def test_statefn_overlaps(self):
""" state functions overlaps test """
wf = (4 * StateFn({'101010': .5, '111111': .3})) + ((3 + .1j) * (Zero ^ 6))
wf_vec = StateFn(wf.to_matrix())
self.assertAlmostEqual(wf.adjoint().eval(wf), 14.45)
self.assertAlmostEqual(wf_vec.adjoint().eval(wf_vec), 14.45)
self.assertAlmostEqual(wf_vec.adjoint().eval(wf), 14.45)
self.assertAlmostEqual(wf.adjoint().eval(wf_vec), 14.45)
def test_wf_evals_x(self):
""" wf evals x test """
qbits = 4
wf = ((Zero ^ qbits) + (One ^ qbits)) * (1 / 2**.5)
# Note: wf = Plus^qbits fails because TensoredOp can't handle it.
wf_vec = StateFn(wf.to_matrix())
op = X ^ qbits
# op = I^6
self.assertAlmostEqual(wf.adjoint().eval(op.eval(wf)), 1)
self.assertAlmostEqual(wf_vec.adjoint().eval(op.eval(wf)), 1)
self.assertAlmostEqual(wf.adjoint().eval(op.eval(wf_vec)), 1)
self.assertAlmostEqual(wf_vec.adjoint().eval(op.eval(wf_vec)), 1)
# op = (H^X^Y)^2
op = H ^ 6
wf = ((Zero ^ 6) + (One ^ 6)) * (1 / 2**.5)
wf_vec = StateFn(wf.to_matrix())
# print(wf.adjoint().to_matrix() @ op.to_matrix() @ wf.to_matrix())
self.assertAlmostEqual(wf.adjoint().eval(op.eval(wf)), .25)
self.assertAlmostEqual(wf_vec.adjoint().eval(op.eval(wf)), .25)
self.assertAlmostEqual(wf.adjoint().eval(op.eval(wf_vec)), .25)
self.assertAlmostEqual(wf_vec.adjoint().eval(op.eval(wf_vec)), .25)
qp2ising = QuadraticProgramToIsing() # convert to Ising Hamiltonian
H, offset = qp2ising.encode(qp)
op = H.to_pauli_op()
help(H)
print(H)
# Prepare the state
qc = QuantumCircuit(3)
qc.h(0)
qc.cx(0, 1)
qc.cy(1, 2)
psi = StateFn(qc) # wrap it into a statefunction
print(psi)
# Calculate the expectation value for Ising Hamiltonian
print('expectation_value:', psi.adjoint().compose(op).compose(psi).eval().real)
# %% another way of calculating expectation
# define your backend or quantum instance (where you can add settings)
backend = Aer.get_backend('qasm_simulator')
q_instance = QuantumInstance(backend, shots=1024)
# define the state to sample
measurable_expression = StateFn(op, is_measurement=True).compose(psi)
# convert to expectation value
expectation = PauliExpectation().convert(measurable_expression)
# get state sampler (you can also pass the backend directly)
