Quantum Neural Networks
Quantum Autoencoder
- class QuantumAutoEncoder(n_qubits=4, n_aux_qubits=1, n_latent_qubits=1, device='default.qubit')[source]
Bases:
torch.nn.modules.module.Module,qlearnkit.nn.qml_mixin.QmlMixinHybrid Quantum AutoEncoder, miming pytorch’s API and exploiting
TorchLayer.References:
- [1] Romero et al.,
Quantum autoencoders for efficient compression of quantum data
- Parameters
n_qubits – int, the actual number of qubits used in the training. It’s the n of the paper formalism.
n_aux_qubits – int, the number of auxiliary qubits, used as ancillas in the swap test.
n_latent_qubits – int, the number of qubits that can be discarded during the encoding.
device – Can be a string representing the device name or a valid
Devicehavingn_qubitswires
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]
Forward propagation pass of the Neural network.
- Parameters
x –
Tensor,input to be forwarded through the autoencoder
- Returns
Array containing model’s prediction.
- training: bool
QML Mixin
Quantum LSTM
- class QLongShortTermMemory(input_size, hidden_size, n_layers=1, n_qubits=4, batch_first=True, device='default.qubit')[source]
Bases:
torch.nn.modules.module.Module,qlearnkit.nn.qml_mixin.QmlMixinHybrid Quantum Long Short Term Memory, miming pytorch’s API and exploiting
TorchLayer.Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.
For each element in the input sequence, each layer computes the following function:
\[\begin{split}\begin{array}{ll} \\ i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{t-1} + b_{hi}) \\ f_t = \sigma(W_{if} x_t + b_{if} + W_{hf} h_{t-1} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{t-1} + b_{hg}) \\ o_t = \sigma(W_{io} x_t + b_{io} + W_{ho} h_{t-1} + b_{ho}) \\ c_t = f_t \odot c_{t-1} + i_t \odot g_t \\ h_t = o_t \odot \tanh(c_t) \\ \end{array}\end{split}\]where \(h_t\) is the hidden state at time t, \(c_t\) is the cell state at time t, \(x_t\) is the input at time t, \(h_{t-1}\) is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and \(i_t\), \(f_t\), \(g_t\), \(o_t\) are the input, forget, cell, and output gates, respectively. \(\sigma\) is the sigmoid function, and \(\odot\) is the Hadamard product.
References:
- [1] Chen et al.,
- Parameters
input_size – the number of expected features in the input x
hidden_size – the number of features in the hidden state h
n_layers – the number of recurrent layers
n_qubits – the number of qubits
batch_first – if
True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, length)device – Can be a string representing the device name or a valid
Devicehavingn_qubitswires
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(input, hx=None)[source]
Forward propagation pass of the Recurrent Neural network using a LSTM cell
- Parameters
input – the input at time \(t\)
hx – Tensor containing the hidden state \(h(t)\) and the cell state \(c(t)\) at time \(t\)
- Returns
the hidden sequence at time \(t+1\) and the new couple \(h(t+1)\), \(c(t+1)\).
- training: bool
Quanvolutional Layer
- class Quanv2DLayer(n_qubits=4, kernel_size=(2, 2), stride=2, out_channels=4, n_layers=1, device='default.qubit')[source]
Bases:
torch.nn.modules.module.Module,qlearnkit.nn.qml_mixin.QmlMixinQuantum Convolution Layer for Pytorch Hybrid Architectures
References:
- [1] Henderson et al.,
Quanvolutional Neural Networks: Powering Image Recognition with Quantum Circuits
- Parameters
n_qubits – int, The number of qubits of the quantum node. Default: 4.
kernel_size – Size of the convolutional kernel. Default: (2,2).
stride – Stride of the convolution. Default: 1
out_channels – Number of channels produced by the quanvolution. Default: 4.
n_layers – int, the number of layers of the variational quantum circuit. Default: 1.
device – Can be a string representing the device name or a valid
Devicehavingn_qubitswires. Default ‘default.qubit’
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- quanvolve(inputs, in_channels=1)[source]
Applies a single channel 2D quantum convolution (quanvolution)
- Parameters
inputs – one channel input batch
in_channels – number of channel in the original input image
Returns:
- training: bool