Analyzing SARS CoV-2 Patient Data Using Quantum Supervised Machine Learning
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Abstract
The novel coronavirus disease 2019 (COVID-19) has created a serious threat to global health. We developed a new quantum machine learning (QML) assisted diagnostic method that can provide an accurate diagnosis to aid decision processes of medical providers. One of the key elements in our method was to implement the quantum variational method to efficiently classify data, taking crucial multiple correlations among the features into account. We established and fine-tuned this quantum classifier by using a group of data drawn from publicly available COVID-19 cases. We have shown that QML is capable of processing patient information efficiently and accurately for the diagnosis of COVID-19.
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SciScore for 10.1101/2021.10.26.466019: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Using the Pennylane program (freely available on Pennylane.ai) and the Anaconda Python application, we were able to design a properly working quantum variational classifier. Pythonsuggested: (IPython, RRID:SCR_001658)Initially, the Iris data set was used to test the classifier and determine if it worked. Irissuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in …SciScore for 10.1101/2021.10.26.466019: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Using the Pennylane program (freely available on Pennylane.ai) and the Anaconda Python application, we were able to design a properly working quantum variational classifier. Pythonsuggested: (IPython, RRID:SCR_001658)Initially, the Iris data set was used to test the classifier and determine if it worked. Irissuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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