def test_methods_qng(simulator, method):
### please note! I am finely tuned to always pass! don't get cocky and change lr, maxiter, etc.
H = tq.paulis.Y(0)
U = tq.gates.Ry(numpy.pi / 4, 0) + tq.gates.Ry(
numpy.pi / 3, 1) + tq.gates.Ry(numpy.pi / 7, 2)
U += tq.gates.Rz('a', 0) + tq.gates.Rz('b', 1)
U += tq.gates.CNOT(control=0, target=1) + tq.gates.CNOT(control=1,
target=2)
U += tq.gates.Ry('c', 1) + tq.gates.Rx('d', 2)
U += tq.gates.CNOT(control=0, target=1) + tq.gates.CNOT(control=1,
target=2)
E = tq.ExpectationValue(H=H, U=U)
# just equal to the original circuit, but i'm checking that all the sub-division works
O = E
initial_values = {"a": 0.432, "b": -0.123, 'c': 0.543, 'd': 0.233}
lr = 0.1
result = minimize(objective=-O,
gradient='qng',
backend=simulator,
method=method,
maxiter=200,
lr=lr,
initial_values=initial_values,
silent=False)
assert (numpy.isclose(result.energy, -0.612, atol=2.e-2))
def test_method_convergence(simulator, method):
U = tq.gates.Trotterized(angles=["a"],
steps=1,
generators=[tq.paulis.Y(0)])
H = tq.paulis.X(0)
O = tq.ExpectationValue(U=U, H=H)
samples = None
angles = {'a': numpy.pi / 3}
result = minimize(objective=O,
method=method,
initial_values=angles,
samples=samples,
lr=0.1,
maxiter=200,
backend=simulator)
assert (numpy.isclose(result.energy, -1.0, atol=3.e-2))
def test_execution_shot(simulator, options):
U = tq.gates.Rz(angle="a", target=0) \
+ tq.gates.X(target=2) \
+ tq.gates.Ry(angle="b", target=1, control=2) \
+ tq.gates.Trotterized(angles=["c","d"],
generators=[-0.25 * tq.paulis.Z(1), tq.paulis.X(0) + tq.paulis.Y(1)], steps=2) \
+ tq.gates.Trotterized(angles=[1.0, 2.0],
generators=[-0.25 * tq.paulis.Z(1), tq.paulis.X(0) + tq.paulis.Y(1)], steps=2) \
+ tq.gates.ExpPauli(angle="a", paulistring="X(0)Y(1)Z(2)")
H = 1.0 * tq.paulis.X(0) + 2.0 * tq.paulis.Y(1) + 3.0 * tq.paulis.Z(2)
O = tq.ExpectationValue(U=U, H=H)
mi = 2
result = minimize(objective=O,
maxiter=mi,
backend=simulator,
samples=10,
gradient=options)
def test_methods_diis(simulator, method):
H = tq.paulis.Y(0)
U = tq.gates.Ry(numpy.pi / 4, 0) + tq.gates.Ry(
numpy.pi / 3, 1) + tq.gates.Ry(numpy.pi / 7, 2)
U += tq.gates.Rz('a', 0) + tq.gates.Rz('b', 1)
U += tq.gates.CNOT(control=0, target=1) + tq.gates.CNOT(control=1,
target=2)
U += tq.gates.Ry('c', 1) + tq.gates.Rx('d', 2)
U += tq.gates.CNOT(control=0, target=1) + tq.gates.CNOT(control=1,
target=2)
E = tq.ExpectationValue(H=H, U=U)
initial_values = {"a": -0.03, "b": 1.65, 'c': 1.4, 'd': -0.53}
lr = 0.1
result = minimize(objective=-E,
diis=True,
backend=simulator,
tol=1e-7,
method=method,
maxiter=100,
lr=lr,
initial_values=initial_values,
silent=True)
assert (numpy.isclose(result.energy, -0.612, atol=2.e-2))
def test_methods_qng(simulator, method):
### please note! I am finely tuned to always pass! don't get cocky and change lr, maxiter, etc.
H = tq.paulis.Y(0)
U = tq.gates.Ry(numpy.pi / 4, 0) + tq.gates.Ry(
numpy.pi / 3, 1) + tq.gates.Ry(numpy.pi / 7, 2)
U += tq.gates.Rz('a', 0) + tq.gates.Rz('b', 1)
U += tq.gates.CNOT(control=0, target=1) + tq.gates.CNOT(control=1,
target=2)
U += tq.gates.Ry('c', 1) + tq.gates.Rx('d', 2)
U += tq.gates.CNOT(control=0, target=1) + tq.gates.CNOT(control=1,
target=2)
E = tq.ExpectationValue(H=H, U=U)
initial_values = {"a": -0.01, "b": 1.60, 'c': 1.4, 'd': -0.53}
lr = 0.1
result = minimize(objective=-E,
gradient='qng',
backend=simulator,
method=method,
maxiter=20,
lr=lr,
initial_values=initial_values,
silent=True)
assert (numpy.isclose(result.energy, -0.612, atol=2.e-2))
