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_qft(self):
trial_state = Computer(4)
trial_circ = build_circuit('X_0 X_1')
trial_state.apply_circuit(trial_circ)
# verify direct transformation
qft(trial_state, 0, 3)
a1_dag_a2 = build_operator('1.0, Z_0')
exp = trial_state.direct_op_exp_val(a1_dag_a2)
assert exp == approx(0, abs=1.0e-16)
# test unitarity
qft(trial_state, 0, 2)
rev_qft(trial_state, 0, 2)
a1_dag_a2 = build_operator('1.0, Z_0')
exp = trial_state.direct_op_exp_val(a1_dag_a2)
assert exp == approx(0, abs=1.0e-16)
# test reverse transformation
qft(trial_state, 0, 3)
a1_dag_a2 = build_operator('1.0, Z_0')
exp = trial_state.direct_op_exp_val(a1_dag_a2)
assert exp == approx(1.0, abs=1.0e-14)
def create_TFIM(n: int, h: float, J: float):
"""Creates a 1D Transverse Field Ising Model hamiltonian with
open boundary conditions, i.e., no interaction between the
first and last spin sites.
n: int
Number of lattice sites
h: float
Strength of magnetic field
j: float
Interaction strength
"""
TFIM = System()
TFIM.hamiltonian = qf.QubitOperator()
circuit = [(-h, f"Z_{i}") for i in range(n)]
circuit += [(-J, f"X_{i} X_{i+1}") for i in range(n-1)]
for coeff, op_str in circuit:
TFIM.hamiltonian.add(coeff, qf.build_circuit(op_str))
TFIM.hf_reference = [0] * n
return TFIM
def test_large_eigenvalue(self):
E_fci = -20
coef_vec = [E_fci, 0]
circ_vec = [
Circuit( ), build_circuit('Z_1')
]
H2_qubit_hamiltonian = QubitOperator()
for i in range(len(circ_vec)):
H2_qubit_hamiltonian.add(coef_vec[i], circ_vec[i])
ref = [1,0]
print('\nBegin QPE test')
print('----------------------')
# make test with algorithm class
mol = Molecule()
mol.hamiltonian = H2_qubit_hamiltonian
alg = QPE(mol, reference=ref, trotter_number=2)
alg.run(guess_energy = E_fci,
t = 1,
nruns = 50,
success_prob = 0.9,
num_precise_bits = 8)
Egs = alg.get_gs_energy()
assert Egs == approx(E_fci, abs=1.1e-3)
def build_operator(Inputstr):
ops = qforte.QubitOperator()
sepstr = Inputstr.split(';')
for i in range(len(sepstr)):
inputterm = sepstr[i].split(',')
ops.add(complex(inputterm[0]), qforte.build_circuit(inputterm[1]))
return ops
def test_io_simplified(self):
# test direct expectation value measurement
trial_state = Computer(4)
trial_circ = build_circuit('H_0 H_1 H_2 H_3 cX_0_1')
# use circuit to prepare trial state
trial_state.apply_circuit(trial_circ)
#build the quantum operator for [a1^ a2]
a1_dag_a2 = build_operator('0.0-0.25j, X_2 Y_1; 0.25, Y_2 Y_1; \
0.25, X_2 X_1; 0.0+0.25j, Y_2 X_1')
#get direct expectatoin value
