def test_stacking_quantum(simulator, value1=(numpy.random.randint(0, 1000) / 1000.0 * (numpy.pi / 2.0)),
value2=(numpy.random.randint(0, 1000) / 1000.0 * (numpy.pi / 2.0))):
a = Variable('a')
b = Variable('b')
values = {a: value1, b: value2}
H1 = tq.paulis.X(0)
H2 = tq.paulis.Y(0)
U1= tq.gates.Ry(angle=a,target=0)
U2= tq.gates.Rx(angle=b,target=0)
e1=ExpectationValue(U1,H1)
e2=ExpectationValue(U2,H2)
stacked= tq.objective.vectorize([e1, e2])
out=simulate(stacked,variables=values,backend=simulator)
v1=out[0]
v2=out[1]
an1= np.sin(a(values))
an2= -np.sin(b(values))
assert np.isclose(v1+v2,an1+an2,atol=1e-3)
# not gonna contract, lets make gradient do some real work
ga=grad(stacked,a)
gb=grad(stacked,b)
la=[tq.simulate(x,variables=values) for x in ga]
print(la)
lb=[tq.simulate(x,variables=values) for x in gb]
print(lb)
tota=np.sum(np.array(la))
totb=np.sum(np.array(lb))
gan1= np.cos(a(values))
gan2= -np.cos(b(values))
assert np.isclose(tota+totb,gan1+gan2,atol=1e-3)
def test_stacking():
a=Variable('a')
b=Variable('b')
def f(x):
return np.cos(x)**2. + np.sin(x)**2.
funcs=[f,f,f,f]
vals = {Variable('a'):numpy.random.uniform(0,np.pi),Variable('b'):numpy.random.uniform(0,np.pi)}
O = VectorObjective(argsets=[[a],[b],[a],[b]],transformations=funcs)
O1 = O.apply_op_list(funcs)
O2 = O1/4
output = simulate(O2,variables=vals)
assert np.isclose(1.,np.sum(output))