Embedded Files
ChemicalQDeviceIon Trap Computing for Radiology Advancements
Dear Healthcare Colleague, my name is Kevin Kawchak, and I am the Founder CEO of ChemicalQDevice. Today is February 17th, 2023 for "Ion Trap Plus Quantum Simulators for Radiology Advancements"
Significant research continues to be performed in Medical Radiology Classification tasks with Ion-Trap Quantum Computers. In specific, IonQ, Duke, and Quantinuum willl be providing detailed information regarding the technology at the APS March 2023 Meeting. Researchers utilizing IonQ technology over time have offered classification and an Associative adversarial network to Existing Classical Generative Adversarial Networks. Duke University continues to partner with Sandia National Laboratories, L3Harris, and AOSense for further progress. Quantinuuum continues to evolve mid circuit measurement, low cross talk, and Quantum Error Correction. (1-5)
Quantum Simulators have also been explored with adversarial attacks for progress regarding Explainable artificial intelligence. A Pixel and a Voxel based Scalable Quantum Convolutional Networks both offer opportunity in requiring less qubits by utilizing more features to curtail Barren Plateaus. In these promising systems, Reverse Fidelity Training and Data re-uploading presents more flexible quantum neural networks. The 3D voxelized version referred to as sQCNN-3D provides hope for quantum technologies with applications such as MRI Raw Data processing. (6-8)
In addition, A Fully Connected Quantum Convolutional Neural Network has been progressed using quantum simulators. This method provides O(log(n)) depth for n qubits, reducing the number of parameters for Electronic Health Records with less Epochs And better accuracy compared to a standard Fully Connected Neural Network and an Optimized Convolutional Neural Network. (9)
Several Alzheimer's Disease quantum simulator studies with Kaggle and ADNI Data Bases have been used for Alzheimer's Disease Neuroradiology over the Author's Classical Classification Methods improved from approximately 91% to 98% for binary classification. (10-12)
References are available in the comments below. Have a productive rest of your day.
Smile at end!
2/17/23 "Ion Trap Plus Quantum Simulators for Radiology Advancements" References[1] https://meetings.aps.org/Meeting/MAR23/Session/M64.3 [2] https://meetings.aps.org/Meeting/MAR23/Session/B67.1[3] https://meetings.aps.org/Meeting/MAR23/Session/Y69.5[4] https://www.nature.com/articles/s41534-021-00456-5[5] https://journals.aps.org/prx/abstract/10.1103/PhysRevX.12.031010[6] https://www.hindawi.com/journals/que/2023/2842217/ [7] https://arxiv.org/abs/2209.12372[8] https://arxiv.org/abs/2210.09728[9] https://ieeexplore.ieee.org/document/9999181[10] https://www.mdpi.com/2079-9292/11/5/721 [11] https://arxiv.org/pdf/2301.12505.pdf [12] https://www.mdpi.com/2227-7390/11/2/376
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaHealthcare Innovation
Significant research continues to be performed in Medical Radiology Classification tasks with Ion-Trap Quantum Computers. In specific, IonQ, Duke, and Quantinuum willl be providing detailed information regarding the technology at the APS March 2023 Meeting. Researchers utilizing IonQ technology over time have offered classification and an Associative adversarial network to Existing Classical Generative Adversarial Networks. Duke University continues to partner with Sandia National Laboratories, L3Harris, and AOSense for further progress. Quantinuuum continues to evolve mid circuit measurement, low cross talk, and Quantum Error Correction. (1-5)
Quantum Simulators have also been explored with adversarial attacks for progress regarding Explainable artificial intelligence. A Pixel and a Voxel based Scalable Quantum Convolutional Networks both offer opportunity in requiring less qubits by utilizing more features to curtail Barren Plateaus. In these promising systems, Reverse Fidelity Training and Data re-uploading presents more flexible quantum neural networks. The 3D voxelized version referred to as sQCNN-3D provides hope for quantum technologies with applications such as MRI Raw Data processing. (6-8)
In addition, A Fully Connected Quantum Convolutional Neural Network has been progressed using quantum simulators. This method provides O(log(n)) depth for n qubits, reducing the number of parameters for Electronic Health Records with less Epochs And better accuracy compared to a standard Fully Connected Neural Network and an Optimized Convolutional Neural Network. (9)
Several Alzheimer's Disease quantum simulator studies with Kaggle and ADNI Data Bases have been used for Alzheimer's Disease Neuroradiology over the Author's Classical Classification Methods improved from approximately 91% to 98% for binary classification. (10-12)
References are available in the comments below. Have a productive rest of your day.
Smile at end!
2/17/23 "Ion Trap Plus Quantum Simulators for Radiology Advancements" References[1] https://meetings.aps.org/Meeting/MAR23/Session/M64.3 [2] https://meetings.aps.org/Meeting/MAR23/Session/B67.1[3] https://meetings.aps.org/Meeting/MAR23/Session/Y69.5[4] https://www.nature.com/articles/s41534-021-00456-5[5] https://journals.aps.org/prx/abstract/10.1103/PhysRevX.12.031010[6] https://www.hindawi.com/journals/que/2023/2842217/ [7] https://arxiv.org/abs/2209.12372[8] https://arxiv.org/abs/2210.09728[9] https://ieeexplore.ieee.org/document/9999181[10] https://www.mdpi.com/2079-9292/11/5/721 [11] https://arxiv.org/pdf/2301.12505.pdf [12] https://www.mdpi.com/2227-7390/11/2/376
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaHealthcare Innovation
Report abuse