exp = trial_state.direct_op_exp_val(a1_dag_a2)
assert exp == approx(0.25, abs=2.0e-16)
def test_advanced_gates(self):
print('\n')
trial_state = Computer(4)
trial_circ = build_circuit('X_0 X_1')
trial_state.apply_circuit(trial_circ)
# verify Toffoli gate
T_circ = Toffoli(0, 1, 2)
print(T_circ.str())
trial_state.apply_circuit(T_circ) # This should turn the state to 1110
a1_dag_a2 = build_operator('1.0, Z_2')
exp = trial_state.direct_op_exp_val(a1_dag_a2)
assert exp == approx(-1, abs=9e-16) # Measure qubit 2 should give -1
# verify Fredkin gate
F_circ = Fredkin(1, 2, 3)
print(F_circ.str())
trial_state.apply_circuit(F_circ) # This should turn the state to 1101
# trial_state.apply_circuit_safe(F_circ) # This should turn the state to 1101
a1_dag_a2 = build_operator('1.0, Z_2')
exp = trial_state.direct_op_exp_val(a1_dag_a2)
assert exp == approx(1, abs=9e-16) # Measure qubit 2 should give +1
def test_jw2(self):
coeff_vec = [
-0.25j, -0.25, 0.25, -0.25j, -0.125j, 0.125, -0.125, -0.125j,
-0.125, -0.125j, 0.125j, -0.125
]
circ_vec = [
build_circuit(x) for x in [
"Y_2 X_3", "Y_2 Y_3", "X_2 X_3", "X_2 Y_3", "Z_0 X_1 Y_3 X_4",
"Z_0 X_1 Y_3 Y_4", "Z_0 X_1 X_3 X_4", "Z_0 X_1 X_3 Y_4",
"Z_0 Y_1 Y_3 X_4", "Z_0 Y_1 Y_3 Y_4", "Z_0 Y_1 X_3 X_4",
"Z_0 Y_1 X_3 Y_4"
]
]
correct_op = QubitOperator()
for coeff, circ in zip(coeff_vec, circ_vec):
correct_op.add(coeff, circ)
a = SQOperator()
a.add(1.00, [2, 3], [])
a.add(1.00, [1], [3, 4])
aop = a.jw_transform()
assert aop.check_op_equivalence(correct_op, True) is True
def test_H4_fast_qkd(self):
print('\n')
# The FCI energy for H4 at 1.5 Angstrom in a sto-6g basis
E_fci = -2.0126741263939656
# The He hamilitonian
circ_vec = [
Circuit(),
build_circuit('Z_0'),
build_circuit('Y_0 Z_1 Z_2 Z_3 Y_4'),
build_circuit('X_0 Z_1 Z_2 Z_3 X_4'),
build_circuit('Z_1'),
build_circuit('Y_1 Z_2 Z_3 Z_4 Y_5'),
build_circuit('X_1 Z_2 Z_3 Z_4 X_5'),
build_circuit('Z_2'),
build_circuit('Y_2 Z_3 Z_4 Z_5 Y_6'),
build_circuit('X_2 Z_3 Z_4 Z_5 X_6'),
build_circuit('Z_3'),
build_circuit('Y_3 Z_4 Z_5 Z_6 Y_7'),
build_circuit('X_3 Z_4 Z_5 Z_6 X_7'),
build_circuit('Z_4'),
build_circuit('Z_5'),
build_circuit('Z_6'),
build_circuit('Z_7'),
build_circuit('Z_0 Z_1'),
build_circuit('Y_0 Z_2 Z_3 Y_4'),
build_circuit('X_0 Z_2 Z_3 X_4'),
build_circuit('Y_0 X_1 X_2 Y_3'),
build_circuit('X_0 X_1 Y_2 Y_3'),
build_circuit('Y_0 Y_1 X_2 X_3'),
build_circuit('X_0 Y_1 Y_2 X_3'),
build_circuit('Y_0 X_1 X_3 Z_4 Z_5 Y_6'),
build_circuit('X_0 X_1 X_3 Z_4 Z_5 X_6'),
build_circuit('Y_0 Y_1 Y_3 Z_4 Z_5 Y_6'),
