def transform_interaction_operator(transformation: str,
input_operator: Union[str,
SymbolicOperator]):
"""Transform an interaction operator through either the Bravyi-Kitaev or Jordan-Wigner transformations.
The results are serialized into a JSON under the files: "transformed-operator.json" and "timing.json"
ARGS:
transformation (str): The transformation to use. Either "Jordan-Wigner" or "Bravyi-Kitaev"
input_operator (Union[str, SymbolicOperator]): The interaction operator to transform
"""
if isinstance(input_operator, str):
input_operator = load_interaction_operator(input_operator)
if transformation == "Jordan-Wigner":
transformation = jordan_wigner
elif transformation == "Bravyi-Kitaev":
input_operator = get_fermion_operator(input_operator)
transformation = bravyi_kitaev
else:
raise RuntimeError("Unrecognized transformation ", transformation)
start_time = time.time()
transformed_operator = transformation(input_operator)
walltime = time.time() - start_time
save_qubit_operator(transformed_operator, "transformed-operator.json")
save_timing(walltime, "timing.json")
def get_local_zero_state_operator(number_of_qubits: int):
operator = IsingOperator("")
for qubit_index in range(number_of_qubits):
operator -= IsingOperator("Z{}".format(qubit_index),
1 / (number_of_qubits))
save_qubit_operator(operator, "qubit-operator.json")
def interpolate_qubit_operators(
reference_qubit_operator: Union[InteractionOperator, str],
target_qubit_operator: Union[InteractionOperator, str],
epsilon: float = 0.5,
):
"""Produce a qubit operator which is the interpolation of two operators through the
function: epsilon * target_qubit_operator + (1.0 - epsilon) *
reference_qubit_operator.
Outputs are serialized to JSON under the file: "qubit-operator.json"
Args:
reference_qubit_operator: The initial operator
target_qubit_operator: The target operator
epsilon: The parameterization between the two operators. Default value is 0.5
"""
reference_qubit_operator = load_qubit_operator(reference_qubit_operator)
target_qubit_operator = load_qubit_operator(target_qubit_operator)
if epsilon > 1.0 or epsilon < 0.0:
raise ValueError("epsilon must be in the range [0.0, 1.0]")
output_qubit_operator = (
epsilon * target_qubit_operator + (1.0 - epsilon) * reference_qubit_operator
)
save_qubit_operator(output_qubit_operator, "qubit-operator.json")
def create_one_qubit_operator(x_coeff: float, y_coeff: float, z_coeff: float,
constant: float) -> None:
qubit_operator = (x_coeff * QubitOperator("X0") +
y_coeff * QubitOperator("Y0") +
z_coeff * QubitOperator("Z0") + constant * QubitOperator(
()))
save_qubit_operator(qubit_operator, "qubit_operator.json")
def get_one_qubit_hydrogen_hamiltonian(
interaction_operator: Union[InteractionOperator, str]
):
"""Generate a one qubit H2 hamiltonian from a corresponding interaction operator.
