def test_absolute():
t = bk.astensor([-2.25 + 4.75j, -3.25 + 5.75j])
t = bk.absolute(t)
assert np.allclose(bk.evaluate(t), [5.25594902, 6.60492229])
t = bk.astensor([-2.25 + 4.75j])
t = bk.absolute(t)
print(bk.evaluate(t))
assert np.allclose(bk.evaluate(t), [5.25594902])
def test_outer():
s0 = np.random.normal(size=[2, 2]) + 1.0j * np.random.normal(size=[2, 2])
s1 = np.random.normal(size=[2, 2, 2]) \
+ 1.0j * np.random.normal(size=[2, 2, 2])
res = bk.astensorproduct(bk.outer(bk.astensor(s0), bk.astensor(s1)))
assert bk.rank(res) == 5
res2 = np.outer(s0, s1).reshape([2]*5)
assert np.allclose(bk.evaluate(res), res2)
def test_inner():
v0 = np.random.normal(size=[2, 2, 2, 2]) \
+ 1.0j * np.random.normal(size=[2, 2, 2, 2])
v1 = np.random.normal(size=[2, 2, 2, 2]) \
+ 1.0j * np.random.normal(size=[2, 2, 2, 2])
res = np.vdot(v0, v1)
bkres = bk.evaluate(bk.inner(bk.astensor(v0), bk.astensor(v1)))
print(bkres)
assert np.abs(res-bkres) == ALMOST_ZERO
def test_size():
"""size(tensor) should return the number of elements"""
t = bk.astensor([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]])
assert bk.size(t) == 16
def test_trace():
tensor = bk.astensor(np.asarray([[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, 2.7, 1],
[0, 0, 1, 0.3j]]))
tensor = bk.reshape(tensor, (4, 4)) # FIXME astensor should not reshape
tr = bk.evaluate(bk.trace(tensor))
print(tr)
assert tr - (2.7+0.3j) == ALMOST_ZERO
def test_expectation():
ket = qf.zero_state(4)
M = np.zeros(shape=([2] * 4))
M[0, 0, 0, 0] = 42
M[1, 0, 0, 0] = 1
M[0, 1, 0, 0] = 2
M[0, 0, 1, 0] = 3
M[0, 0, 0, 1] = 4
M = bk.astensor(M)
avg = ket.expectation(M)
assert qf.asarray(avg) == 42
ket = qf.w_state(4)
assert qf.asarray(ket.expectation(M)) == 2.5
def test_real_imag():
tensor = bk.astensor([1.0 + 2.0j, 0.5 - 0.2j])
t = bk.real(tensor)
t = bk.evaluate(t)
assert np.allclose(bk.evaluate(bk.real(tensor)), [1.0, 0.5])
assert np.allclose(bk.evaluate(bk.imag(tensor)), [2.0, -0.2])
