Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage

Machine Learning for ligand based virtual screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.

Epidemic Healthcare Kiosk: A Social Economic Remote Solution Using IoT

One of the most difficult tasks for the physicians is to analyze, manage, and plan suitable diagnoses and treatments for society, especially in an epidemic situation like Corona virus disease (COVID-19). Hence, the mortality rate shoots up. The ultimate reason for such pathetic situation is due to large mass of people being infected, lack of physicians and testing staff, and the threat of physicians themselves being infected while testing patients. This article proposes a solution to tackle this major issue worldwide. This article portrays the methodology of an IoT-interfaced epidemic healthcare kiosk (EHK)-intelligent monitoring system to plan and manage epidemics. The EHK is a non-invasive data acquisition system that consists of several sensors that can record the physiological measurements of the EHK user. The measured parameters are computed using quantum machine learning techniques. The proposed ideology can reduce the mortality rate, control the epidemic, and moreover, provide safety to citizens and physicians.

Epidemic Healthcare Kiosk: A Social Economic Remote Solution Using IoT

One of the most difficult tasks for the physicians is to analyze, manage, and plan suitable diagnoses and treatments for society, especially in an epidemic situation like Corona virus disease (COVID-19). Hence, the mortality rate shoots up. The ultimate reason for such pathetic situation is due to large mass of people being infected, lack of physicians and testing staff, and the threat of physicians themselves being infected while testing patients. This article proposes a solution to tackle this major issue worldwide. This article portrays the methodology of an IoT-interfaced epidemic healthcare kiosk (EHK)-intelligent monitoring system to plan and manage epidemics. The EHK is a non-invasive data acquisition system that consists of several sensors that can record the physiological measurements of the EHK user. The measured parameters are computed using quantum machine learning techniques. The proposed ideology can reduce the mortality rate, control the epidemic, and moreover, provide safety to citizens and physicians. © 2022 IGI Global. All rights reserved.

Severity Classification of COVID-19 Patients Data using Quantum Machine Learning Approaches

Artificial intelligence (AI) is assisting in several aspects of the COVID-19 pandemic, including medical diagnosis and therapy, drug development, molecular research and epidemiology. The involvement of AI in healthcare can help doctors to detect symptoms more quickly. In such era, we use the Quantum Machine Learning (QML) approaches to address clinical applications of machine learning (ML) technology, such as electronic healthcare data and clinical features. This paper presents the two QML algorithms, i.e, Enhanced Quantum Support Vector Machine (E-QSVM) and Quantum Random Forest (QRF) applied to COVID-19 and influenza datasets, which were collected from different private hospitals. The experimental results show that the proposed models outperform in terms of accuracy by achieving the highest accuracy of 78% for the E-QSVM model and 75% for the QRF model respectively. The competency of the models is obtained by comparing them with classical models and recently published quantum models. © 2022 IEEE.

Quantum Machine Learning for Audio Classification with Applications to Healthcare

Accessible rapid COVID-19 testing continues to be necessary and several studies involving deep neural network (DNN) methods for detection have been published. As part of a sponsored NSF I/UCRC project, our team explored the use of deep learning algorithms for recognizing COVID-19 related cough audio signatures. More specifically, we have worked with several DNN algorithms and cough audio databases and reported results with the VGG-13 architecture. In this paper, we report a study on the use of quantum neural networks for audio signature detection and classification. A hybrid quantum neural network (QNN) model for COVID-19 cough classification is developed. The design of the QNN simulation architecture is described and results are given with and without quantum noise. Comparative results between classical and quantum neural network methods for COVID-19 audio detection are also presented. © 2022 IEEE.

Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review

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 has become quite a challenging field in the 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 using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.

Quantum Ordering Points to Identify the Clustering Structure and Application to Emergency Transportation

Studies exploring the use of artificial intelligence (AI) and machine learning (ML) are knowing an undeniable success in many domains. On the other hand, quantum computing (QC) is an emerging field investigated by a large expanding research these last years. Its high computing performance is attracting the scientific community in search of computing power. Hybridizing ML with QC is a recent concern that is growing fast. In this paper, we are interested in quantum machine learning (QML) and more precisely in developing a quantum version of a density-based clustering algorithm namely, the Ordering Points To Identify the Clustering Structure (QOPTICS). The algorithm is evaluated theoretically showing that its computational complexity outperforms that of its classical counterpart. Furthermore, the algorithm is applied to cluster a large geographic zone with the aim to contribute in solving the problem of dispatching ambulances and covering emergency calls in case of COVID-19 crisis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation.

In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation.

6G Networks for Next Generation of Digital TV Beyond 2030.

This paper prosed a novel 6G QoS over the future 6G wireless architecture to offer excellent Quality of Service (QoS) for the next generation of digital TV beyond 2030. During the last 20 years, the way society used to watch and consume TV and Cinema has changed radically. The creation of the Over The Top content platforms based on Cloud Services followed by its commercial video consumption model, offering flexibility for subscribers such as n Video on Demand. Besides the new business model created, the network infrastructure and wireless technologies also permitted the streaming of high-quality TV and film formats such as High Definition, followed by the latest widespread TV standardization Ultra-High- Definition TV. Mobile Broadband services onset the possibility for consumers to watch TV or Video content anywhere at any time. However, the network infrastructure needs continuous improvement, primarily when crises, like the coronavirus disease (COVID-19) and the worldwide pandemic, creates immense network traffic congestions. The outcome of that congestion was the decrease of QoS for such multimedia services, impacting the user's experience. More power-hungry video applications are commencing to test the networks' resilience and future roadmap of 5G and Beyond 5G (B5G). For this, 6G architecture planning must be focused on offering the ultimate QoS for prosumers beyond 2030.