Quantum Machine Learning Applications

Arshdeep, Konika Rani, Karanpreet Singh +3 more

Machine learning, a subset of AI is moving algorithms in the system which can be learned by data and not programmed explicitly. Various applications of AI across professionals like finance, health-care and automation. With the advancements in Machine Learning, Quantum Machine Learning (QML) or more concretely integrating quantum mechanics into classical machine learning tasks is emerging since QM provides some of the principles to increase efficiency from computational benchmarks. From the most used types of machine learning and algorithms to quantum its usage and applications in Quantum Machine Learning (QML) this paper covers everything. We then articulate some benefits and difficulties of merging quantum computing with machine learning, as well the real-life applications across industry backbones.

Danyal Maheshwari, Begonya García-Zapirain, Daniel Sierra-Sosa

Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning, become a quite challenging field in case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models, and quantum circuits are using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.

Alessandro Luongo

Dhanunjai Bandi, Yong Shi, Hossain Shahriar +4 more

In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.

Cristian Romero García