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

Abstract

Diagnose 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.

An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph

“… 68 Proposed a classical quantum TL methodology for corona virus detection using IBM quantum computer. 69 …”

Hybrid Model of Quantum Transfer Learning to Classify Face Images with a COVID-19 Mask

“…To run a Hybrid Quantum Transfer Learning model with ResNet-18, you can use simulators such as Pennylane [16], Qiskit-Aer [17], and Cirq [20]; you can also use real quantum hardware like the one provided by IBM: ibmq london, ibmq rome, among others, is how the authors Acar and Yilmaz [21] made their proposal in the devices mentioned above; They also used CT images of the lungs of 126 people with COVID and 100 ordinary people, achieving an accuracy of 90% in the simulator and 94% to 100% in quantum computers.…”

X-ışınlı Göğüs İmgelerini Kullanarak Solunum Yolu Hastalıklarının Tespitinde Kuantum Transfer Öğrenme Modelinin Rolü

“…Fakat deneysel analizlerin kuantum bilgisayarlar tarafından gerçekleştirilememesi ise bir dezavantaj olarak görülmüştür. Neticesinde kuantum öğrenmenin analizlerini kuantum bilgisayarlar ile gerçekleştiren çalışmalarda performansın arttığı gözlemlenmiştir [12,13]. Acar ve Yılmaz [13] çalışmalarında eğitim-test sayısı dağılımını diğer çalışmalardan farklı olarak gerçekleştirmiştir.…”

“…Neticesinde kuantum öğrenmenin analizlerini kuantum bilgisayarlar ile gerçekleştiren çalışmalarda performansın arttığı gözlemlenmiştir [12,13]. Acar ve Yılmaz [13] çalışmalarında eğitim-test sayısı dağılımını diğer çalışmalardan farklı olarak gerçekleştirmiştir. Az sayıda eğitim verisi kullanarak önerdikleri KTÖ modeli ile başarılı sonuçlar elde etmişlerdir.…”

“…HK-ESA modeli ile %98,4 genel doğruluk oranı elde etmişler. Acar ve Yılmaz [13], COVID-19 hastalığının tanı sürecinde klasik kuantum transfer öğrenimi modelini kullanmışlar. Çalışmalarında kullandıkları veri seti, normal ve COVID-19 görüntü türlerinden oluşmaktaydı.…”