Closed sychang42 closed 3 years ago
I was trying to apply "Phase shift rule" for gradient calculation in quantum GAN using the tutorial : https://qiskit.org/documentation/tutorials/machine_learning/04_qgans_for_loading_random_distributions.html.
To do so, I replaced the line :
qgan.set_generator(generator_circuit=g_circuit, generator_init_params=init_params)
by
qgan.set_generator(generator_circuit=g_circuit, generator_init_params=init_params, generator_gradient = Gradient())
with qiskit.aqua.operators.gradients.Gradient.
qiskit.aqua.operators.gradients.Gradient
However, while running
result = qgan.run(quantum_instance)
with reps = 2 (2 blocks in the quantum generator), the code shows me an error message below :
reps = 2
ValueError Traceback (most recent call last) ~\Anaconda3\lib\site-packages\qiskit\aqua\components\neural_networks\quantum_generator.py in loss(self, x, weights) 382 # pylint: disable=no-member --> 383 loss = (-1) * np.dot(np.log(x).transpose(), weights) 384 except Exception: # pylint: disable=broad-except <__array_function__ internals> in dot(*args, **kwargs) ValueError: shapes (4,1) and (6,4) not aligned: 1 (dim 1) != 6 (dim 0)
Please also find the full error message below
.
Please also find the code here.
In the tutorial 04_qgans_for_loading_random_distributions.ipynb,
1) Import gradient object :
from qiskit.opflow import Gradient
2) Replace the line :
qgan.set_generator(generator_circuit=g_circuit, generator_init_params=init_params, generator_gradient = Gradient())`
3) Replace the line :
var_form = TwoLocal(int(np.sum(num_qubits)), 'ry', 'cz', entanglement=entangler_map, reps=1)
var_form = TwoLocal(int(np.sum(num_qubits)), 'ry', 'cz', entanglement=entangler_map, reps=2)
4) Add two more values for init_params (array of size 6)
init_params
5) Run the quantum GAN.
By the chain rule, the gradient of the generator is calculated as :
(c.f. Appendix B in Supplementary Information: Quantum Generative Adversarial Networks for Learning and Loading Random Distributions)
As we are summing over , the matrix multiplication performed in
loss_gradients = self.loss(prediction_generated, analytical_gradients).real
(line 411 in quantum_machine_learning.algorithms.distribution_learners.qgan.quantum_generator)
should take the form :
where
,
However,
analytical_gradients = np.array(grad_object.assign_parameters(value_dict).eval())
in
def _convert_to_gradient_function(self, gradient_object, quantum_instance, discriminator)
currently returns:
(line 410 in quantum_machine_learning.algorithms.distribution_learners.qgan.quantum_generator)
so the code returns an error for matrix multiplication.
I think the analytical gradients should be transposed :
to
analytical_gradients = np.transpose(np.array(grad_object.assign_parameters(value_dict).eval()))
Closed in #77
adekusar-drl
commented
3 years ago adekusar-drl
commented
3 years ago
Information
What is the current behavior?
I was trying to apply "Phase shift rule" for gradient calculation in quantum GAN using the tutorial : https://qiskit.org/documentation/tutorials/machine_learning/04_qgans_for_loading_random_distributions.html.
To do so, I replaced the line :
by
with
qiskit.aqua.operators.gradients.Gradient.However, while running
with
reps = 2(2 blocks in the quantum generator), the code shows me an error message below :Please also find the full error message below
Steps to reproduce the problem
Please also find the code here.
In the tutorial 04_qgans_for_loading_random_distributions.ipynb,
1) Import gradient object :
2) Replace the line :
by
3) Replace the line :
by
4) Add two more values for
init_params(array of size 6)5) Run the quantum GAN.
What is the expected behavior?
By the chain rule, the gradient of the generator is calculated as :
(c.f. Appendix B in Supplementary Information: Quantum Generative Adversarial Networks for Learning and Loading Random Distributions)
As we are summing over
, the matrix multiplication performed in
(line 411 in quantum_machine_learning.algorithms.distribution_learners.qgan.quantum_generator)
should take the form :
where
However,
in
currently returns:
(line 410 in quantum_machine_learning.algorithms.distribution_learners.qgan.quantum_generator)
so the code returns an error for matrix multiplication.
Suggested solutions
I think the analytical gradients should be transposed :
to