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+[Parallel Quantum Algorithms Innovation: PyTorch, Keras, QiML](https://drive.google.com/file/d/1-_KxLH4RACFtPiXsYIUu62DtjsQgvRIe/view?usp=drive_link) PDF, [Mobile Version](https://www.chemicalqdevice.com/parallel-quantum-algorithms-innovation-pytorch-keras) 12/14/23. 
+
+The following experiments indicate that parallel architectures for running multitudes of low qubit quantum algorithms are a very promising solution to access very high total qubit counts for classification tasks. This method is inaccessible to larger single state vector algorithms that experience exponential increases in Time and RAM. 
+1) Algorithms 
+2) Runtime/RAM
+3) Batch size
+4) Learning Rate
+5) Dataset Noise 
+6) Overfitting