Do you have any ideas about how to construct Recurrent Networks?
Thanks!
WeiHi @cubicgate,
Since PennyLane connects to both PyTorch and TensorFlow, you could use the recurrent network modules of either of those packages to create recurrent networks, and potentially include a quantum element.
Yes, I understand. You could begin with the RNN modules in either of those two ML libraries, then create a new subclass of these which has a quantum circuit at its heart. If you were to build the RNN by hand, it would be a lot more work. Those libraries will handle the RNN part, and PennyLane can be used for the quantum part
@nathan this sounds very interesting to me. do you know any resources i can refer to build upon this idea ? Thanks !
Hi @vijpandaturtle,
You can check out some PyTorch tutorials related to RNNs here:
- https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html
- https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html
For TensorFlow, there are some tutorials here:
Oh no i was looking for resources related to quantum circuits used in rnns
There aren’t any that I know of 
oh i see. thanks anyway !
Hi,
I’m trying to build a QRNN with Tensorflow for the prediction of a simple time series. Here is my code:
from numpy import array
from keras.models import Sequential
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import LSTM, LSTMCell, Input, RNN, Dense
from tensorflow.keras.models import Model
tf.random.set_seed(0)
def split_sequence(sequence, n_steps):
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the sequence
if end_ix > len(sequence)-1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]
# choose a number of time steps
n_steps = 3
# split into samples
X, y = split_sequence(raw_seq, n_steps)
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))
class customLSTM(LSTM):
def __init__(self, **kwargs):
super(customLSTM, self).__init__(**kwargs)
def build(self, input_shape):
super(customLSTM, self).build(input_shape)
def compute_output_shape(self, input_shape):
return input_shape
# define model
inputs1 = Input(shape=(3,1))
lstm1 = customLSTM(units=1, activation='relu')(inputs1)
model = Model(inputs=inputs1, outputs=lstm1)
opt = tf.keras.optimizers.Adam(learning_rate=0.1)
model.compile(opt, loss='mse')
model.fit(X, y, epochs=1000)
x_input = array([70.0, 80.0, 90.0])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input)
print(yhat)
In words, I created a subclass of Keras LSTM, which currently works as the “original” one. If I now want to insert a quantum circuit in it as suggested by @nathan, how should I modify it? I’m aware of the existence of KerasLayer and I already used it in other experiments, but I don’t know how to exploit it in this case.
Thanks in advance!
Hey @valeria!
One way you could insert a quantum circuit is just to add a KerasLayer that follows the standard LSTM layer, e.g., something like:
intermediate_dim = 4
layers = 2
dev = qml.device("default.qubit", wires=intermediate_dim)
@qml.qnode(dev)
def qnode(inputs, weights):
qml.templates.AngleEmbedding(inputs, wires=range(intermediate_dim))
qml.templates.StronglyEntanglingLayers(weights, wires=range(intermediate_dim))
return [qml.expval(qml.PauliZ(i)) for i in range(intermediate_dim)]
qlayer = qml.qnn.KerasLayer(qnode, {"weights": (layers, intermediate_dim, 3)}, output_dim=intermediate_dim)
inputs1 = Input(shape=(3,1))
lstm1 = LSTM(units=intermediate_dim, activation='relu')(inputs1)
lstm2 = qlayer(lstm1)
out = Dense(units=1, activation="relu")(lstm2)
model = Model(inputs=inputs1, outputs=out)
I tried this but was not getting much luck in training, possibly due to the outputs of the QNode being [-1, 1] which should be rescaled. Note that you can train successfully with
inputs1 = Input(shape=(3,1))
lstm1 = LSTM(units=intermediate_dim, activation='relu')(inputs1)
# lstm2 = qlayer(lstm1)
out = Dense(units=1, activation="relu")(lstm1)
so it should just be a case of getting the qlayer to integrate there.
In the code block you shared, you’re trying to do the more ambitious task of making the LSTM layer itself contain a quantum circuit. I’d say this is more of an open research question and it’s not clear to me how the quantum circuit would be incorporated. However, maybe there are some ideas on the literature?
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Thanks for your reply, @Tom_Bromley!
