Internal:Quantum Machine Learning: Difference between revisions
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https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.013231 | https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.4.013231 | ||
The QCNN follows the following protocol: | The (SIMULATED) QCNN follows the following protocol: | ||
1. Amplitude(?) encoded particle images of size 30x30 are implemented in the encoding layer. | 1. Amplitude(?) encoded particle images of size 30x30 are implemented in the encoding layer. | ||
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5. Back Prop to adjust the parameters in the VQC's (convolutional layers). | 5. Back Prop to adjust the parameters in the VQC's (convolutional layers). | ||
We can implement this "by hand" in qiskit using their approximate expectation function on a Wahab GPU node using simulator.set_options(device='GPU'). | |||
Revision as of 20:20, 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 (SIMULATED) 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).
We can implement this "by hand" in qiskit using their approximate expectation function on a Wahab GPU node using simulator.set_options(device='GPU').