def test_qaoa_callbacks(backend, accelerators):
from qibo import callbacks
# use ``Y`` Hamiltonian so that there are no errors
# in the Trotter decomposition
if accelerators:
with K.on_cpu():
h = hamiltonians.Y(5)
else:
h = hamiltonians.Y(5)
energy = callbacks.Energy(h)
params = 0.1 * np.random.random(4)
state = random_state(5)
ham = hamiltonians.Y(5, dense=False)
qaoa = models.QAOA(ham, callbacks=[energy], accelerators=accelerators)
qaoa.set_parameters(params)
final_state = qaoa(np.copy(state))
h_matrix = K.to_numpy(h.matrix)
m_matrix = K.to_numpy(qaoa.mixer.matrix)
calc_energy = lambda s: (s.conj() * h_matrix.dot(s)).sum()
target_state = np.copy(state)
target_energy = [calc_energy(target_state)]
for i, p in enumerate(params):
if i % 2:
u = expm(-1j * p * m_matrix)
else:
u = expm(-1j * p * h_matrix)
target_state = u @ target_state
target_energy.append(calc_energy(target_state))
K.assert_allclose(energy[:], target_energy)
def test_qaoa_callbacks(accelerators):
from qibo import callbacks
# use ``Y`` Hamiltonian so that there are no errors
# in the Trotter decomposition
h = hamiltonians.Y(3)
energy = callbacks.Energy(h)
params = 0.1 * np.random.random(4)
state = utils.random_numpy_state(3)
ham = hamiltonians.Y(3, trotter=True)
qaoa = models.QAOA(ham, callbacks=[energy], accelerators=accelerators)
qaoa.set_parameters(params)
final_state = qaoa(np.copy(state))
h_matrix = h.matrix.numpy()
m_matrix = qaoa.mixer.matrix.numpy()
calc_energy = lambda s: (s.conj() * h_matrix.dot(s)).sum()
target_state = np.copy(state)
target_energy = [calc_energy(target_state)]
for i, p in enumerate(params):
if i % 2:
u = expm(-1j * p * m_matrix)
else:
u = expm(-1j * p * h_matrix)
target_state = u @ target_state
target_energy.append(calc_energy(target_state))
np.testing.assert_allclose(energy[:], target_energy)
def test_hamiltonian_matmul(backend, sparse_type):
"""Test matrix multiplication between Hamiltonians."""
if sparse_type is None:
nqubits = 3
H1 = hamiltonians.TFIM(nqubits, h=1.0)
H2 = hamiltonians.Y(nqubits)
else:
nqubits = 5
nstates = 2 ** nqubits
H1 = hamiltonians.Hamiltonian(nqubits, random_sparse_matrix(nstates, sparse_type))
H2 = hamiltonians.Hamiltonian(nqubits, random_sparse_matrix(nstates, sparse_type))
m1 = K.to_numpy(H1.matrix)
m2 = K.to_numpy(H2.matrix)
if K.name == "tensorflow" and sparse_type is not None:
with pytest.raises(NotImplementedError):
_ = H1 @ H2
else:
K.assert_allclose((H1 @ H2).matrix, m1 @ m2)
K.assert_allclose((H2 @ H1).matrix, m2 @ m1)
with pytest.raises(ValueError):
H1 @ np.zeros(3 * (2 ** nqubits,), dtype=m1.dtype)
with pytest.raises(NotImplementedError):
H1 @ 2
def test_hamiltonian_matmul():
"""Test matrix multiplication between Hamiltonians and state vectors."""
H1 = hamiltonians.TFIM(nqubits=3, h=1.0)
H2 = hamiltonians.Y(nqubits=3)
m1 = K.to_numpy(H1.matrix)
m2 = K.to_numpy(H2.matrix)
K.assert_allclose((H1 @ H2).matrix, m1 @ m2)
K.assert_allclose((H2 @ H1).matrix, m2 @ m1)
v = random_complex(8, dtype=m1.dtype)
m = random_complex((8, 8), dtype=m1.dtype)
H1v = H1 @ K.cast(v)
H1m = H1 @ K.cast(m)
K.assert_allclose(H1v, m1.dot(v))
K.assert_allclose(H1m, m1 @ m)
from qibo.core.states import VectorState
H1state = H1 @ VectorState.from_tensor(K.cast(v))
K.assert_allclose(H1state, m1.dot(v))
with pytest.raises(ValueError):
H1 @ np.zeros((8, 8, 8), dtype=m1.dtype)
with pytest.raises(NotImplementedError):
H1 @ 2
def test_hamiltonian_addition():
H1 = hamiltonians.Y(nqubits=3)
H2 = hamiltonians.TFIM(nqubits=3, h=1.0)
H = H1 + H2
matrix = H1.matrix + H2.matrix
K.assert_allclose(H.matrix, matrix)
H = H1 - 0.5 * H2
matrix = H1.matrix - 0.5 * H2.matrix
K.assert_allclose(H.matrix, matrix)
H1 = hamiltonians.XXZ(nqubits=2, delta=0.5)
H2 = hamiltonians.XXZ(nqubits=3, delta=0.1)
with pytest.raises(RuntimeError):
R = H1 + H2
with pytest.raises(RuntimeError):
R = H1 - H2
def test_hamiltonian_addition(sparse_type):
if sparse_type is None:
H1 = hamiltonians.Y(nqubits=3)
H2 = hamiltonians.TFIM(nqubits=3, h=1.0)
else:
H1 = hamiltonians.Hamiltonian(6, sparse.rand(64, 64, format=sparse_type))
H2 = hamiltonians.Hamiltonian(6, sparse.rand(64, 64, format=sparse_type))
H = H1 + H2
matrix = H1.matrix + H2.matrix
K.assert_allclose(H.matrix, matrix)
H = H1 - 0.5 * H2
matrix = H1.matrix - 0.5 * H2.matrix
K.assert_allclose(H.matrix, matrix)
H1 = hamiltonians.XXZ(nqubits=2, delta=0.5)
H2 = hamiltonians.XXZ(nqubits=3, delta=0.1)
with pytest.raises(RuntimeError):
R = H1 + H2
with pytest.raises(RuntimeError):
R = H1 - H2