build_circuit('X_0 Y_1 Y_3 Z_4 Z_5 X_6'),
build_circuit('Z_0 Y_1 Z_2 Z_3 Z_4 Y_5'),
build_circuit('Z_0 X_1 Z_2 Z_3 Z_4 X_5'),
build_circuit('Y_0 X_1 X_4 Y_5'),
build_circuit('X_0 X_1 Y_4 Y_5'),
build_circuit('Y_0 Y_1 X_4 X_5'),
build_circuit('X_0 Y_1 Y_4 X_5'),
build_circuit('Y_0 X_1 X_2 Z_3 Z_4 Z_5 Z_6 Y_7'),
build_circuit('X_0 X_1 Y_2 Z_3 Z_4 Z_5 Z_6 Y_7'),
build_circuit('Y_0 Y_1 X_2 Z_3 Z_4 Z_5 Z_6 X_7'),
build_circuit('X_0 Y_1 Y_2 Z_3 Z_4 Z_5 Z_6 X_7'),
build_circuit('Y_0 X_1 X_6 Y_7'),
build_circuit('X_0 X_1 Y_6 Y_7'),
build_circuit('Y_0 Y_1 X_6 X_7'),
build_circuit('X_0 Y_1 Y_6 X_7'),
build_circuit('Z_0 Z_2'),
build_circuit('Z_0 Y_2 Z_3 Z_4 Z_5 Y_6'),
build_circuit('Z_0 X_2 Z_3 Z_4 Z_5 X_6'),
build_circuit('Y_0 Z_1 Z_3 Y_4'),
build_circuit('X_0 Z_1 Z_3 X_4'),
build_circuit('Y_0 Z_1 X_2 X_4 Z_5 Y_6'),
build_circuit('X_0 Z_1 X_2 X_4 Z_5 X_6'),
build_circuit('X_0 Z_1 X_2 Y_4 Z_5 Y_6'),
build_circuit('Y_0 Z_1 Y_2 X_4 Z_5 X_6'),
build_circuit('Y_0 Z_1 Y_2 Y_4 Z_5 Y_6'),
build_circuit('X_0 Z_1 Y_2 Y_4 Z_5 X_6'),
build_circuit('Z_0 Z_3'),
build_circuit('Y_0 Z_1 Z_2 Y_4'),
build_circuit('X_0 Z_1 Z_2 X_4'),
build_circuit('Y_0 Z_1 Y_2 Y_3 Z_4 Y_5'),
build_circuit('X_0 Z_1 X_2 Y_3 Z_4 Y_5'),
build_circuit('Y_0 Z_1 Y_2 X_3 Z_4 X_5'),
build_circuit('X_0 Z_1 X_2 X_3 Z_4 X_5'),
build_circuit('Y_0 Z_1 Z_2 X_3 X_5 Y_6'),
build_circuit('X_0 Z_1 Z_2 X_3 X_5 X_6'),
build_circuit('Y_0 Z_1 Z_2 Y_3 Y_5 Y_6'),
build_circuit('X_0 Z_1 Z_2 Y_3 Y_5 X_6'),
build_circuit('Z_0 Y_3 Z_4 Z_5 Z_6 Y_7'),
build_circuit('Z_0 X_3 Z_4 Z_5 Z_6 X_7'),
build_circuit('Y_0 Z_1 Z_2 X_3 X_4 Z_5 Z_6 Y_7'),
build_circuit('X_0 Z_1 Z_2 X_3 Y_4 Z_5 Z_6 Y_7'),
build_circuit('Y_0 Z_1 Z_2 Y_3 X_4 Z_5 Z_6 X_7'),
build_circuit('X_0 Z_1 Z_2 Y_3 Y_4 Z_5 Z_6 X_7'),
build_circuit('Z_0 Z_4'),
build_circuit('Z_0 Z_5'),
build_circuit('Y_0 Z_1 Z_2 Z_3 Y_4 Z_5'),
build_circuit('X_0 Z_1 Z_2 Z_3 X_4 Z_5'),
build_circuit('Y_0 Z_1 Y_2 Y_5 Z_6 Y_7'),
build_circuit('X_0 Z_1 X_2 Y_5 Z_6 Y_7'),
build_circuit('Y_0 Z_1 Y_2 X_5 Z_6 X_7'),
build_circuit('X_0 Z_1 X_2 X_5 Z_6 X_7'),
build_circuit('Y_0 Z_1 Z_2 Z_3 Z_4 X_5 X_6 Y_7'),
build_circuit('X_0 Z_1 Z_2 Z_3 Z_4 X_5 Y_6 Y_7'),
build_circuit('Y_0 Z_1 Z_2 Z_3 Z_4 Y_5 X_6 X_7'),
build_circuit('X_0 Z_1 Z_2 Z_3 Z_4 Y_5 Y_6 X_7'),
build_circuit('Z_0 Z_6'),
build_circuit('Y_0 Z_1 Z_2 Z_3 Y_4 Z_6'),
build_circuit('X_0 Z_1 Z_2 Z_3 X_4 Z_6'),
build_circuit('Z_0 Z_7'),
build_circuit('Y_0 Z_1 Z_2 Z_3 Y_4 Z_7'),
build_circuit('X_0 Z_1 Z_2 Z_3 X_4 Z_7'),
build_circuit('Z_1 Z_2'),
build_circuit('Y_1 Z_3 Z_4 Y_5'),
build_circuit('X_1 Z_3 Z_4 X_5'),
