Acar, ErdiYılmaz, İhsan2023-06-192023-06-192021Acar, E., & Yılmaz, İ. (2021). COVID-19 detection on IBM quantum computer with classical-quantum transfer learning. Turkish Journal of Electrical Engineering and Computer Sciences, 29(1), 46-61. doi:10.3906/ELK-2006-941300-06321303-6203https://doi.org/10.3906/ELK-2006-94https://hdl.handle.net/20.500.12428/4330Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94%–100% in quantum computers. Also, according to the results obtained, machine learning process in classical computers requiring more processors and time than quantum computers can be realized in a very short time with a very small quantum processor such as 4 qubits in quantum computers. If the size of the data set is small; due to the superior properties of quantum, it is seen that according to the classification of COVID-19 and normal, in terms of machine learning, quantum computers seem to outperform traditional computers.eninfo:eu-repo/semantics/openAccessAttribution 3.0 United StatesCovid-19Quantum transfer learningVariational quantum circuitCOVID-19 detection on IBM quantum computer with classical-quantum transfer learningArticle291466110.3906/ELK-2006-94Q4WOS:0006144374000042-s2.0-85101014124514143