Quantum-classical hybrid convolutional neural network for classical image classification quantum-computingimage-classificationconvolutional-neural-networksquantum-algorithmsquantum-machine-learning UpdatedMar 4, 2023 Python PennyLaneAI/pennylane-lightning Star101 ...
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Visualization functions for displaying matrices, quantum states, data etc.RequirementsQuantumUtils officially requires Mathematica 10.0.0 or newer. Most features should work with Mathematica 9, and full compatibility with Mathematica 9 will be added in the future. Older versions of Mathematica are not ...
We demonstrate the potential of this architecture by applying it to a Tetris dataset, and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.1 Introduction With the rapid progress in quantum computing hardware, we are...
Convolutional neural networks (CNNs) are classical ML models extensively used for image classification, speech recognition, etc (LeCun et al.2015; Schmidhuber2015). They consist of a sequence of convolutional and pooling layers followed by a fully connected layer at the end. The convolution operati...
quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed ...
fabricated the devices, performed the measurements and developed the program for applying CNN. P.C. and F.Z. performed the theoretical analysis and calculations. K.W. and T.T. synthesized the hBN crystals. F.Z. and F.X. supervised the project. C.M., S.Y., P.C., F.Z. and F.X...
For humans, the difficulty of analyzing quantum optical experiments goes far beyond that of other deep learning problems like, e.g., image classification. The network performs well even on unseen data beyond the training distribution, proving its extrapolation capabilities. This paves the way for ...
CNNs are one of the most widely used types of deep neural networks for image classification [17]. It consists of convolutional, pooling, and fully connected layers. The convolutional layer applies multiple filters to the input to create feature maps. The pooling layer reduces the dimensionality ...
Latest commit Git stats 76 commits Files Type Name Latest commit message Commit time data/rt-polaritydata .gitignore LICENSE README.md data_helpers.py eval.py text_cnn.py train.py README.md This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" ...