build_circuit('Y_1 X_2 X_3 Y_4'),
build_circuit('X_1 X_2 Y_3 Y_4'),
build_circuit('Y_1 Y_2 X_3 X_4'),
build_circuit('X_1 Y_2 Y_3 X_4'),
build_circuit('Y_1 X_2 X_4 Z_5 Z_6 Y_7'),
build_circuit('X_1 X_2 X_4 Z_5 Z_6 X_7'),
build_circuit('Y_1 Y_2 Y_4 Z_5 Z_6 Y_7'),
build_circuit('X_1 Y_2 Y_4 Z_5 Z_6 X_7'),
build_circuit('Z_1 Y_2 Z_3 Z_4 Z_5 Y_6'),
build_circuit('Z_1 X_2 Z_3 Z_4 Z_5 X_6'),
build_circuit('Y_1 X_2 X_5 Y_6'),
build_circuit('X_1 X_2 Y_5 Y_6'),
build_circuit('Y_1 Y_2 X_5 X_6'),
build_circuit('X_1 Y_2 Y_5 X_6'),
build_circuit('Z_1 Z_3'),
build_circuit('Z_1 Y_3 Z_4 Z_5 Z_6 Y_7'),
build_circuit('Z_1 X_3 Z_4 Z_5 Z_6 X_7'),
build_circuit('Y_1 Z_2 Z_4 Y_5'),
build_circuit('X_1 Z_2 Z_4 X_5'),
build_circuit('Y_1 Z_2 X_3 X_5 Z_6 Y_7'),
build_circuit('X_1 Z_2 X_3 X_5 Z_6 X_7'),
build_circuit('X_1 Z_2 X_3 Y_5 Z_6 Y_7'),
build_circuit('Y_1 Z_2 Y_3 X_5 Z_6 X_7'),
build_circuit('Y_1 Z_2 Y_3 Y_5 Z_6 Y_7'),
build_circuit('X_1 Z_2 Y_3 Y_5 Z_6 X_7'),
build_circuit('Z_1 Z_4'),
build_circuit('Y_1 Z_2 Z_3 Y_5'),
build_circuit('X_1 Z_2 Z_3 X_5'),
build_circuit('Y_1 Z_2 Y_3 Y_4 Z_5 Y_6'),
build_circuit('X_1 Z_2 X_3 Y_4 Z_5 Y_6'),
build_circuit('Y_1 Z_2 Y_3 X_4 Z_5 X_6'),
build_circuit('X_1 Z_2 X_3 X_4 Z_5 X_6'),
build_circuit('Y_1 Z_2 Z_3 X_4 X_6 Y_7'),
build_circuit('X_1 Z_2 Z_3 X_4 X_6 X_7'),
build_circuit('Y_1 Z_2 Z_3 Y_4 Y_6 Y_7'),
build_circuit('X_1 Z_2 Z_3 Y_4 Y_6 X_7'),
build_circuit('Z_1 Z_5'),
build_circuit('Z_1 Z_6'),
build_circuit('Y_1 Z_2 Z_3 Z_4 Y_5 Z_6'),
build_circuit('X_1 Z_2 Z_3 Z_4 X_5 Z_6'),
build_circuit('Z_1 Z_7'),
build_circuit('Y_1 Z_2 Z_3 Z_4 Y_5 Z_7'),
build_circuit('X_1 Z_2 Z_3 Z_4 X_5 Z_7'),
build_circuit('Z_2 Z_3'),
build_circuit('Y_2 Z_4 Z_5 Y_6'),
build_circuit('X_2 Z_4 Z_5 X_6'),
build_circuit('Y_2 X_3 X_4 Y_5'),
build_circuit('X_2 X_3 Y_4 Y_5'),
build_circuit('Y_2 Y_3 X_4 X_5'),
build_circuit('X_2 Y_3 Y_4 X_5'),
build_circuit('Z_2 Y_3 Z_4 Z_5 Z_6 Y_7'),
build_circuit('Z_2 X_3 Z_4 Z_5 Z_6 X_7'),
build_circuit('Y_2 X_3 X_6 Y_7'),
build_circuit('X_2 X_3 Y_6 Y_7'),
build_circuit('Y_2 Y_3 X_6 X_7'),
build_circuit('X_2 Y_3 Y_6 X_7'),
build_circuit('Z_2 Z_4'),
build_circuit('Y_2 Z_3 Z_5 Y_6'),
build_circuit('X_2 Z_3 Z_5 X_6'),
build_circuit('Z_2 Z_5'),
build_circuit('Y_2 Z_3 Z_4 Y_6'),
build_circuit('X_2 Z_3 Z_4 X_6'),
build_circuit('Y_2 Z_3 Y_4 Y_5 Z_6 Y_7'),
build_circuit('X_2 Z_3 X_4 Y_5 Z_6 Y_7'),
build_circuit('Y_2 Z_3 Y_4 X_5 Z_6 X_7'),
build_circuit('X_2 Z_3 X_4 X_5 Z_6 X_7'),
build_circuit('Z_2 Z_6'),
build_circuit('Z_2 Z_7'),
build_circuit('Y_2 Z_3 Z_4 Z_5 Y_6 Z_7'),
