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test_templates.py
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# pylint: disable=invalid-name
import sys
import os
import numpy as np
import pytest
from pytest_lazyfixture import lazy_fixture as lf
thisfile = os.path.abspath(__file__)
modulepath = os.path.dirname(os.path.dirname(thisfile))
sys.path.insert(0, modulepath)
import tensorcircuit as tc
def test_any_measurement():
c = tc.Circuit(2)
c.H(0)
c.H(1)
mea = np.array([1, 1])
r = tc.templates.measurements.any_measurements(c, mea, onehot=True)
np.testing.assert_allclose(r, 1.0, atol=1e-5)
mea2 = np.array([3, 0])
r2 = tc.templates.measurements.any_measurements(c, mea2, onehot=True)
np.testing.assert_allclose(r2, 0.0, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("jaxb"), lf("tfb")])
def test_parameterized_local_measurement(backend):
c = tc.Circuit(3)
c.X(0)
c.cnot(0, 1)
c.H(-1)
basis = tc.backend.convert_to_tensor(np.array([3, 3, 1]))
r = tc.templates.measurements.parameterized_local_measurements(
c, structures=basis, onehot=True
)
np.testing.assert_allclose(r, np.array([-1, -1, 1]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_sparse_expectation(backend):
ham = tc.backend.coo_sparse_matrix(
indices=[[0, 1], [1, 0]], values=tc.backend.ones([2]), shape=(2, 2)
)
def f(param):
c = tc.Circuit(1)
c.rx(0, theta=param[0])
c.H(0)
return tc.templates.measurements.sparse_expectation(c, ham)
fvag = tc.backend.jit(tc.backend.value_and_grad(f))
param = tc.backend.zeros([1])
print(fvag(param))
def test_bell_block():
c = tc.Circuit(4)
c = tc.templates.blocks.Bell_pair_block(c)
for _ in range(10):
s = c.perfect_sampling()[0]
assert s[0] != s[1]
assert s[2] != s[3]
def test_qft_block() -> None:
c = tc.Circuit(4)
c = tc.templates.blocks.qft(c, 0, 1, 2, 3)
s = c.perfect_sampling()
assert s[1] - 0.0624999 < 10e-6
def test_grid_coord():
cd = tc.templates.graphs.Grid2DCoord(3, 2)
assert cd.all_cols() == [(0, 3), (1, 4), (2, 5)]
assert cd.all_rows() == [(0, 1), (1, 2), (3, 4), (4, 5)]
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_qaoa_template(backend):
cd = tc.templates.graphs.Grid2DCoord(3, 2)
g = cd.lattice_graph(pbc=False)
for e1, e2 in g.edges:
g[e1][e2]["weight"] = np.random.uniform()
def forward(paramzz, paramx):
c = tc.Circuit(6)
for i in range(6):
c.H(i)
c = tc.templates.blocks.QAOA_block(c, g, paramzz, paramx)
return tc.templates.measurements.spin_glass_measurements(c, g)
fvag = tc.backend.jit(tc.backend.value_and_grad(forward, argnums=(0, 1)))
paramzz = tc.backend.real(tc.backend.ones([1]))
paramx = tc.backend.real(tc.backend.ones([1]))
_, gr = fvag(paramzz, paramx)
np.testing.assert_allclose(gr[1].shape, [1])
paramzz = tc.backend.real(tc.backend.ones([7]))
paramx = tc.backend.real(tc.backend.ones([1]))
_, gr = fvag(paramzz, paramx)
np.testing.assert_allclose(gr[0].shape, [7])
paramzz = tc.backend.real(tc.backend.ones([1]))
paramx = tc.backend.real(tc.backend.ones([6]))
_, gr = fvag(paramzz, paramx)
np.testing.assert_allclose(gr[0].shape, [1])
np.testing.assert_allclose(gr[1].shape, [6])
