def get_sparse_operator(operator, n_qubits=None, trunc=None, hbar=1.):
r"""Map an operator to a sparse matrix.
If the input is not a QubitOperator, the Jordan-Wigner Transform is used.
Args:
operator: Currently supported operators include:
FermionOperator, QubitOperator, DiagonalCoulombHamiltonian,
PolynomialTensor, BosonOperator, QuadOperator.
n_qubits(int): Number qubits in the system Hilbert space.
Applicable only to fermionic systems.
trunc (int): The size at which the Fock space should be truncated.
Applicable only to bosonic systems.
hbar (float): the value of hbar to use in the definition of the
canonical commutation relation [q_i, p_j] = \delta_{ij} i hbar.
Applicable only to the QuadOperator.
"""
if isinstance(operator, (DiagonalCoulombHamiltonian, PolynomialTensor)):
return jordan_wigner_sparse(get_fermion_operator(operator))
elif isinstance(operator, FermionOperator):
return jordan_wigner_sparse(operator, n_qubits)
elif isinstance(operator, QubitOperator):
return qubit_operator_sparse(operator, n_qubits)
elif isinstance(operator, (BosonOperator, QuadOperator)):
return boson_operator_sparse(operator, trunc, hbar)
else:
raise TypeError('Failed to convert a {} to a sparse matrix.'.format(
type(operator).__name__))
def get_sparse_operator(operator, n_qubits=None):
"""Map a FermionOperator, QubitOperator, or PolyomialTensor to a
sparse matrix."""
if isinstance(operator, PolynomialTensor):
return polynomial_tensor_sparse(operator)
elif isinstance(operator, FermionOperator):
return jordan_wigner_sparse(operator, n_qubits)
elif isinstance(operator, QubitOperator):
return qubit_operator_sparse(operator, n_qubits)
def get_sparse_operator(operator, n_qubits=None):
"""Map a Fermion, Qubit, or InteractionOperator to a SparseOperator."""
if isinstance(operator, PolynomialTensor):
return get_sparse_polynomial_tensor(operator)
elif isinstance(operator, FermionOperator):
return jordan_wigner_sparse(operator, n_qubits)
elif isinstance(operator, QubitOperator):
if n_qubits is None:
n_qubits = count_qubits(operator)
return qubit_operator_sparse(operator, n_qubits)
def jw_get_ground_state_at_particle_number(particle_number, qubit_operator):
qubit_operator = load_qubit_operator(qubit_operator)
sparse_matrix = qubit_operator_sparse(qubit_operator)
ground_energy, ground_state_amplitudes = _jw_get_ground_state_at_particle_number(
sparse_matrix, particle_number
)
ground_state = Wavefunction(ground_state_amplitudes)
value_estimate = ValueEstimate(ground_energy)
save_wavefunction(ground_state, "ground-state.json")
save_value_estimate(value_estimate, "value-estimate.json")
def get_sparse_operator(operator, n_qubits=None):
"""Map an operator to a sparse matrix.
If the input is not a QubitOperator, the Jordan-Wigner Transform is used.
"""
if isinstance(operator, (DiagonalCoulombHamiltonian, PolynomialTensor)):
return jordan_wigner_sparse(get_fermion_operator(operator))
elif isinstance(operator, FermionOperator):
return jordan_wigner_sparse(operator, n_qubits)
elif isinstance(operator, QubitOperator):
return qubit_operator_sparse(operator, n_qubits)
else:
raise TypeError('Failed to convert a {} to a sparse matrix.'.format(
type(operator).__name__))
def test_qubitop_matrix_converion(self):
# Given
m = 4
n = 2**m
TOL = 10**-15
random.seed(RNDSEED)
A = np.array([[random.uniform(-1, 1) for x in range(n)]
for y in range(n)])
# When
A_qubitop = get_qubitop_from_matrix(A)
A_qubitop_matrix = np.array(qubit_operator_sparse(A_qubitop).todense())
test_matrix = A_qubitop_matrix - A
# Then
for row in test_matrix:
for elem in row:
self.assertEqual(abs(elem) < TOL, True)