build_circuit('X_2 Z_3 Z_4 Z_5 X_6 Z_7'),
build_circuit('Z_3 Z_4'),
build_circuit('Y_3 Z_5 Z_6 Y_7'),
build_circuit('X_3 Z_5 Z_6 X_7'),
build_circuit('Y_3 X_4 X_5 Y_6'),
build_circuit('X_3 X_4 Y_5 Y_6'),
build_circuit('Y_3 Y_4 X_5 X_6'),
build_circuit('X_3 Y_4 Y_5 X_6'),
build_circuit('Z_3 Z_5'),
build_circuit('Y_3 Z_4 Z_6 Y_7'),
build_circuit('X_3 Z_4 Z_6 X_7'),
build_circuit('Z_3 Z_6'),
build_circuit('Y_3 Z_4 Z_5 Y_7'),
build_circuit('X_3 Z_4 Z_5 X_7'),
build_circuit('Z_3 Z_7'),
build_circuit('Z_4 Z_5'),
build_circuit('Y_4 X_5 X_6 Y_7'),
build_circuit('X_4 X_5 Y_6 Y_7'),
build_circuit('Y_4 Y_5 X_6 X_7'),
build_circuit('X_4 Y_5 Y_6 X_7'),
build_circuit('Z_4 Z_6'),
build_circuit('Z_4 Z_7'),
build_circuit('Z_5 Z_6'),
build_circuit('Z_5 Z_7'),
build_circuit('Z_6 Z_7')]
coef_vec = [
-0.9398866173443505,
0.12129073807829349,
0.00641125283150221,
0.00641125283150221,
0.12129073807829353,
0.0064112528315022135,
0.0064112528315022135,
0.07308734612725618,
-0.010595295586558178,
-0.010595295586558178,
0.0730873461272563,
-0.010595295586558171,
-0.010595295586558171,
-0.004903180263267734,
-0.0049031802632676785,
-0.0972417632017775,
-0.09724176320177753,
0.10115751743776852,
0.016853161923367253,
0.016853161923367253,
0.03977035285263866,
-0.03977035285263866,
-0.03977035285263866,
0.03977035285263866,
0.00905981792020611,
0.00905981792020611,
0.00905981792020611,
0.00905981792020611,
0.016853161923367253,
0.016853161923367253,
0.028827330860814942,
-0.028827330860814942,
-0.028827330860814942,
0.028827330860814942,
-0.00905981792020611,
0.00905981792020611,
0.00905981792020611,
-0.00905981792020611,
0.027456714140683562,
-0.027456714140683562,
-0.027456714140683562,
0.027456714140683562,
0.05019794254014193,
0.008400644724214635,
0.008400644724214635,
0.01678679876377047,
0.01678679876377047,
0.008370670304957601,
0.019986723846961468,
0.011616053542003863,
0.011616053542003863,
0.019986723846961468,
0.008370670304957601,
0.08996829539278059,
-0.00401664635059683,
-0.00401664635059683,
0.020803445114367297,
0.020803445114367297,
0.020803445114367297,
0.020803445114367297,
-0.020045295426307817,
-0.020045295426307817,
-0.020045295426307817,
-0.020045295426307817,
0.01746046264442075,
0.01746046264442075,
0.028415965731265418,
-0.028415965731265418,
-0.028415965731265418,
0.028415965731265418,
0.062364797002734416,
0.09119212786354935,
-0.0029292718691351837,
-0.0029292718691351837,
0.040032019273269284,
0.040032019273269284,
0.040032019273269284,
0.040032019273269284,
