def test_wrong_final_state():
qb0 = WeakQubitRef(engine=None, idx=0)
qb1 = WeakQubitRef(engine=None, idx=1)
cmd = Command(None, StatePreparation([0, 1j]), qubits=([qb0, qb1], ))
with pytest.raises(ValueError):
stateprep2cnot._decompose_state_preparation(cmd)
cmd2 = Command(None, StatePreparation([0, 0.999j]), qubits=([qb0], ))
with pytest.raises(ValueError):
stateprep2cnot._decompose_state_preparation(cmd2)
def test_state_preparation(n_qubits, zeros):
engine_list = restrictedgateset.get_engine_list(one_qubit_gates=(Ry, Rz,
Ph))
eng = projectq.MainEngine(engine_list=engine_list)
qureg = eng.allocate_qureg(n_qubits)
eng.flush()
f_state = [
0.2 + 0.1 * x * cmath.exp(0.1j + 0.2j * x) for x in range(2**n_qubits)
]
if zeros:
for i in range(2**(n_qubits - 1)):
f_state[i] = 0
norm = 0
for amplitude in f_state:
norm += abs(amplitude)**2
f_state = [x / math.sqrt(norm) for x in f_state]
StatePreparation(f_state) | qureg
eng.flush()
wavefunction = deepcopy(eng.backend.cheat()[1])
# Test that simulator hasn't reordered wavefunction
mapping = eng.backend.cheat()[0]
for key in mapping:
assert mapping[key] == key
All(Measure) | qureg
eng.flush()
assert np.allclose(wavefunction, f_state, rtol=1e-10, atol=1e-10)
def circuit_generator(eng, target_state, mapper):
"""
circuit_generator makes use of the above functions and reutrn
a projectq circuit in string form
Args:
eng：
target_state: The final state of the algorithm.
It is a list or array with 2^n complex numbers
mapper: A string representing the connected qubits in the device.
E.g. "5\n0,1\n1,2\n1,3\n2,3\n2,4\n3,4"
Returns:
The quantum circuit prepare the targets_state that can be
implemented directly on the device restricted by mapper
"""
n, directed_mapper = read_mapper(mapper)
connections = set([(j, i) for i, j in directed_mapper] + directed_mapper)
LocalOptimizer.receive = my_receive
LocalOptimizer.add_swap = add_swap
LocalOptimizer.graph = Graph(connections)
LocalOptimizer.connections = connections
n = int(np.log2(len(target_state)))
LocalOptimizer.qureg = qureg
eng.flush()
with Capturing() as output:
StatePreparation(target_state) | qureg
eng.flush()
# mappting, wave_func = deepcopy(eng.backend.cheat())
# print(sum(wave_func.conj() * target_state))
circuit = "; ".join(output)
return circuit.replace("Qureg", "qubit")
def test_X_no_eigenvectors():
rule_set = DecompositionRuleSet(
modules=[pe, dqft, stateprep2cnot, ucr2cnot])
eng = MainEngine(
backend=Simulator(),
engine_list=[
AutoReplacer(rule_set),
],
)
N = 100
results = np.array([])
results_plus = np.array([])
results_minus = np.array([])
for i in range(N):
autovector = eng.allocate_qureg(1)
amplitude0 = (np.sqrt(2) + np.sqrt(6)) / 4.0
amplitude1 = (np.sqrt(2) - np.sqrt(6)) / 4.0
StatePreparation([amplitude0, amplitude1]) | autovector
unit = X
ancillas = eng.allocate_qureg(1)
QPE(unit) | (ancillas, autovector)
All(Measure) | ancillas
fasebinlist = [int(q) for q in ancillas]
fasebin = ''.join(str(j) for j in fasebinlist)
faseint = int(fasebin, 2)
phase = faseint / (2.0**(len(ancillas)))
results = np.append(results, phase)
Tensor(H) | autovector
if np.allclose(phase, 0.0, rtol=1e-1):
results_plus = np.append(results_plus, phase)
All(Measure) | autovector
autovector_result = int(autovector)
assert autovector_result == 0
elif np.allclose(phase, 0.5, rtol=1e-1):
results_minus = np.append(results_minus, phase)
All(Measure) | autovector
autovector_result = int(autovector)
assert autovector_result == 1
eng.flush()
total = len(results_plus) + len(results_minus)
plus_probability = len(results_plus) / N
assert total == pytest.approx(N, abs=5)
assert plus_probability == pytest.approx(
1.0 / 4.0, abs=1e-1
), "Statistics on |+> probability are not correct (%f vs. %f)" % (
plus_probability,
1.0 / 4.0,
)
def W(eng, individual_terms, initial_wavefunction, ancilla_qubits,
system_qubits):
"""
Applies the W operator as defined in arXiv:1711.11025.
