About the input of quantum neural network in tensorflow quantum

Question

I created a quantum neural network using tensorflow quantum，It's input is a tensor converted by circuit.About this input circuit，I found that if the parameters of the circuit are also specified by tensors, the quantum neural network cannot be trained.

The circuit when using normal parameters can make the network train normally

theta_g=1
blob_size = abs(1 - 4) / 5
spread_x = np.random.uniform(-blob_size, blob_size)
spread_y = np.random.uniform(-blob_size, blob_size)
angle = theta_g + spread_y
cir=cirq.Circuit(cirq.ry(-angle)(qubit), cirq.rx(-spread_x)(qubit))
discriminator_network(tfq.convert_to_tensor([cir]))

But when I use the following code, the quantum neural network cannot be trained

theta_g=tf.constant([1])
blob_size = abs(1 - 4) / 5
spread_x = np.random.uniform(-blob_size, blob_size)
spread_y = np.random.uniform(-blob_size, blob_size)
spred_x = tf.constant(spread_x)
spred_y = tf.constant(spread_y)
angle = theta_g + spread_y
cir=cirq.Circuit(cirq.ry(-angle)(qubit), cirq.rx(-spread_x)(qubit))
discriminator_network(tfq.convert_to_tensor([cir]))

** the disciminator_network**

def discriminator():
    theta = sympy.Symbol('theta')
    q_model = cirq.Circuit(cirq.ry(theta)(qubit))
    q_data_input = tf.keras.Input(
        shape=(), dtype=tf.dtypes.string)
    expectation = tfq.layers.PQC(q_model, cirq.Z(qubit))
    expectation_output = expectation(q_data_input)

    classifier = tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid)
    classifier_output = classifier(expectation_output)
    model = tf.keras.Model(inputs=q_data_input, outputs=classifier_output)
    return model

Answer 1

Without being able to see the trace of the error you are getting, I would say that I think the problem you are running into in the second snippet is that you have placed tf.constant objects into the placeholders of the cirq.Circuit. The reason your first example works is because cirq.Circuits know how to interpret values from np.float32 datatypes. Cirq does not know how to interpret values from tf.float32 (or any tf.dtypes.* for that matter).

TensorFlow Quantum's entry point to interface tensorflow datatypes with cirq.Circuit objects is via resolving the sympy.Symbol values inside of the circuits in tfq native operations (which you have done in creating the tfq.layers.PQC).

Does this help clear things up ?

-Michael