- HCQDL uses quantum circuits to nonlinearly transform classical inputs into features that can then be used in a number of deep learning algorithms. HCQDL consists of classical layer, quantum layer, and fully connection layer. Please refer to
Hybrid_CNN.py
- The first function of SP&A-Net is the self-proliferation, using a series of linear transformations to generate more feature maps at a cheaper cost. We can train image classifier in a more efficient way. Please refer to
Self_Proliferate.py
- The second function is self-attention, capturing the long-range dependencies of the feature map using the channel-wise and spatial attention mechanism. Please refer to
Self_Attention.py
- SP&A Block please refer to
Self_Proliferate_and_Attention.py
The quantum layer implemented by various quantum circuit built in the continuous-variable architecture. It consists of three consecutive parts (as shown in Figure 8). An encoding circuit encodes classical data to states of the qubits followed by a parametrized quantum circuit (PQC) that is applied to transform these states to their optimal location on the Hilbert space. Please refer to quantum_circuit.py
python -m pip install -r requirements.txt
This code was tested with python 3.7
Self_Proliferate.py is used to generate more feature maps (As paper section 3.1).
Self_Attention.py is used to capturing the long-range dependencies of the feature map (As paper secton 3.1).
Self_Proliferate_and_Attention.py follow the spirit of MobileNet, "capture features in high dimensions and transfer information in low dimensions", to make the network more efficient. (As paper secton 3.1).
quantum_circuit.py is one sample of Parametrized Quantum Circuit (PQC). Please refer section 3.2 of this paper.
Hybrid_CNN.py is the architecture of Hybrid Classical-Quantum Deep Learning.
CircleLoss.py is used to estimate the loss rate during model training with two elemental deep feature learning approaches: class-level labels and pair-wise labels.
