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01a QNNs 0.405 Weights kkawchak.ipynb
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02a Classifier & Regressor 85% kkawchak.ipynb
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03a Train Real Dataset 82% Test kkawchak.ipynb
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04a Kernel ML 100% Reg kkawchak.ipynb
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05a PyTorch qGAN LowEntropy kkawchak.ipynb
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06a Torch Connector 100% kkawchak.ipynb
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07a QSVM 2xData 16.6s kkawchak.ipynb
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08a1 Kernel 6Loss 100% kkawchak.ipynb
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08a2 Kernel 10Loss 100% kkawchak.ipynb
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08a3 Kernel 8.5Loss 90% kkawchak.ipynb
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08a4 Kernel 14Loss 90% kkawchak.ipynb
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09a Qiskit ML Model 90%Test kkawchak.ipynb
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10a1 Eff. Dimension .985Local kkawchak.ipynb
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10a2 Eff. Dimension .965Local kkawchak.ipynb
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10a3 Eff. Dimension .945Local kkawchak.ipynb
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10a4 Eff. Dimension .97Local kkawchak.ipynb
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10a5 Eff. Dimension .98Local kkawchak.ipynb
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10a6 Eff. Dimension .955Local kkawchak.ipynb
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11a QCNN 2xIter 100% 100% kkawchak.ipynb
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12a Autoencoder 0.999 Fidelity kkawchak.ipynb
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13a Equivariant Graph 1.21s kkawchak.ipynb
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14a Time Series RY 70% RM1 kkawchak.ipynb
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15a Geometric QML HRYEmbed kkawchak.ipynb
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16a Generalization Acc N greater than 5 kkawchak.ipynb
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17a Function Fitting 0.26 Loss kkawchak.ipynb
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18a Many Body Low Error kkawchak.ipynb
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19a1 A100 Lightning.gpu 27.52s kkawchak.ipynb
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19a2 V100 Lightning.gpu 35.17s kkawchak.ipynb
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19a3 CPU Default.qubit 197.77s kkawchak.ipynb
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20a Tensor Network 20 Qubit kkawchak.ipynb
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21a Approximate Kernel 7Wires kkawchak.ipynb
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22a Quantum GANs 600Iter kkawchak.ipynb
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23a Unitary Designs Fid. +.99 kkawchak.ipynb
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24a1 Kernels 100% No Train kkawchak.ipynb
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24a2 Kernels 50% No Train kkawchak.ipynb
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25a Haar -2+2 Params kkawchak.ipynb
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26a Learn QNNs 14nodes kkawchak.ipynb
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27a1 A100, Adjoint 44.72s kkawchak.ipynb
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27a2 CPU P. Shift 367.29s kkawchak.ipynb
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28a Keras Layers 86% Acc kkawchak.ipynb
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29a Torch Layers 99.5% kkawchak.ipynb
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30a Fourier Cost 0.00022 kkawchak.ipynb
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31a Quantum Graph RNN 8of8 kkawchak.ipynb
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32a Quanvolutional NN 83.3% kkawchak.ipynb
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33a1 A100 Light.gpu,qsim 4.10s kkawchak.ipynb
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33a2 CPU Light.qubit,qsim 4.27s kkawchak.ipynb
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33a3 CPU Def.qubit,qsim 5.55s kkawchak.ipynb
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34a Quantum TL 90.9% kkawchak.ipynb
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35a Photonic QNN 0.0109 Loss kkawchak.ipynb
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36a1 GAN A100 Light.gpu 10.10s kkawchak.ipynb
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36a2 GAN CPU Light.qub 11.91s kkawchak.ipynb
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37a Data-Reuploading 90.6% kkawchak.ipynb
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38a1 A100 0.24 Cost 259.78s kkawchak.ipynb
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38a2 CPU 0.24 Cost 278.03s kkawchak.ipynb
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LICENSE.md
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README.md
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Datasets
Models
