Both of the .ipynb files under the software directory can be run by opening them in Google Colab, and running all cells. bQCNN-TensorflowQuantum.ipynb includes all of the source code for our main software implementation with TensorFlow Quantum. bQCNN-Qiskit.ipynb includes the source code for our attempt to implement the circuit with Qiskit. The Qiskit code does not include a full implementation of the original paper, but does detail our attempts to reproduce the original results.
A majority of the software implementation was completed through pair programming. However, the general individual contributions are as follows:
- Rain Zhang:
- Created the Qiskit notebook
- Developed the Qiskit functionality for creating the convolution layer to reflect the original paper's convolution layer architecture
- Amir Barkam:
- Developed the Qiskit functionality for including branching in the pooling layers and performing mid-circuit measurements on a subset of qubits in the pooling layers
- Arnold Ying:
- Developed the TensorFlow Quantum functionality for implementing the convolution layers to reflect the original paper's implementation
- Developed the TensorFlow Quantum code for branching in the architecture's pooling layers
- Stella Wang:
- Developed the TensorFlow Quantum functionality for generating random excitations of the cluster state and random 4-pixel images
- Developed the Tensorflow Quantum functionality for running gradient descent on the bQCNN