Internal:Quantum Machine Learning: Difference between revisions
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Created page with "= Quantum convolutional neural networks for high energy physics data analysis = === Chen et al., Brookhaven National Labs === The QCNN follows the following protocol: 1. Amp..." |
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= Quantum convolutional neural networks for high energy physics data analysis = | = Quantum convolutional neural networks for high energy physics data analysis = | ||
=== Chen et al., Brookhaven National Labs === | === Chen et al., Brookhaven National Labs === | ||
https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.013231 | |||
The QCNN follows the following protocol: | The QCNN follows the following protocol: | ||
Revision as of 20:17, 7 March 2023
Quantum convolutional neural networks for high energy physics data analysis
Chen et al., Brookhaven National Labs
https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.013231
The QCNN follows the following protocol:
1. Amplitude(?) encoded particle images of size 30x30 are implemented in the encoding layer.
2. A VQC acts as a convolutional layer. Convolutional outputs are calculated via the expectation value of the Pauli-Z operator acting on the first qubit.
3. Pooling is done classically with nonlinear activation functions in between convolutional layers (look deeper into this).
4. Categorical Cross Entropy Loss Function is applied to the final output.
5. Back Prop to adjust the parameters in the VQC's (convolutional layers).