COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

Acar, Erdi; Yılmaz, İhsan

doi:10.3906/elk-2006-94

Cited by 25 publications

(13 citation statements)

References 26 publications

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“…Rasheed et al ( 87 ) used 32 GB in RAM with a processor of 3.40 GHz, even when they employed chest images; other works needed a GPU (graphic processor unit) ( 88 ). Also, specific studies operate a quantum computer ( 89 ), and others utilized fewer resources in processor (2.8 to 3.2 GHz) and RAM (8 or 12 GB) ( 90 – 92 ).…”

Section: Methodsmentioning

confidence: 99%

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

Real

Martínez-García²,

Márquez³

et al. 2022

Front. Public Health

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The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status during confinement due to the pandemic associated with COVID-19. The results showed that the XGBoost model got the best features associated with COVID-19 infection, and random forest approximated the best predictive model with a balanced accuracy of 90.41% using SMOTE as a resampling technique. The model with the best performance provides a tool to help prevent contracting SARS-CoV-2 since the variables with the highest risk factor are detected, and some of them are, to a certain extent controllable.

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Section: Methodsmentioning

confidence: 99%

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

Real

Martínez-García²,

Márquez³

et al. 2022

Front. Public Health

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show abstract

“…6 present the steps of our optimization approach using the above rules. Using this framework, we optimized the quantum machine learning circuit of a classification task for medical diagnosis using quantum transfer learning 28 . This circuit has been tested in several real quantum processors as well as various simulators.…”

Section: Methodsmentioning

confidence: 99%

“…This quantum circuit aims at distinguishing a sick person from a healthy person based on computed tomography images. The circuit consists of four steps: The Hadamard gate is first applied to all qubits and then with the help of U operator defined in 28 , the classical data is encoded and then entanglement is created. The dotted box of Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Transfer learning is an interesting technique in neural networks in which a pre-trained model is reused as an input model for a new task. One of the works in quantum neural networks presented in this field was developed by Acar et al 28 and uses the quantum transfer learning method. This method is a hybrid machine learning method consisting of a classical network feature extractors and a diverse quantum classification circuit.…”

Section: Related Workmentioning

confidence: 99%

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An optimizing method for performance and resource utilization in quantum machine learning circuits

Salehi

Zomorodi‐Moghadam

Pławiak

et al. 2022

Sci Rep

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Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computing can be used to implement many existing problems in different fields incredibly effectively. One important field that quantum computing has shown great results in machine learning. Until now, many different quantum algorithms have been presented to perform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will be reduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computation time, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since these algorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms of quantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimize quantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach including different optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms for big data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original ones and by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% in different circuits respectively. This is the amount of reduction for one iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the main circuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost and performance are improved.

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“…The study focuses on the availability of electricity in hospitals and health centers to ensure pervasive medical care to COVID-19 patients. Using CT scanned images of COVID-19 patients, [16] present a detection tool and experiment on IBM quantum computer for COVID-19 detection.…”

Section: Introductionmentioning

confidence: 99%

New normal: cooperative paradigm for COVID-19 timely detection and containment using Internet of things and deep learning

HASSAN KUMBHAR,

HASSAN,

SHİN

2021

Turk J Elec Eng & Comp Sci

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The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things (IoT) based paradigm using convolution neural networks (CNN), smart wearable, and connected E-Health to help governments resume normal life again. Our autonomous scheme provides three-level detection: inter-object distance for social distancing violations using CNN, area-based monitoring of active smartphone users and their current state of illness using connected E-Health, and smart wearable. Our exhaustive performance evaluation identifies that the proposed scheme with CNN YOLOv3 achieves up to 90% mean average precision in detecting social distancing violations, as compared to existing YOLOv2 achieving 70% and faster R-CNN with 76% . Our evaluation also identifies that wearing protective gear reduces spread by 50% , and timely actions in the first hour can help avoid three times COVID-19 exposure.

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COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

Cited by 25 publications

References 26 publications

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

An optimizing method for performance and resource utilization in quantum machine learning circuits

New normal: cooperative paradigm for COVID-19 timely detection and containment using Internet of things and deep learning

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