I tried to predict the same input data following your suggestion, in particular I defined:
n_modes = 1
n_if = n_modes * (n_modes - 1) // 2
fraction_train = 0.75
depth = 1
sdev = 0.05
cutoff = 5
reg_strength = 1
n_epochs = 400
dev = qml.device("strawberryfields.fock", wires=n_modes, cutoff_dim=cutoff, analytic=False)
@qml.qnode(dev)
def qnode(inputs, theta_1,phi_1,varphi_1,r,phi_r,theta_2,phi_2,varphi_2,a,phi_a,k):
qml.templates.DisplacementEmbedding(inputs, wires=range(n_modes), method='phase', c=0.1)
qml.templates.layers.CVNeuralNetLayers(theta_1,phi_1,varphi_1,r,phi_r,theta_2,phi_2,varphi_2,a,phi_a,k, wires=range(n_modes))
return [qml.expval(qml.X(wires = w)) for w in range(n_modes)]
weight_shapes = {"theta_1":[depth, n_if],"phi_1":[depth, n_if],"varphi_1":[depth, n_modes],"r":[depth, n_modes],
"phi_r":[depth, n_modes],"theta_2":[depth, n_if],"phi_2":[depth, n_if],"varphi_2":[depth, n_modes],
"a":[depth, n_modes],"phi_a":[depth, n_modes],"k":[depth, n_modes]}
unif_init = tf.random_uniform_initializer(minval=0, maxval=np.pi*2, seed=42)
normal_init = tf.random_normal_initializer(mean=0.0, stddev=sdev, seed=42)
reg = tf.keras.regularizers.l2(reg_strength)
weight_specs = {"theta_1": {"initializer": unif_init, "trainable": True}, "phi_1": {"initializer": unif_init, "trainable": True},
"varphi_1": {"initializer": unif_init, "trainable": True}, "r": {"initializer": normal_init, "trainable": True, "regularizer": reg},
"phi_r": {"initializer": unif_init, "trainable": True}, "theta_2": {"initializer": unif_init, "trainable": True},
"phi_2": {"initializer": unif_init, "trainable": True}, "varphi_2": {"initializer": unif_init, "trainable": True},
"a": {"initializer": normal_init, "trainable": True, "regularizer": reg}, "phi_a": {"initializer": unif_init, "trainable": True},
"k": {"initializer": unif_init, "trainable": True, "regularizer": reg}}
qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, weight_specs=weight_specs, output_dim=n_modes)
clayer1 = tf.keras.layers.LSTM(50, activation='relu', input_shape=(n_steps, n_features))
clayer2 = tf.keras.layers.Dense(1, activation='relu')
clayer3 = tf.keras.layers.Dense(1, activation='relu')
model = tf.keras.models.Sequential([clayer1, clayer2, qlayer, clayer3])
opt = tf.keras.optimizers.Adam(learning_rate=0.1)
model.compile(opt, loss='mse')
model.fit(X, y, epochs=n_epochs)
However, the results are not good… Instead, removing the qlayer results in a more efficient and accurate algorithm: maybe it is simply not convenient to use a quantum layer in this case.
Regarding my previous question on how to insert a quantum circuit inside LSTM, I have no clue how to do it and unfortunately I didn’t find anything helpful on the net, but I can try to think about it.
Thanks again
Thanks! I just took a look at the code and it runs fine for me, but I agree that the resulting trained model is not very accurate. I would guess a couple of possible factors that are influencing performance:
- The cutoff dimension - it’s possible that the model is learning to push outside of the cutoff dimension. This could be helped with increasing the cutoff at a cost of greater training time.
- The
clayer1toclayer2transition from 50 dimensions to 1 might be a bit too much of a compression, although I understand the choice of 1 is to match the number of modes in the qlayer. Perhaps an intermediate classical layer of, e.g., 10 neurons might help, again at a cost of increased training time. - The number of modes in the qlayer could also be increased, but this will result in the biggest overhead in training time. Perhaps two modes might be a compromise.
One option to counter increased training time could be to switch to an all-Gaussian based simulation and use strawberryfields.gaussian, which runs faster. This Gaussian limitation is quite a restriction, but it might let us see that at least the model can train with a quantum layer.
maybe it is simply not convenient to use a quantum layer in this case
Yes, something to always keep in mind! It might not make sense at this point. Perhaps a future run on hardware with multiple modes might be more performant.
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@Tom_Bromley, thanks for your answer! I will try to modify the code following your suggestions and really hope to see an improvement 
1 Like
yo, I was able to find this research paper regarding qRNN written by the xanadu team and MIT
I wanted to know if this is purely theoretical work, or if at any point this was implemented in code
Hi @cosmic!
Unfortunately this work has not been implemented using PL/SF. The underlying algorithm involves implementation of HHL, which has not yet been added to PL due to a focus on near-term variational quantum circuits.
Oh I see
thanks for the information!
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