Args:
eng(MainEngine): compiler engine
individual_terms(list<QubitOperator>): list of individual unitary
QubitOperators. It applies
individual_terms[0] if ancilla
qubits are in state |0> where
ancilla_qubits[0] is the least
significant bit.
initial_wavefunction: Initial wavefunction of the ancilla qubits
ancilla_qubits(Qureg): ancilla quantum register in state |0>
system_qubits(Qureg): system quantum register
"""
# Apply V:
for ancilla_state in range(len(individual_terms)):
with Compute(eng):
for bit_pos in range(len(ancilla_qubits)):
if not (ancilla_state >> bit_pos) & 1:
X | ancilla_qubits[bit_pos]
with Control(eng, ancilla_qubits):
individual_terms[ancilla_state] | system_qubits
Uncompute(eng)
# Apply S: 1) Apply B^dagger
with Compute(eng):
with Dagger(eng):
StatePreparation(initial_wavefunction) | ancilla_qubits
# Apply S: 2) Apply I-2|0><0|
with Compute(eng):
All(X) | ancilla_qubits
with Control(eng, ancilla_qubits[:-1]):
Z | ancilla_qubits[-1]
Uncompute(eng)
# Apply S: 3) Apply B
Uncompute(eng)
# Could also be omitted and added when calculating the eigenvalues:
Ph(math.pi) | system_qubits[0]
def test_invalid_arguments():
qb0 = WeakQubitRef(engine=None, idx=0)
qb1 = WeakQubitRef(engine=None, idx=1)
cmd = Command(None, StatePreparation([0, 1j]), qubits=([qb0], [qb1]))
with pytest.raises(ValueError):
stateprep2cnot._decompose_state_preparation(cmd)
eng = projectq.MainEngine()
system_qubits = eng.allocate_qureg(num_qubits)
# Create a normalized equal superposition of the two eigenstates for numerical testing:
initial_state_norm = 0.
initial_state = [i + j for i, j in zip(eigenvectors[0], eigenvectors[1])]
for amplitude in initial_state:
initial_state_norm += abs(amplitude)**2
normalized_initial_state = [
amp / math.sqrt(initial_state_norm) for amp in initial_state
]
#initialize system qubits in this state:
StatePreparation(normalized_initial_state) | system_qubits
individual_terms = []
initial_ancilla_wavefunction = []
for term in normalized_hamiltonian.terms:
coefficient = normalized_hamiltonian.terms[term]
initial_ancilla_wavefunction.append(math.sqrt(abs(coefficient)))
if coefficient < 0:
individual_terms.append(QubitOperator(term, -1))
else:
individual_terms.append(QubitOperator(term))
# Calculate the number of ancilla qubits required and pad
# the ancilla wavefunction with zeros:
num_ancilla_qubits = int(math.ceil(math.log(len(individual_terms), 2)))
required_padding = 2**num_ancilla_qubits - len(initial_ancilla_wavefunction)
#Intialization
ME = MainEngine()
#Input
x,y = map(complex,input("enter the state: ").split())
#Normalization
norm = np.sqrt(np.abs(x**2) + np.abs(y**2))
x /= norm
y /= norm
#Qubit allocation , define a state for the qubit,and measure it.
qubit = ME.allocate_qubit()
StatePreparation([x,y]) | qubit
Measure | qubit
read = int(qubit)
#Measuring output
ME.flush()
print(read)
#State vector to sphereical coordinates
phi = np.angle(y) - np.angle(x)
theta = 2 * np.arccos(np.abs(x))
vec = [np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)]
#Visualization