-0.008895760438436422,
0.008895760438436422,
0.008895760438436422,
-0.008895760438436422,
0.077813400984472,
0.008622786573744252,
0.008622786573744252,
0.10527011512515555,
0.017518547012180676,
0.017518547012180676,
0.08996829539278059,
-0.00401664635059683,
-0.00401664635059683,
0.020803445114367297,
-0.020803445114367297,
-0.020803445114367297,
0.020803445114367297,
-0.020045295426307817,
-0.020045295426307817,
-0.020045295426307817,
-0.020045295426307817,
0.01746046264442075,
0.01746046264442075,
0.028415965731265418,
-0.028415965731265418,
-0.028415965731265418,
0.028415965731265418,
0.05019794254014193,
0.008400644724214635,
0.008400644724214635,
0.01678679876377047,
0.01678679876377047,
0.008370670304957601,
0.019986723846961468,
0.011616053542003863,
0.011616053542003863,
0.019986723846961468,
0.008370670304957601,
0.09119212786354935,
-0.0029292718691351837,
-0.0029292718691351837,
0.040032019273269284,
0.040032019273269284,
0.040032019273269284,
0.040032019273269284,
0.008895760438436422,
0.008895760438436422,
0.008895760438436422,
0.008895760438436422,
0.062364797002734416,
0.10527011512515555,
0.017518547012180676,
0.017518547012180676,
0.077813400984472,
0.008622786573744252,
0.008622786573744252,
0.09416046345893181,
-0.0025909758017290006,
-0.0025909758017290006,
0.035233425879700006,
-0.035233425879700006,
-0.035233425879700006,
0.035233425879700006,
-0.0025909758017290006,
-0.0025909758017290006,
0.029476085351951252,
-0.029476085351951252,
-0.029476085351951252,
0.029476085351951252,
0.05898073895378817,
0.018463477774636035,
0.018463477774636035,
0.09421416483348818,
-0.003276374161489622,
-0.003276374161489622,
0.02173985193612566,
0.02173985193612566,
0.02173985193612566,
0.02173985193612566,
0.06484557443787466,
0.0943216597898259,
0.01867832322072119,
0.01867832322072119,
0.09421416483348818,
-0.003276374161489622,
-0.003276374161489622,
0.02173985193612566,
-0.02173985193612566,
-0.02173985193612566,
0.02173985193612566,
0.05898073895378817,
0.018463477774636035,
0.018463477774636035,
0.0943216597898259,
0.01867832322072119,
0.01867832322072119,
0.06484557443787466,
0.0970891982291751,
0.042405629926062886,
-0.042405629926062886,
-0.042405629926062886,
0.042405629926062886,
0.05395287215237218,
0.09635850207843506,
0.09635850207843506,
0.05395287215237218,
0.11278693858600855]
H4_qubit_hamiltonian = QubitOperator()
for i in range(len(circ_vec)):
H4_qubit_hamiltonian.add(coef_vec[i], circ_vec[i])
ref = [1,1,1,1,0,0,0,0]
# make test with algorithm class
mol = Molecule()
