def random_unitary_matrix(dim, seed=None):
"""
Return a random unitary ndarray over num qubits.
Args:
dim (int): the dim of the state space.
seed (int): Optional. To set a random seed.
Returns:
ndarray: U (2**num, 2**num) unitary ndarray.
"""
unitary = np.zeros([dim, dim], dtype=complex)
for j in range(dim):
if j == 0:
a = random_state(dim, seed)
else:
a = random_state(dim)
unitary[:, j] = np.copy(a)
# Grahm-Schmidt Orthogonalize
i = j - 1
while i >= 0:
dc = np.vdot(unitary[:, i], a)
unitary[:, j] = unitary[:, j] - dc * unitary[:, i]
i = i - 1
# normalize
unitary[:, j] = unitary[:, j] * (
1.0 / np.sqrt(np.vdot(unitary[:, j], unitary[:, j])))
return unitary
def test_random_state_circuit(self):
state = random_state(3)
q = QuantumRegister(3)
qc = QuantumCircuit(q)
qc.initialize(state, [q[0], q[1], q[2]])
backend = Aer.get_backend('statevector_simulator_py')
qc_state = execute(qc, backend).result().get_statevector(qc)
self.assertAlmostEqual(state_fidelity(qc_state, state), 1.0, places=7)
def test_random_state(self):
# this test that a random state converges to 1/d
number = 100000
E_P0_last = 0
for ii in range(number):
state = basis_state(bin(3)[2:].zfill(3), 3)
E_P0 = (E_P0_last*ii)/(ii+1)+state_fidelity(state, random_state(2**3, seed=ii))/(ii+1)
E_P0_last = E_P0
self.assertAlmostEqual(E_P0, 1/8, places=2)
def test_random_state_circuit(self):
"""TO BE REMOVED Run initizalized circuit"""
with self.assertWarns(DeprecationWarning):
state = random_state(2**3, seed=40)
# Initializer test should be elsewhere
q = QuantumRegister(3)
qc = QuantumCircuit(q)
qc.initialize(state, [q[0], q[1], q[2]])
backend = BasicAer.get_backend('statevector_simulator')
qc_state = execute(qc, backend).result().get_statevector(qc)
self.assertAlmostEqual(state_fidelity(qc_state, state), 1.0, places=7)
def test_random_state(self):
"""TO BE REMOVED with qiskit.quantum_info.basis_state"""
# this test that a random state converges to 1/d
number = 1000
E_P0_last = 0
for ii in range(number):
with self.assertWarns(DeprecationWarning):
state = basis_state(bin(3)[2:].zfill(3), 3)
E_P0 = (E_P0_last*ii)/(ii+1)+state_fidelity(state, random_state(2**3, seed=ii))/(ii+1)
E_P0_last = E_P0
self.assertAlmostEqual(E_P0, 1/8, places=2)
def main():
print("Loading Qiskit Packages...")
# getting usser input
display_report = input("Display the full report at the end y/n? ")
output_file = input("Name your output file (.csv): ")
layers = int(input("input an integer number of layers: "))
# Initializing required parameters for call of minimize
backend = Aer.get_backend('statevector_simulator')
random_state = quantum_info.random_state(16, seed=101)
process_stats = optimization_stats()
angle_bounds = [(0, 2 * np.pi)] * layers * 8
qr = QuantumRegister(4)
theta = init_theta(layers)
print("Optimization Starts (method: L-BFGS-B): ")
# call for minimize on quantum_simulator() see above for specifications
result = minimize(quantum_simulator,
theta,
method='L-BFGS-B',
bounds=angle_bounds,
args=(random_state, layers, backend, qr, process_stats),
options={
'ftol': 2.22e-08,
'gtol': 1e-8,
'maxiter': 100000
})
optimization_data = process_stats.create_data_frame(layers)
# print report based on user input
if display_report.upper() != 'Y':
print("optimization success: ", bool(result.success))
else:
print('\n\n', result)
print(result.x)
print("epsilon :", result.fun)
print(optimization_data)
plot_process(optimization_data)
optimization_data.to_csv(output_file)
