def test_sampler_expectation_value() -> None:
c = Circuit(2)
c.H(0)
c.CX(0, 1)
op = QubitPauliOperator({
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Z
}):
1.0,
QubitPauliString({
Qubit(0): Pauli.X,
Qubit(1): Pauli.X
}):
0.3,
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Y
}):
0.8j,
QubitPauliString({Qubit(0): Pauli.Y}):
-0.4j,
})
b = MySampler()
b.compile_circuit(c)
expectation = get_operator_expectation_value(c,
op,
b,
n_shots=2000,
seed=0)
assert (np.real(expectation), np.imag(expectation)) == pytest.approx(
(1.3, 0.0), abs=0.1)
def test_aer_placed_expectation() -> None:
# bug TKET-695
n_qbs = 3
c = Circuit(n_qbs, n_qbs)
c.X(0)
c.CX(0, 2)
c.CX(1, 2)
c.H(1)
# c.measure_all()
b = AerBackend()
operator = QubitPauliOperator({
QubitPauliString(Qubit(0), Pauli.Z): 1.0,
QubitPauliString(Qubit(1), Pauli.X): 0.5,
})
assert b.get_operator_expectation_value(c, operator) == (-0.5 + 0j)
with open(os.path.join(sys.path[0], "ibmqx2_properties.pickle"),
"rb") as f:
properties = pickle.load(f)
noise_model = NoiseModel.from_backend(properties)
noise_b = AerBackend(noise_model)
with pytest.raises(RuntimeError) as errorinfo:
noise_b.get_operator_expectation_value(c, operator)
assert "not supported with noise model" in str(errorinfo.value)
c.rename_units({Qubit(1): Qubit("node", 1)})
with pytest.raises(ValueError) as errorinfoCirc:
b.get_operator_expectation_value(c, operator)
assert "default register Qubits" in str(errorinfoCirc.value)
def test_expectation_value() -> None:
c = Circuit(2)
c.H(0)
c.CX(0, 1)
op = QubitPauliOperator({
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Z
}):
1.0,
QubitPauliString({
Qubit(0): Pauli.X,
Qubit(1): Pauli.X
}):
0.3,
QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Y
}):
0.8j,
QubitPauliString({Qubit(0): Pauli.Y}):
-0.4j,
})
b = MyBackend()
b.compile_circuit(c)
assert get_operator_expectation_value(c, op, b) == pytest.approx(1.3)
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() -> 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 ucc(params):
singles_params = {qps: params[0] * coeff for qps, coeff in singles.items()}
doubles_params = {qps: params[1] * coeff for qps, coeff in doubles.items()}
excitation_op = QubitPauliOperator({**singles_params, **doubles_params})
reference_circ = Circuit(4).X(1).X(3)
ansatz = gen_term_sequence_circuit(excitation_op, reference_circ)
GuidedPauliSimp().apply(ansatz)
FullPeepholeOptimise().apply(ansatz)
return ansatz
def test_moments_with_shots() -> None:
b = BraketBackend(local=True)
c = Circuit(1).H(0)
z = QubitPauliString(Qubit(0), Pauli.Z)
e = b.get_pauli_expectation_value(c, z, n_shots=10)
assert abs(e) <= 1
v = b.get_pauli_variance(c, z, n_shots=10)
assert v <= 1
op = QubitPauliOperator({z: 3})
e = b.get_operator_expectation_value(c, op, n_shots=10)
assert abs(e) <= 3
v = b.get_operator_variance(c, op, n_shots=10)
assert v <= 9
def test_variance() -> None:
b = BraketBackend(local=True)
assert b.supports_variance
# - Prepare a state (1/sqrt(2), 1/sqrt(2)).
# - Measure w.r.t. the operator Z which has evcs (1,0) (evl=+1) and (0,1) (evl=-1).
# - Get +1 with prob. 1/2 and -1 with prob. 1/2.
c = Circuit(1).H(0)
z = QubitPauliString(Qubit(0), Pauli.Z)
assert b.get_pauli_expectation_value(c, z) == pytest.approx(0)
assert b.get_pauli_variance(c, z) == pytest.approx(1)
op = QubitPauliOperator({z: 3})
assert b.get_operator_expectation_value(c, op) == pytest.approx(0)
assert b.get_operator_variance(c, op) == pytest.approx(9)
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))
#
# During a VQE run, we will call this objective function many times and run many measurement circuits within each, but the circuits that are run on the quantum computer are almost identical, having the same gate structure but with different gate parameters and measurements. We have already exploited this within the body of the objective function by simplifying the ansatz circuit before we call `get_operator_expectation_value`, so it is only done once per objective calculation rather than once per measurement circuit.
#
# We can go even further by simplifying it once outside of the objective function, and then instantiating the simplified ansatz with the parameter values needed. For this, we will construct the UCC ansatz circuit using symbolic (parametric) gates.
from sympy import symbols
# Symbolic UCC ansatz generation:
syms = symbols("p0 p1 p2")
singles_a_syms = {qps: syms[0] * coeff for qps, coeff in singles_a.items()}
singles_b_syms = {qps: syms[1] * coeff for qps, coeff in singles_b.items()}
doubles_syms = {qps: syms[2] * coeff for qps, coeff in doubles.items()}
excitation_op = QubitPauliOperator({
**singles_a_syms,
**singles_b_syms,
**doubles_syms
})
ucc_ref = Circuit(4).X(1).X(3)
ucc = gen_term_sequence_circuit(excitation_op, ucc_ref)
GuidedPauliSimp().apply(ucc)
FullPeepholeOptimise().apply(ucc)
# Objective function using the symbolic ansatz:
def objective(params):
circ = ucc.copy()
sym_map = dict(zip(syms, params))
circ.symbol_substitution(sym_map)
return (get_operator_expectation_value(
c = Circuit(3)
c.H(0).CX(0, 1)
c.Rz(0.3, 0)
c.Rz(-0.3, 1)
c.Ry(0.8, 2)
# Define the measurement operator:
xxi = QubitPauliString({Qubit(0): Pauli.X, Qubit(1): Pauli.X})
zzz = QubitPauliString({
Qubit(0): Pauli.Z,
Qubit(1): Pauli.Z,
Qubit(2): Pauli.Z
})
op = QubitPauliOperator({xxi: -1.8, zzz: 0.7j})
# Run on the backend:
backend = AerBackend()
backend.compile_circuit(c)
exp = backend.get_operator_expectation_value(c, op)
print(exp)
# ## `pytket.extensions.qiskit.AerBackend`
# `AerBackend` wraps up the `qasm_simulator` from the Qiskit Aer package. It supports an extremely flexible set of circuits and uses many effective simulation methods making it a great all-purpose sampling simulator.
#
# Unique features:
# - supports mid-circuit measurement and OpenQASM-style conditional gates;
# - encompasses a variety of underlying simulation methods and automatically selects the best one for each circuit (including statevector, density matrix, (extended) stabilizer and matrix product state);