mol.hamiltonian = H4_qubit_hamiltonian
# WARNING! With large s, these methods can become numerically unstable.
# Near 0 eigenvalues in the overlap matrices lead to unstable eigenvectors.
# MRSQK
alg2 = MRSQK(mol, reference=ref, trotter_number=100)
alg2.run(s=3, d=3)
Egs2 = alg2.get_gs_energy()
assert Egs2 == approx(E_fci, abs=1.0e-6)
# SRQK
alg1 = SRQK(mol, reference=ref, trotter_number=100)
alg1.run(s=6)
Egs1 = alg1.get_gs_energy()
assert Egs1 == approx(E_fci, abs=1.0e-4)
# Non-Trotterized (exact) SRQK
alg0 = NTSRQK(mol, reference=ref, verbose=True)
alg0.run(s=4, dt = 0.8)
Egs0 = alg0.get_gs_energy()
assert Egs0 == approx(E_fci, abs=1.0e-3)
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
def test_H2_qite(self):
# The FCI energy for H2 at 1.5 Angstrom in a sto-3g basis
E_fci = -0.9981493534
coef_vec = [
-0.4917857774144603, 0.09345649662931771, 0.09345649662931771,
-0.0356448161226769, -0.0356448161226769, 0.1381758457453024,
0.05738398402634884, -0.0573839840263488, -0.0573839840263488,
0.05738398402634884, 0.08253705485911705, 0.13992103888546592,
0.13992103888546592, 0.08253705485911705, 0.1458551902800438
]
circ_vec = [
Circuit(),
build_circuit('Z_0'),
build_circuit('Z_1'),
build_circuit('Z_2'),
build_circuit('Z_3'),
build_circuit('Z_0 Z_1'),
build_circuit('Y_0 X_1 X_2 Y_3'),
build_circuit('X_0 X_1 Y_2 Y_3'),
build_circuit('Y_0 Y_1 X_2 X_3'),
build_circuit('X_0 Y_1 Y_2 X_3'),
build_circuit('Z_0 Z_2'),
build_circuit('Z_0 Z_3'),
build_circuit('Z_1 Z_2'),
build_circuit('Z_1 Z_3'),
build_circuit('Z_2 Z_3')
]
H2_qubit_hamiltonian = QubitOperator()
for i in range(len(circ_vec)):
H2_qubit_hamiltonian.add(coef_vec[i], circ_vec[i])
H2_sq_hamiltonian = [[(), 0.3527848071133334],
[(0, 0), -0.9081808722384057],
[(1, 1), -0.9081808722384057],
[(2, 2), -0.6653369358038996],
[(3, 3), -0.6653369358038996],
[(1, 0, 1, 0), -0.5527033829812091],
[(1, 0, 3, 2), -0.22953593610539536],
[(2, 0, 2, 0), -0.3301482194364681],
[(3, 0, 2, 1), 0.22953593610539527],
[(3, 0, 3, 0), -0.5596841555418633],
[(2, 1, 3, 0), 0.22953593610539527],
[(2, 1, 2, 1), -0.5596841555418633],
[(3, 1, 3, 1), -0.3301482194364681],
[(3, 2, 1, 0), -0.22953593610539522],
[(3, 2, 3, 2), -0.5834207611201749]]
ref = [1, 1, 0, 0]
print('\nBegin QITE test for H2')
print('-------------------------')
# make test with algorithm class #
mol = Molecule()
mol.hamiltonian = H2_qubit_hamiltonian
mol.sq_hamiltonian = H2_sq_hamiltonian
alg = QITE(mol, reference=ref)
alg.run(beta=18.0, do_lanczos=True, lanczos_gap=49)
Egs = alg.get_gs_energy()
assert Egs == approx(E_fci, abs=1.0e-10)
def test_jw1(self):
"""
test JW transform of 1.0(2^ 3^ 4 6) + 1.5(1^ 2^ 3 4)
"""
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')
]
correct_op = QubitOperator()
for coeff, circ in zip(coeff_vec, circ_vec):
correct_op.add(coeff, circ)
# Test qforte construction
a1 = SQOperator()
a1.add(1.00, [2, 3], [4, 6])
a1.add(+1.50, [1, 2], [3, 4])
