import json
import pennylane as qml
import pennylane.numpy as np
np.random.seed(1967)
def get_matrix(params):
"""
Args:
- params (array): The four parameters of the model.
Returns:
- (matrix): The associated matrix to these parameters.
"""
alpha, beta, gamma, phi = params
# Put your code here #
# Return the matrix
def error(U, params):
"""
This function determines the similarity between your generated matrix and
the target unitary.
Args:
- U (np.array): Goal matrix that we want to approach.
- params (array): The four parameters of the model.
Returns:
- (float): Error associated with the quality of the solution.
"""
matrix = get_matrix(params)
# Put your code here #
# Return the error
def train_parameters(U):
epochs = 1000
lr = 0.01
grad = qml.grad(error, argnum=1)
params = np.random.rand(4) * np.pi
for epoch in range(epochs):
params -= lr * grad(U, params)
return params
# These functions are responsible for testing the solution.
def run(test_case_input: str) -> str:
matrix = json.loads(test_case_input)
params = [float(p) for p in train_parameters(matrix)]
return json.dumps(params)
def check(solution_output: str, expected_output: str) -> None:
matrix1 = get_matrix(json.loads(solution_output))
matrix2 = json.loads(expected_output)
assert not np.allclose(get_matrix(np.random.rand(4)), get_matrix(np.random.rand(4)))
assert np.allclose(matrix1, matrix2, atol=0.2)
# These are the public test cases
test_cases = [
('[[ 0.70710678, 0.70710678], [ 0.70710678, -0.70710678]]', '[[ 0.70710678, 0.70710678], [ 0.70710678, -0.70710678]]'),
('[[ 1, 0], [ 0, -1]]', '[[ 1, 0], [ 0, -1]]')
]
# This will run the public test cases locally
for i, (input_, expected_output) in enumerate(test_cases):
print(f"Running test case {i} with input '{input_}'...")
try:
output = run(input_)
except Exception as exc:
print(f"Runtime Error. {exc}")
else:
if message := check(output, expected_output):
print(f"Wrong Answer. Have: '{output}'. Want: '{expected_output}'.")
else:
print("Correct!")