def test_state_wrapper():
Bell_pair_block_state = tc.templates.blocks.state_centric(
tc.templates.blocks.Bell_pair_block
)
s = Bell_pair_block_state(np.array([1.0, 0, 0, 0]))
np.testing.assert_allclose(
s, np.array([0.0, 0.70710677 + 0.0j, -0.70710677 + 0.0j, 0]), atol=1e-5
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_amplitude_encoding(backend):
batched_amplitude_encoding = tc.backend.vmap(
tc.templates.dataset.amplitude_encoding, vectorized_argnums=0
)
figs = np.stack([np.eye(2), np.ones([2, 2])])
figs = tc.array_to_tensor(figs)
states = batched_amplitude_encoding(figs, 3)
# note that you cannot use nqubits=3 here for jax backend
# see this issue: https://github.com/google/jax/issues/7465
np.testing.assert_allclose(states[1], np.array([0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0]))
states = batched_amplitude_encoding(
figs, 2, tc.array_to_tensor(np.array([0, 3, 1, 2]), dtype="int32")
)
np.testing.assert_allclose(states[0], 1 / np.sqrt(2) * np.array([1, 1, 0, 0]))
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_mpo_measurement(backend):
def f(theta):
mpo = tc.quantum.QuOperator.from_local_tensor(
tc.array_to_tensor(tc.gates._x_matrix), [2, 2, 2], [0]
)
c = tc.Circuit(3)
c.ry(0, theta=theta)
c.H(1)
c.H(2)
e = tc.templates.measurements.mpo_expectation(c, mpo)
return e
v, g = tc.backend.jit(tc.backend.value_and_grad(f))(tc.backend.ones([]))
np.testing.assert_allclose(v, 0.84147, atol=1e-4)
np.testing.assert_allclose(g, 0.54032, atol=1e-4)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_operator_measurement(backend):
mpo = tc.quantum.QuOperator.from_local_tensor(
tc.array_to_tensor(tc.gates._x_matrix), [2, 2], [0]
)
dense = tc.array_to_tensor(np.kron(tc.gates._x_matrix, np.eye(2)))
sparse = tc.quantum.PauliString2COO([1, 0])
if tc.backend.name == "jax":
sparse = tc.backend.coo_sparse_matrix(
sparse.indices, sparse.values, sparse.shape
)
for h in [dense, sparse, mpo]:
def f(theta):
c = tc.Circuit(2)
c.ry(0, theta=theta)
c.H(1)
e = tc.templates.measurements.operator_expectation(c, h)
return e
v, g = tc.backend.jit(tc.backend.value_and_grad(f))(tc.backend.ones([]))
np.testing.assert_allclose(v, 0.84147, atol=1e-4)
np.testing.assert_allclose(g, 0.54032, atol=1e-4)
@pytest.fixture
def symmetric_matrix():
matrix = np.array([[-5.0, -2.0], [-2.0, 6.0]])
nsym_matrix = np.array(
[[1.0, 2.0, 3.0], [2.0, 4.0, 5.0], [3.0, 5.0, 6.0], [8.0, 7.0, 6.0]]
)
return matrix, nsym_matrix
def test_QUBO_to_Ising(symmetric_matrix):
matrix1, matrix2 = symmetric_matrix
pauli_terms, weights, offset = tc.templates.conversions.QUBO_to_Ising(matrix1)
n = matrix1.shape[0]
expected_num_terms = n + n * (n - 1) // 2
assert len(pauli_terms) == expected_num_terms
assert len(weights) == expected_num_terms
assert isinstance(pauli_terms, list)
assert isinstance(weights, np.ndarray)
assert isinstance(offset, float)
assert pauli_terms == [
[1, 0],
[0, 1],
[1, 1],
]
assert all(weights == np.array([3.5, -2.0, -1.0]))
assert offset == -0.5
with pytest.raises(ValueError):
tc.templates.conversions.QUBO_to_Ising(matrix2)