def test_trotterization(self):
circ_vec = [Circuit(), build_circuit('Z_0')]
coef_vec = [-1.0j * 0.5, -1.0j * -0.04544288414432624]
# the operator to be exponentiated
minus_iH = QubitOperator()
for i in range(len(circ_vec)):
minus_iH.add(coef_vec[i], circ_vec[i])
# exponentiate the operator
Utrot, phase = trotterization.trotterize(minus_iH)
inital_state = np.zeros(2**4, dtype=complex)
inital_state[3] = np.sqrt(2 / 3)
inital_state[12] = -np.sqrt(1 / 3)
# initalize a quantum computer with above coeficients
# i.e. ca|1100> + cb|0011>
qc = Computer(4)
qc.set_coeff_vec(inital_state)
# apply the troterized minus_iH
qc.apply_circuit(Utrot)
qc.apply_constant(phase)
smart_print(qc)
coeffs = qc.get_coeff_vec()
assert np.real(coeffs[3]) == approx(0.6980209737879599, abs=1.0e-15)
assert np.imag(coeffs[3]) == approx(-0.423595782342996, abs=1.0e-15)
assert np.real(coeffs[12]) == approx(-0.5187235657531178, abs=1.0e-15)
assert np.imag(coeffs[12]) == approx(0.25349397560041553, abs=1.0e-15)
def test_sparse_operator(self):
"""
test the SparseMatrix and SparseVector classes
"""
coeff_vec = [
-0.093750, +0.093750j, -0.093750, -0.093750j, -0.093750,
-0.093750j, +0.062500j, -0.093750, -0.093750, +0.093750j,
+0.093750, -0.062500, -0.093750j, -0.062500j, +0.062500j,
-0.062500, +0.062500, +0.062500, -0.062500j, -0.093750, +0.062500j,
-0.062500, -0.062500j, -0.062500, +0.093750j, +0.093750j,
+0.062500j, +0.093750, -0.062500, -0.062500, -0.093750j, -0.062500j
]
circ_vec = [
build_circuit('X_1 Y_2 X_3 Y_4'),
build_circuit('X_1 Y_2 X_3 X_4'),
build_circuit('X_1 Y_2 Y_3 X_4'),
build_circuit('X_1 X_2 X_3 Y_4'),
build_circuit('X_1 X_2 X_3 X_4'),
build_circuit('X_1 X_2 Y_3 X_4'),
build_circuit('Y_2 X_3 X_4 Z_5 X_6'),
build_circuit('Y_1 Y_2 Y_3 Y_4'),
build_circuit('Y_1 X_2 X_3 Y_4'),
build_circuit('Y_1 X_2 X_3 X_4'),
build_circuit('Y_1 Y_2 X_3 X_4'),
build_circuit('X_2 X_3 X_4 Z_5 X_6'),
build_circuit('Y_1 X_2 Y_3 Y_4'),
build_circuit('X_2 Y_3 Y_4 Z_5 Y_6'),
build_circuit('X_2 Y_3 X_4 Z_5 X_6'),
build_circuit('X_2 Y_3 Y_4 Z_5 X_6'),
build_circuit('X_2 X_3 Y_4 Z_5 Y_6'),
build_circuit('Y_2 Y_3 X_4 Z_5 X_6'),
build_circuit('X_2 X_3 Y_4 Z_5 X_6'),
build_circuit('Y_1 X_2 Y_3 X_4'),
build_circuit('Y_2 Y_3 X_4 Z_5 Y_6'),
build_circuit('Y_2 X_3 X_4 Z_5 Y_6'),
build_circuit('X_2 X_3 X_4 Z_5 Y_6'),
build_circuit('X_2 Y_3 X_4 Z_5 Y_6'),
build_circuit('Y_1 Y_2 Y_3 X_4'),
build_circuit('Y_1 Y_2 X_3 Y_4'),
build_circuit('Y_2 Y_3 Y_4 Z_5 X_6'),
build_circuit('X_1 X_2 Y_3 Y_4'),
build_circuit('Y_2 Y_3 Y_4 Z_5 Y_6'),
build_circuit('Y_2 X_3 Y_4 Z_5 X_6'),
build_circuit('X_1 Y_2 Y_3 Y_4'),
build_circuit('Y_2 X_3 Y_4 Z_5 Y_6')
]
qubit_op = QubitOperator()
for coeff, circ in zip(coeff_vec, circ_vec):
qubit_op.add(coeff, circ)
num_qb = qubit_op.num_qubits()
qci = Computer(num_qb)
arb_vec = np.linspace(0, 2 * np.pi, 2**num_qb)
arb_vec = arb_vec / np.linalg.norm(arb_vec)
qci.set_coeff_vec(arb_vec)
sp_mat_op = qubit_op.sparse_matrix(num_qb)
ci = np.array(qci.get_coeff_vec())
qci.apply_operator(qubit_op)
diff_vec = np.array(qci.get_coeff_vec())
# Reset stae
qci.set_coeff_vec(ci)
qci.apply_sparse_matrix(sp_mat_op)
# see if there is a difference
diff_vec = np.array(qci.get_coeff_vec()) - diff_vec
diff_val = np.linalg.norm(diff_vec)
print('Operator used for sparse matrix operator test: \n', qubit_op)
print('||∆||: ', diff_val)
assert diff_val < 1.0e-15