def test_operator() -> None:
for b in [AerBackend(), AerStateBackend()]:
c = circuit_gen()
zz = QubitPauliOperator(
{QubitPauliString([Qubit(0), Qubit(1)], [Pauli.Z, Pauli.Z]): 1.0})
assert cmath.isclose(get_operator_expectation_value(c, zz, b), 1.0)
c.X(0)
assert cmath.isclose(get_operator_expectation_value(c, zz, b), -1.0)
def test_operator() -> None:
c = circuit_gen()
b = ProjectQBackend()
zz = QubitPauliOperator(
{QubitPauliString([Qubit(0), Qubit(1)], [Pauli.Z, Pauli.Z]): 1.0})
assert np.isclose(get_operator_expectation_value(c, zz, b), complex(1.0))
c.X(0)
assert np.isclose(get_operator_expectation_value(c, zz, b), complex(-1.0))
def test_operator_statevector(qvm: None, quilc: None) -> None:
c = Circuit(2, 2)
c.Rz(0.5, 0)
b = ForestStateBackend()
zi = QubitPauliString(Qubit(0), Pauli.Z)
iz = QubitPauliString(Qubit(1), Pauli.Z)
op = QubitPauliOperator({zi: 0.3, iz: -0.1})
assert get_operator_expectation_value(c, op, b) == pytest.approx(0.2)
c.X(0)
assert get_operator_expectation_value(c, op, b) == pytest.approx(-0.4)
def test_operator_sim(qvm: None, quilc: None) -> None:
c = Circuit(2, 2)
c.Rz(0.5, 0)
b = ForestBackend("9q-square")
zi = QubitPauliString(Qubit(0), Pauli.Z)
iz = QubitPauliString(Qubit(1), Pauli.Z)
op = QubitPauliOperator({zi: 0.3, iz: -0.1})
assert get_operator_expectation_value(c, op, b,
10) == pytest.approx(0.2, rel=0.001)
c.X(0)
assert get_operator_expectation_value(c, op, b,
10) == pytest.approx(-0.4, rel=0.001)
def test_expectation() -> None:
b = BraketBackend(local=True)
assert b.supports_expectation
c = Circuit(2, 2)
c.Rz(0.5, 0)
zi = QubitPauliString(Qubit(0), Pauli.Z)
iz = QubitPauliString(Qubit(1), Pauli.Z)
op = QubitPauliOperator({zi: 0.3, iz: -0.1})
assert get_pauli_expectation_value(c, zi, b) == 1
assert get_operator_expectation_value(c, op, b) == pytest.approx(0.2)
c.X(0)
assert get_pauli_expectation_value(c, zi, b) == -1
assert get_operator_expectation_value(c, op, b) == pytest.approx(-0.4)
def test_device() -> None:
c = circuit_gen(False)
b = IBMQBackend("ibmq_santiago", hub="ibm-q", group="open", project="main")
assert b._max_per_job == 75
operator = QubitPauliOperator({
QubitPauliString(Qubit(0), Pauli.Z): 1.0,
QubitPauliString(Qubit(0), Pauli.X): 0.5,
})
val = get_operator_expectation_value(c, operator, b, 8000)
print(val)
c1 = circuit_gen(True)
c2 = circuit_gen(True)
b.compile_circuit(c1)
b.compile_circuit(c2)
print(b.get_shots(c1, n_shots=10))
print(b.get_shots(c2, n_shots=10))
shots_backend.compile_circuit(c)
op = QubitPauliOperator({
QubitPauliString([Qubit(0)], [Pauli.Z]):
0.1,
QubitPauliString(
[Qubit(0), Qubit(1), Qubit(2),
Qubit(3), Qubit(4)],
[Pauli.Y, Pauli.Z, Pauli.X, Pauli.X, Pauli.Y],
):
0.4,
QubitPauliString([Qubit(0), Qubit(1)], [Pauli.X, Pauli.X]):
0.2,
})
shots_result = get_operator_expectation_value(c, op, shots_backend, n_shots)
print(shots_result)
# The result should be around 0.1, although as the shot simulator is stochastic this will be inexact. Let's test to check what the exact result should be using the statevector simulator:
state_backend = AerStateBackend()
state_result = get_operator_expectation_value(c, op, state_backend)
print(state_result)
# Now we can introduce measurement reduction. First we need to choose a strategy:
from pytket.partition import PauliPartitionStrat
# This first one only performs measurements on simultaneous Pauli operators when there is no cost incurred to do so.
strat = PauliPartitionStrat.NonConflictingSets