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+[ChemicalQDevice Quantum Medical Image Analysis Platform](https://www.chemicalqdevice.com/chemicalqdevice-quantum-medical-image-analysis-platform) PDF 6/19/23.
+
+Introduction: The purpose of the Platform is to develop, test, and implement quantum algorithms into more complex hybrid medical
+image analysis models. This will be accomplished by utilizing quantum algorithm advancements, ML strategy, and industry resources. <br>
+
+Project #1 introduced an increase in image classes to 44 and dataset size to 4478 compared to previous iterations, with challenges in
+more complex data analysis. Several classes contained less than 20 images to train from, which may not provide a good
+representation of the brain tumor type for validation of unseen images, as shown in Figure 1. The other limitation in the study was that
+the number of available qubits and quantum circuit were limited to 20, which did not match the original model guidelines. <br>
+
+The “Quantum transfer learning” model states a 2:1 ratio between number of qubits:image classes. For best performance in previous
+works, 8 qubits were used for 4 classes and 20 qubits were used for 10 classes; with 20 qubits maxing available resources due to
+model complexity. Therefore, the number of optimal qubits and quantum circuit qubits for 44 classes should be 88. Figure 2,
+normalized confusion matrix, illustrates a vertical “striping” between classes 25 and 32 likely due to not using ideal qubit numbers.<br>
+
+Figures 3, 4, 5 are different combinations of machine learning hyperparameters for the same quantum algorithm 1. “A” weight decay
+hyperparameters yielded the best results. Variants B and C were prohibited in training likely due to a gamma lr scheduler value that
+was too high when utilizing learning rate reduction after a given epoch. Figures 6 and 7 illustrate two other smaller algorithms with
+identical results to each other, due to having the same subpar sinusoidal function as revealed in algorithm testing. <br>
+
+Additional model performance for this dataset can be expected by implementing 1) tensor network quantum circuits, 2) hybrid state
+vector/tensor network quantum circuits, or 3) state vector algorithms with information and operation being local - to reach 88 qubits.
+These methods have restrictions on quantum circuit types, and may require collaboration with vendors. In addition, datasets with
+similar or higher classes consisting of larger numbers of images can be substituted for improved model training. <br>
+
+A focus is on integrating testing methods from leading platforms for better quantum algorithm candidates to use in upcoming models.