, such as quantum support vector machines, quantum decision trees, and quantum K-nearest neighbors, and compares them to their classical counterparts. Next, it delves into the practical aspects of quantum classification, such as hardware and software considerations, error correction, and optimization techniques. The use of quantum machine learning libraries, such as PennyLane and Qiskit, to implement quantum classification algorithms are also explored. The chapter then showcases the wide range of applications of quantum classification, including image and speech recognition, natural language processing, finance, and drug discovery. It highlights the potential advantages and limitations of quantum classification, compared to classical approaches, and presents several use cases where quantum classification has demonstrated superior performance. Finally, the current state of the art in quantum classification is discussed and future directions for research in this exciting and rapidly growing field are outlined. The challenges and opportunities for the adoption of quantum classification in industry and academia, and the potential impact of quantum classification on society at large are also covered. Overall, this chapter serves as a comprehensive guide to the emerging field of quantum classification, offering readers a clear understanding of the theory, algorithms, and applications of this exciting new area of research.