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New tutorial/example for qGAN #339

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@jaeyoo jaeyoo Aug 22, 2020 •
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some minor typos:

arbitrary

an n-qubit

A qGAN is a quantum version of ...

Well, you said p θ j is related to the probability of the state j, but it is the probability of the state j.


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Initialize qubits.

qubits = cirq.GridQubit.rect(1, num_qubits)


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@jaeyoo jaeyoo Aug 22, 2020 •
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mu = 1

sigma = 1

why do you -0.5 to 2**num_qubits? why don't use just < rather thatn <= with arbitrary number 0.5?

There are many typos.

Please replace all continous to continuous

hardardman --> Hadamard

intialise --> initialize

Lint issues. please keep the number of characters in a line under 80. For example,

# Discretize the remaining samples so the continous distribution can be
# approximated by a discrete distribution

discrete_data = tf.convert_to_tensor(np.digitize(

continous_data, [i - 0.5 for i in range(1, 2**num_qubits)]),

dtype=tf.dtypes.int32)

what's the purpose of this line? it uses the list of bin values [0.5, 1.5, 2.5, 3.5] for num_qubits = 2. why not use [0., 1.0, 2.0, 3.0, 4.0] with right=True option in np.digitize ?

# Convert the decimal into binary tensor

discrete_data = tf.cast(tf.math.mod(tf.bitwise.right_shift(

tf.expand_dims(discrete_data, 1), tf.range(num_qubits)), 2),

dtype=tf.float32)

 

Also, we can clean up the last for-loop code.

noise = tfq.convert_to_tensor(

[cirq.Circuit(cirq.H.on_each(qubits)) for _ in range(size)])

 



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Why the result have 3 qubits even if num_qubits = 2 and you mentioned "2-qubit example"


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@jaeyoo jaeyoo Aug 22, 2020 •
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Use format

theta = sympy.symbols(f'a0:{num_qubits}')

Lint please.

inputs = tf.keras.Input(shape=(), dtype=tf.dtypes.string)

Simplify it

 parameterized_circuit = cirq.Circuit([cirq.ry(t)(q) for t, q in zip(theta, qubits)])

Line please. < 80 characeters

# Entangle all the qubits by applying CZ in a circular fashion 
# except when there are only two qubits and then just apply one CZ

Simplify it.

entangle_circuit = cirq.Circuit(
    [cirq.CZ(q1, q2) for q1, q2 in zip(qubits[0:-1], qubits[1:])])

if(num_qubits > 2):

entangle_circuit = cirq.Circuit(cirq.CZ(qubits[0], qubits[-1]))

 

Lint please.

# Add this circuit layer to the network with an output on measurements on


in the Z component. Manipulate the output so it maps the -1, 1 outputs


to 0, 1 like the binary discrete data generated by generate_data

 

Simplify it. and saying "Important to have repetition = 1" is not helpful at all. could you describe the real reason?

observables = [(cirq.Z(q)+1)/2 for q in qubits]


[describe the real reason why it is important to have repetition = 1]


layer = tfq.layers.PQC(layer_circuit, observables, repetitions=1)(inputs)

model = tf.keras.Model(inputs=[inputs], outputs=[layer])


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This is not a good place to have this function. discriminator_gen_outputs is used before assignment. could you describe the definition in the new text block for users?

Also, please DO NOT use the python keyword sum as a local variable. please rename it as loss for example.

Also, could you use tf.math.reduce_mean. it will help you simplify the code. e.g. you don't have to use 1/m.


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Hmm, you have input layer with 2 input nodes, 2 hidden layers with 50 and 20 nodes respectively, and output layer with 1 output node


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Please remove all the commented lines if you don't use anymore.

As you can see, make_discriminator_model doesn't exist, so uncommenting the line causes error, which is not helpful to users for their debug.


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Lint please / use tf.math.reduce_mean, sum->loss, and please describe the definition of discriminator_gen_outputs in the new text block.


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@jaeyoo jaeyoo Aug 22, 2020 •
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Again, discriminator_real_outputs and discriminator_gen_outputs are used before their assignments. Please describe them in text block.

Also, I don't understand why you require the following 4 tensors here.

zeros = tf.zeros(sum_tensor.shape)
ones = tf.ones(sum_tensor.shape)
twos = tf.ones(sum_tensor.shape) * 2
threes = tf.ones(sum_tensor.shape) * 3

Please don't create this tensor inside this function, just create and use directly in the main training loop.

Simplify the code.

sum_tensor = tf.reduce_sum(tf.map_fn(lambda t: t * 2 ** tf.range(tf.cast(gan_model.generated_data.shape[1], dtype=tf.int64)), tf.cast(tf.reverse(tensor=gan_model.generated_data, axis=[1]), dtype=tf.int64)), axis=1)

-->

# Convert binary to decimal
def binary_to_decimal(discrete_data):
  return tf.cast(tf.math.reduce_sum(tf.bitwise.left_shift(
      tf.cast(discrete_data, dtype=tf.int64), tf.range(num_qubits)), axis=1),
      dtype=tf.float32)

sum_tensor = binary_to_decimal(gan_model.generated_data)


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I don't know if I am generating the correct format of data. I have tried a whole mix of options from averaging over a number of repetitions to the frequency of occurance of each number but I am not sure what is correct. This version seemed to make the most sense to me as the discriminator will see a sample which is a set of outputs of a circuit with the generator weights and so should be able to deduce the distribution from that. I thought that averaging might lose information about the distribution.


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I need to change this to reflect the fact I removed the layer with 50 nodes. Though I'm not sure if I should add it back as removing it didn't seem to make a difference.


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I need to add an explaination of how this all works. However, it currently doesn't work, so I cannot write it yet


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If it was learning correctly these graphs would show it tending towards a lognormal distribution. However, as you can see it isn't learning correctly. I have tried fiddling around with the learning rate etc. but I can't seem to fix it. Also, I don't think the entropy calculation is working correctly but it isn't necessary - I might just remove it.


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