2 
# Set upper and lower data values
bounds = np.array([0.,7.])
# Set number of qubits used in the uncertainty model
num_qubits = [3]

# Set entangler map
entangler_map = []
for i in range(sum(num_qubits)):
    entangler_map.append([i, int(np.mod(i+1, sum(num_qubits)))])

# Load the trained circuit parameters
g_params = [0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428, 0.13537225]

# Set an initial state for the generator circuit
init_dist = NormalDistribution(sum(num_qubits), mu=1., sigma=1., low=bounds[0], high=bounds[1])
init_distribution = np.sqrt(init_dist.probabilities)
init_distribution = Custom(num_qubits=sum(num_qubits), state_vector=init_distribution)

I want to know how to obtain the trained circuit parameters, which Attribute or Method should be used?

4

0 回答 0