qlstm_core.py

concat_size = inputs_dim + hidden_dim
clayer_in = torch.nn.Linear(concat_size, n_qubits)
VQC = [qml.qnn.TorchLayer(qlayer, weight_shapes) for _ in range(4)]
clayer_out = torch.nn.Linear(n_qubits, hidden_size)

hidden_seq = []
for t in range(seq_length):
    # get features from the t-th element in seq, for all entries in the batch
    x_t = x[:, t, :]
            
    # Concatenate input and hidden state
    v_t = torch.cat((h_t, x_t), dim=1)

    # match qubit dimension
    y_t = self.clayer_in(v_t)

    f_t = torch.sigmoid(self.clayer_out(self.VQC[0](y_t)))  # forget block
    i_t = torch.sigmoid(self.clayer_out(self.VQC[1](y_t)))  # input block
    g_t = torch.tanh(self.clayer_out(self.VQC[2](y_t)))  # update block
    o_t = torch.sigmoid(self.clayer_out(self.VQC[3](y_t))) # output block

    c_t = (f_t * c_t) + (i_t * g_t)
    h_t = o_t * torch.tanh(c_t)

    hidden_seq.append(h_t.unsqueeze(0))