print(a1.str(), '\n')
aop = a1.jw_transform()
print(aop.str(), '\n')
assert aop.check_op_equivalence(correct_op, True) is True
def test_H2_qpe(self):
print('\n'),
# The FCI energy for H2 at 1.5 Angstrom in a sto-3g basis
E_fci = -0.9981493534
coef_vec = [-0.4917857774144603,
0.09345649662931771,
0.09345649662931771,
-0.0356448161226769,
-0.0356448161226769,
0.1381758457453024,
0.05738398402634884,
-0.0573839840263488,
-0.0573839840263488,
0.05738398402634884,
0.08253705485911705,
0.13992103888546592,
0.13992103888546592,
0.08253705485911705,
0.1458551902800438]
circ_vec = [
Circuit( ),
build_circuit( 'Z_0' ),
build_circuit( 'Z_1' ),
build_circuit( 'Z_2' ),
build_circuit( 'Z_3' ),
build_circuit( 'Z_0 Z_1' ),
build_circuit( 'Y_0 X_1 X_2 Y_3' ),
build_circuit( 'X_0 X_1 Y_2 Y_3' ),
build_circuit( 'Y_0 Y_1 X_2 X_3' ),
build_circuit( 'X_0 Y_1 Y_2 X_3' ),
build_circuit( 'Z_0 Z_2' ),
build_circuit( 'Z_0 Z_3' ),
build_circuit( 'Z_1 Z_2' ),
build_circuit( 'Z_1 Z_3' ),
build_circuit( 'Z_2 Z_3' )]
H2_qubit_hamiltonian = QubitOperator()
for i in range(len(circ_vec)):
H2_qubit_hamiltonian.add(coef_vec[i], circ_vec[i])
ref = [1,1,0,0]
print('\nBegin QPE test for H2')
print('----------------------')
# make test with algorithm class
mol = Molecule()
mol.hamiltonian = H2_qubit_hamiltonian
alg = QPE(mol, reference=ref, trotter_number=2)
alg.run(guess_energy = -1,
t = 0.4,
nruns = 100,
success_prob = 0.5,
num_precise_bits = 8)
Egs = alg.get_gs_energy()
assert Egs == approx(E_fci, abs=1.1e-3)
def test_H2_experiment_perfect(self):
print('\n')
#the RHF H2 energy at equilibrium bond length
E_hf = -1.1166843870661929
#the H2 qubit hamiltonian
circ_vec = [
Circuit(),
build_circuit('Z_0'),
build_circuit('Z_1'),
build_circuit('Z_2'),
build_circuit('Z_3'),
build_circuit('Z_0 Z_1'),
build_circuit('Y_0 X_1 X_2 Y_3'),
build_circuit('Y_0 Y_1 X_2 X_3'),
build_circuit('X_0 X_1 Y_2 Y_3'),
build_circuit('X_0 Y_1 Y_2 X_3'),
build_circuit('Z_0 Z_2'),
build_circuit('Z_0 Z_3'),
build_circuit('Z_1 Z_2'),
build_circuit('Z_1 Z_3'),
build_circuit('Z_2 Z_3')
]
coef_vec = [
-0.098863969784274, 0.1711977489805748, 0.1711977489805748,
-0.222785930242875, -0.222785930242875, 0.1686221915724993,
0.0453222020577776, -0.045322202057777, -0.045322202057777,
0.0453222020577776, 0.1205448220329002, 0.1658670240906778,
0.1658670240906778, 0.1205448220329002, 0.1743484418396386
]
H2_qubit_hamiltonian = QubitOperator()
for i in range(len(circ_vec)):
H2_qubit_hamiltonian.add(coef_vec[i], circ_vec[i])
# circuit for making HF state
circ = Circuit()
circ.add(gate('X', 0, 0))
circ.add(gate('X', 1, 1))
TestExperiment = Experiment(4, circ, H2_qubit_hamiltonian, 1000000)
params2 = []
avg_energy = TestExperiment.perfect_experimental_avg(params2)
print('Perfectly Measured H2 Experimental Avg. Energy')
print(avg_energy)
print('H2 RHF Energy')
print(E_hf)
experimental_error = abs(avg_energy - E_hf)
assert experimental_error == approx(0, abs=1.0e-14)
