Quantum Machine Learning Algorithms for Medical School Research PDF + Discussion 1/18/24.
Artificial Intelligence for Medical is Increasingly Applied and Researched. This is apparent through 692 FDA Approved AI/ML Enabled Software as Medical Devices (SaMD) as of October 2023; and also regarding Stanford Medicine, UCSF/Berkeley, and UCLA School of Medicine investments and studies likely demonstrating the future of Medical AI.
Quantum Artificial Intelligence Innovation with Medical Images has seen development by many other researchers through classical Deep Learning Networks combined with Trainable QML circuits for hybrid applications and custom datasets. This includes Covid-19, Arthritis, Alzheimer’s, Parkinson’s, and Brain Tumor datasets using a variety of QML methodologies.
Quantum Machine Learning Templates/Ansatz can be used as suitable quantum embeddings and variational layers. In addition, Quantum Tensor Networks, Quantum Circuits in Neural Networks, and Circuit Cutting help address qubit limitations. Lastly, designing Efficient GPU Quantum ML Algorithms increases the likelihood of success vs. using Traditional ML Algorithms. Discussion.