Original H2 hamiltonian will be reduced to a 2 x 2 matrix defined on a subspace
spanned by |0011> and |1100> and expanded in terms of I, X, Y, and Z matrices
Args:
interaction_operator: The input interaction operator
"""
if isinstance(interaction_operator, str):
interaction_operator = load_interaction_operator(interaction_operator)
fermion_h = get_fermion_operator(interaction_operator)
# H00
H00 = normal_ordered(FermionOperator("0 1") * fermion_h * FermionOperator("1^ 0^"))
H00 = H00.terms[()]
# H11
H11 = normal_ordered(FermionOperator("2 3") * fermion_h * FermionOperator("3^ 2^"))
H11 = H11.terms[()]
# H10
H10 = normal_ordered(FermionOperator("2 3") * fermion_h * FermionOperator("1^ 0^"))
H10 = H10.terms[()]
# H01
H01 = np.conj(H10)
one_qubit_h_matrix = np.array([[H00, H01], [H10, H11]])
pauli_x = np.array([[0.0, 1.0], [1.0, 0.0]])
pauli_y = np.array([[0.0, -1.0j], [1.0j, 0.0]])
pauli_z = np.array([[1.0, 0.0], [0.0, -1.0]])
r_id = 0.5 * np.trace(one_qubit_h_matrix)
r_x = 0.5 * np.trace(one_qubit_h_matrix @ pauli_x)
r_y = 0.5 * np.trace(one_qubit_h_matrix @ pauli_y)
r_z = 0.5 * np.trace(one_qubit_h_matrix @ pauli_z)
one_qubit_h = (
r_id * QubitOperator("")
+ r_x * QubitOperator("X0")
+ r_y * QubitOperator("Y0")
+ r_z * QubitOperator("Z0")
)
save_qubit_operator(one_qubit_h, "qubit-operator.json")
def get_one_qubit_hydrogen_hamiltonian(
interaction_operator: Union[InteractionOperator, str]):
"""Generate a one qubit H2 hamiltonian from a corresponding interaction operator.
Original H2 hamiltonian will be reduced to a 2 x 2 matrix defined on a subspace spanned
by |0011> and |1100> and expanded in terms of I, X, Y, and Z matrices
ARGS:
interaction_operator (Union[InteractionOperator, str]): The input interaction operator
"""
if isinstance(interaction_operator, str):
interaction_operator = load_interaction_operator(interaction_operator)
fermion_h = get_fermion_operator(interaction_operator)
# H00
H00 = normal_ordered(
FermionOperator('0 1') * fermion_h * FermionOperator('1^ 0^'))
H00 = H00.terms[()]
# H11
H11 = normal_ordered(
FermionOperator('2 3') * fermion_h * FermionOperator('3^ 2^'))
H11 = H11.terms[()]
# H10
H10 = normal_ordered(
FermionOperator('2 3') * fermion_h * FermionOperator('1^ 0^'))
H10 = H10.terms[()]
# H01
H01 = np.conj(H10)
one_qubit_h_matrix = np.array([[H00, H01], [H10, H11]])
pauli_x = np.array([[0., 1.], [1., 0.]])
pauli_y = np.array([[0., -1.j], [1.j, 0.]])
pauli_z = np.array([[1., 0.], [0., -1.]])
r_id = 0.5 * np.trace(one_qubit_h_matrix)
r_x = 0.5 * np.trace(one_qubit_h_matrix @ pauli_x)
r_y = 0.5 * np.trace(one_qubit_h_matrix @ pauli_y)
r_z = 0.5 * np.trace(one_qubit_h_matrix @ pauli_z)
one_qubit_h = r_id * QubitOperator('') + r_x * QubitOperator(
'X0') + r_y * QubitOperator('Y0') + r_z * QubitOperator('Z0')
save_qubit_operator(one_qubit_h, 'qubit-operator.json')
def create_random_qubitop(nqubits: int, nterms: int):
output_qubit_operator = _create_random_qubitop(nqubits, nterms)
save_qubit_operator(output_qubit_operator, "qubit-operator.json")
def get_graph_partition_hamiltonian(graph, scale_factor=1.0, offset=0.0):
graph_object = load_graph(graph)
hamiltonian = GraphPartitioning().get_hamiltonian(
graph_object, scale_factor=scale_factor, offset=offset)
save_qubit_operator(hamiltonian, "hamiltonian.json")
def get_maxcut_hamiltonian(graph, scale_factor=1.0, offset=0.0):
graph_object = load_graph(graph)
hamiltonian = MaxCut().get_hamiltonian(graph_object,
scale_factor=scale_factor,
offset=offset)
save_qubit_operator(hamiltonian, "hamiltonian.json")
def get_maxcut_hamiltonian(graph, scaling=1.0, shifted=False):
graph_object = load_graph(graph)
hamiltonian = _get_maxcut_hamiltonian(
graph_object, scaling=scaling, shifted=shifted
)
save_qubit_operator(hamiltonian, "hamiltonian.json")
