Internal:Quantum Machine Learning

Quantum convolutional neural networks for high energy physics data analysis

Chen et al., Brookhaven National Labs

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).