def circuit_tester(prep, test_circ):
for gate in test_circ.gates():
id = gate.gate_id()
target = gate.target()
control = gate.control()
num_qubits = 5
qc1 = Computer(num_qubits)
qc2 = Computer(num_qubits)
qc1.apply_circuit_safe(prep)
qc2.apply_circuit_safe(prep)
qc1.apply_gate_safe(gate)
qc2.apply_gate(gate)
C1 = qc1.get_coeff_vec()
C2 = qc2.get_coeff_vec()
diff_vec = [(C1[i] - C2[i]) * np.conj(C1[i] - C2[i])
for i in range(len(C1))]
diff_norm = np.sum(diff_vec)
if (np.sum(diff_vec) != (0.0 + 0.0j)):
print('|C - C_safe|F^2 control target id')
print('----------------------------------------')
print(diff_norm, ' ', control, ' ', target,
' ', id)
return diff_norm
def test_trotterization_with_controlled_U(self):
circ_vec = [build_circuit('Y_0 X_1'), build_circuit('X_0 Y_1')]
coef_vec = [-1.0719145972781818j, 1.0719145972781818j]
# the operator to be exponentiated
minus_iH = QubitOperator()
for i in range(len(circ_vec)):
minus_iH.add(coef_vec[i], circ_vec[i])
ancilla_idx = 2
# exponentiate the operator
Utrot, phase = trotterization.trotterize_w_cRz(minus_iH, ancilla_idx)
# Case 1: positive control
# initalize a quantum computer
qc = Computer(3)
# build HF state
qc.apply_gate(gate('X', 0, 0))
# put ancilla in |1> state
qc.apply_gate(gate('X', 2, 2))
# apply the troterized minus_iH
qc.apply_circuit(Utrot)
smart_print(qc)
coeffs = qc.get_coeff_vec()
assert coeffs[5] == approx(-0.5421829373021542, abs=1.0e-15)
assert coeffs[6] == approx(-0.8402604730072732, abs=1.0e-15)
# Case 2: negitive control
# initalize a quantum computer
qc = Computer(3)
# build HF state
qc.apply_gate(gate('X', 0, 0))
# apply the troterized minus_iH
qc.apply_circuit(Utrot)
smart_print(qc)
coeffs = qc.get_coeff_vec()
assert coeffs[1] == approx(1, abs=1.0e-15)
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_circuit(self):
print('\n')
num_qubits = 10
qc1 = Computer(num_qubits)
qc2 = Computer(num_qubits)
prep_circ = Circuit()
circ = Circuit()
for i in range(num_qubits):
prep_circ.add(gate('H', i, i))
for i in range(num_qubits):
prep_circ.add(gate('cR', i, i + 1, 1.116 / (i + 1.0)))
for i in range(num_qubits - 1):
circ.add(gate('cX', i, i + 1))
circ.add(gate('cX', i + 1, i))
circ.add(gate('cY', i, i + 1))
circ.add(gate('cY', i + 1, i))
circ.add(gate('cZ', i, i + 1))
circ.add(gate('cZ', i + 1, i))
circ.add(gate('cR', i, i + 1, 3.14159 / (i + 1.0)))
circ.add(gate('cR', i + 1, i, 2.17284 / (i + 1.0)))
qc1.apply_circuit_safe(prep_circ)
qc2.apply_circuit_safe(prep_circ)
qc1.apply_circuit_safe(circ)
qc2.apply_circuit(circ)
C1 = qc1.get_coeff_vec()
C2 = qc2.get_coeff_vec()
diff_vec = [(C1[i] - C2[i]) * np.conj(C1[i] - C2[i])
for i in range(len(C1))]
diff_norm = np.sum(diff_vec)
print('\nNorm of diff vec |C - Csafe|')
print('-----------------------------')
print(' ', diff_norm)
assert diff_norm == approx(0, abs=1.0e-16)
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