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ChemicalQDeviceQuantum Computing and Medical Industry Results
Dear Healthcare Colleague, my name is Kevin Kawchak, and I am the Founder CEO of ChemicalQDevice. Today is February 3rd, 2023 for "Quantum Computing and Medical Imaging Industry Results"
In 2019, Harvard researchers published an article in Nature Physics titled "Quantum convolutional neural networks", "QCNNs" which highlighted their benefits over conventional approaches. The authors explained that Today's neural networks used for image processing and precision medicine are lacking in the area of quantum physics problems (1)
In 2021 an APS paper asserted that issues associated with higher quantum bits "qubits" such as Barren plateaus can be eliminated in some cases utilizing QCNNs. (2) In Modern Day publications, Instantaneous Quantum Polynomial (IQP) encoding is used because of its ability to Entangle qubits for exponential calculation capacity in Real Quantum Computers. (3)
In a 2023 Journal of Thermal Biology paper, a 2 qubit simulator was used with Quantum Kernel Aignment to classify Rheumatoid Arthritis hand thermal images as "normal" or "RA". Implementing a Quantum Support Vector Machine "QSVM", an accuracy of 92.7% was achieved. (4) In 2022 IBM and Amgen utilized a 27 qubit computer with other hybrid techniques to preliminarily predict the persistance of Rheumatoid Arthritis patients. The goal of the paper was to better understand whether a given dataset would exhibit quantum advantage utilizing Explainable AI/Shapley value methods. Using a QSVM, Empirical predictions were found to be as good as classical computers. (5) In 2022, Roche and QCware performed Medical Image classification in 2 areas of Health utilizing a 16 qubit IBM computer, and a quantum simulator - Both with Quantum Neural Networks. Chest images were identified as “healthy” or “pneumonia-infected” having slightly better accuracy with quantum methods. Retinopathy images were classified as “normal” or “different levels” from the MedMNIST database and accuracies were slightly better than classical methods. (6)
A 2022 article titled “Automated Binary Classification of Alzheimer’s via Hybrid Classical Quantum Neural Networks” featured a 4 qubit Pennylane hybrid simulator with a Quantum Variational Circuit. MR Images were classified as "demented" or "non-demented", with a classification accuracy of 97%, 5% higher than using Conventional Neural Networks without quantum metods. (7)
In Conclusion: Several other Quantum approaches to Medical Imaging are available in literature. The primary factor that will improve accuracies and effectiveness is the emergence of new quantum computers with higher numbers of qubits and lower error rates.
National Science Foundation's 2023 practical quantum computer forecast will likely assist Medical Imaging Analysis. MIT's Technology Review reflects this sentiment, stating that researchers will now be consolidating their years of hard work - as better hardware is realized. Lastly, The February 2023 TIME Magazine Cover prominently features a quantum computer, with the caption "The Future of Computing is Here". (8-10)
References are available in the comments below. Have a productive rest of your day.
Smile at end!
2/3/23 "Quantum Computing and Medical Imaging Industry Results" References[1] https://www.nature.com/articles/s41567-019-0648-8[2] https://journals.aps.org/prx/abstract/10.1103/PhysRevX.11.041011 [3] https://arxiv.org/ftp/arxiv/papers/2212/2212.08693.pdf[4] https://www.sciencedirect.com/science/article/pii/S0306456522002182[5] https://ieeexplore.ieee.org/document/9779984[6] https://arxiv.org/abs/2109.01831[7] https://www.mdpi.com/2079-9292/11/5/721[8] https://nsf.gov/news/factsheets/Factsheet_Quantum-proof7_508.pdf[9] https://www.technologyreview.com/2023/01/06/1066317/whats-next-for-quantum-computing/[10] https://time.com/6249784/quantum-computing-revolution/
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaHealthcare Innovation
In 2019, Harvard researchers published an article in Nature Physics titled "Quantum convolutional neural networks", "QCNNs" which highlighted their benefits over conventional approaches. The authors explained that Today's neural networks used for image processing and precision medicine are lacking in the area of quantum physics problems (1)
In 2021 an APS paper asserted that issues associated with higher quantum bits "qubits" such as Barren plateaus can be eliminated in some cases utilizing QCNNs. (2) In Modern Day publications, Instantaneous Quantum Polynomial (IQP) encoding is used because of its ability to Entangle qubits for exponential calculation capacity in Real Quantum Computers. (3)
In a 2023 Journal of Thermal Biology paper, a 2 qubit simulator was used with Quantum Kernel Aignment to classify Rheumatoid Arthritis hand thermal images as "normal" or "RA". Implementing a Quantum Support Vector Machine "QSVM", an accuracy of 92.7% was achieved. (4) In 2022 IBM and Amgen utilized a 27 qubit computer with other hybrid techniques to preliminarily predict the persistance of Rheumatoid Arthritis patients. The goal of the paper was to better understand whether a given dataset would exhibit quantum advantage utilizing Explainable AI/Shapley value methods. Using a QSVM, Empirical predictions were found to be as good as classical computers. (5) In 2022, Roche and QCware performed Medical Image classification in 2 areas of Health utilizing a 16 qubit IBM computer, and a quantum simulator - Both with Quantum Neural Networks. Chest images were identified as “healthy” or “pneumonia-infected” having slightly better accuracy with quantum methods. Retinopathy images were classified as “normal” or “different levels” from the MedMNIST database and accuracies were slightly better than classical methods. (6)
A 2022 article titled “Automated Binary Classification of Alzheimer’s via Hybrid Classical Quantum Neural Networks” featured a 4 qubit Pennylane hybrid simulator with a Quantum Variational Circuit. MR Images were classified as "demented" or "non-demented", with a classification accuracy of 97%, 5% higher than using Conventional Neural Networks without quantum metods. (7)
In Conclusion: Several other Quantum approaches to Medical Imaging are available in literature. The primary factor that will improve accuracies and effectiveness is the emergence of new quantum computers with higher numbers of qubits and lower error rates.
National Science Foundation's 2023 practical quantum computer forecast will likely assist Medical Imaging Analysis. MIT's Technology Review reflects this sentiment, stating that researchers will now be consolidating their years of hard work - as better hardware is realized. Lastly, The February 2023 TIME Magazine Cover prominently features a quantum computer, with the caption "The Future of Computing is Here". (8-10)
References are available in the comments below. Have a productive rest of your day.
Smile at end!
2/3/23 "Quantum Computing and Medical Imaging Industry Results" References[1] https://www.nature.com/articles/s41567-019-0648-8[2] https://journals.aps.org/prx/abstract/10.1103/PhysRevX.11.041011 [3] https://arxiv.org/ftp/arxiv/papers/2212/2212.08693.pdf[4] https://www.sciencedirect.com/science/article/pii/S0306456522002182[5] https://ieeexplore.ieee.org/document/9779984[6] https://arxiv.org/abs/2109.01831[7] https://www.mdpi.com/2079-9292/11/5/721[8] https://nsf.gov/news/factsheets/Factsheet_Quantum-proof7_508.pdf[9] https://www.technologyreview.com/2023/01/06/1066317/whats-next-for-quantum-computing/[10] https://time.com/6249784/quantum-computing-revolution/
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaHealthcare Innovation
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