def test_ccr_offsite_even_aa(self):
a2 = FermionOperator(((2, 0), ))
a4 = FermionOperator(((4, 0), ))
self.assertTrue(
normal_ordered(a2 * a4).isclose(normal_ordered(-a4 * a2)))
self.assertTrue(
jordan_wigner(a2 * a4).isclose(jordan_wigner(-a4 * a2)))
def test_ccr_offsite_even_cc(self):
c2 = FermionOperator(((2, 1), ))
c4 = FermionOperator(((4, 1), ))
self.assertTrue(
normal_ordered(c2 * c4).isclose(normal_ordered(-c4 * c2)))
self.assertTrue(
jordan_wigner(c2 * c4).isclose(jordan_wigner(-c4 * c2)))
def test_ccr_offsite_odd_cc(self):
c1 = FermionOperator(((1, 1), ))
c4 = FermionOperator(((4, 1), ))
self.assertTrue(
normal_ordered(c1 * c4).isclose(normal_ordered(-c4 * c1)))
self.assertTrue(
jordan_wigner(c1 * c4).isclose(jordan_wigner(-c4 * c1)))
def test_ccr_offsite_odd_aa(self):
a1 = FermionOperator(((1, 0), ))
a4 = FermionOperator(((4, 0), ))
self.assertTrue(
normal_ordered(a1 * a4).isclose(normal_ordered(-a4 * a1)))
self.assertTrue(
jordan_wigner(a1 * a4).isclose(jordan_wigner(-a4 * a1)))
def test_get_molecular_operator(self):
coefficient = 3.
operators = ((2, 1), (3, 0), (0, 0), (3, 1))
op = FermionOperator(operators, coefficient)
molecular_operator = get_interaction_operator(op)
fermion_operator = get_fermion_operator(molecular_operator)
fermion_operator = normal_ordered(fermion_operator)
self.assertTrue(normal_ordered(op).isclose(fermion_operator))
def benchmark_fermion_math_and_normal_order(n_qubits, term_length, power):
"""Benchmark both arithmetic operators and normal ordering on fermions.
The idea is we generate two random FermionTerms, A and B, each acting
on n_qubits with term_length operators. We then compute
(A + B) ** power. This is costly that is the first benchmark. The second
benchmark is in normal ordering whatever comes out.
Args:
n_qubits: The number of qubits on which these terms act.
term_length: The number of operators in each term.
power: Int, the exponent to which to raise sum of the two terms.
Returns:
runtime_math: The time it takes to perform (A + B) ** power
runtime_normal_order: The time it takes to perform
FermionOperator.normal_order()
"""
# Generate random operator strings.
operators_a = [(numpy.random.randint(n_qubits), numpy.random.randint(2))]
operators_b = [(numpy.random.randint(n_qubits), numpy.random.randint(2))]
for operator_number in range(term_length):
# Make sure the operator is not trivially zero.
operator_a = (numpy.random.randint(n_qubits), numpy.random.randint(2))
while operator_a == operators_a[-1]:
operator_a = (numpy.random.randint(n_qubits),
numpy.random.randint(2))
operators_a += [operator_a]
# Do the same for the other operator.
operator_b = (numpy.random.randint(n_qubits), numpy.random.randint(2))
while operator_b == operators_b[-1]:
operator_b = (numpy.random.randint(n_qubits),
numpy.random.randint(2))
operators_b += [operator_b]
# Initialize FermionTerms and then sum them together.
fermion_term_a = FermionOperator(tuple(operators_a),
float(numpy.random.randn()))
fermion_term_b = FermionOperator(tuple(operators_b),
float(numpy.random.randn()))
fermion_operator = fermion_term_a + fermion_term_b
# Exponentiate.
start_time = time.time()
fermion_operator **= power
runtime_math = time.time() - start_time
# Normal order.
start_time = time.time()
normal_ordered(fermion_operator)
runtime_normal_order = time.time() - start_time
# Return.
return runtime_math, runtime_normal_order
def test_ccr_onsite(self):
c1 = FermionOperator(((1, 1), ))
a1 = hermitian_conjugated(c1)
self.assertTrue(
normal_ordered(
c1 *
a1).isclose(FermionOperator(()) - normal_ordered(a1 * c1)))
self.assertTrue(
jordan_wigner(
c1 * a1).isclose(QubitOperator(()) - jordan_wigner(a1 * c1)))
def test_jordan_wigner_twobody_interaction_op_allunique(self):
test_op = FermionOperator('1^ 2^ 3 4')
test_op += hermitian_conjugated(test_op)
retransformed_test_op = reverse_jordan_wigner(
jordan_wigner(get_interaction_operator(test_op)))
self.assertTrue(
normal_ordered(retransformed_test_op).isclose(
normal_ordered(test_op)))
def test_inverse_fourier_transform_2d(self):
grid = Grid(dimensions=2, scale=1.5, length=3)
spinless = True
geometry = [('H', (0, 0)), ('H', (0.5, 0.8))]
h_plane_wave = plane_wave_hamiltonian(grid, geometry, spinless, True)
h_dual_basis = plane_wave_hamiltonian(grid, geometry, spinless, False)
h_dual_basis_t = inverse_fourier_transform(h_dual_basis, grid,
spinless)
self.assertTrue(
normal_ordered(h_dual_basis_t).isclose(
normal_ordered(h_plane_wave)))
def test_xy(self):
xy = QubitOperator(((4, 'X'), (5, 'Y')), -2.j)
transmed_xy = reverse_jordan_wigner(xy)
retransmed_xy = jordan_wigner(transmed_xy)
expected1 = -2j * (FermionOperator(((4, 1), ), 1j) - FermionOperator(
((4, 0), ), 1j))
expected2 = (FermionOperator(((5, 1), )) - FermionOperator(((5, 0), )))
expected = expected1 * expected2
self.assertTrue(xy.isclose(retransmed_xy))
self.assertTrue(
normal_ordered(transmed_xy).isclose(normal_ordered(expected)))
def test_yy(self):
yy = QubitOperator(((2, 'Y'), (3, 'Y')), 2.)
transmed_yy = reverse_jordan_wigner(yy)
retransmed_yy = jordan_wigner(transmed_yy)
expected1 = -(FermionOperator(((2, 1), ), 2.) + FermionOperator(
((2, 0), ), 2.))
expected2 = (FermionOperator(((3, 1), )) - FermionOperator(((3, 0), )))
expected = expected1 * expected2
self.assertTrue(yy.isclose(retransmed_yy))
self.assertTrue(
normal_ordered(transmed_yy).isclose(normal_ordered(expected)))
def test_fourier_transform(self):
grid = Grid(dimensions=1, scale=1.5, length=3)
spinless_set = [True, False]
geometry = [('H', (0, )), ('H', (0.5, ))]
for spinless in spinless_set:
h_plane_wave = plane_wave_hamiltonian(grid, geometry, spinless,
True)
h_dual_basis = plane_wave_hamiltonian(grid, geometry, spinless,
False)
h_plane_wave_t = fourier_transform(h_plane_wave, grid, spinless)
self.assertTrue(
normal_ordered(h_plane_wave_t).isclose(
normal_ordered(h_dual_basis)))
def test_xx(self):
xx = QubitOperator(((3, 'X'), (4, 'X')), 2.)
transmed_xx = reverse_jordan_wigner(xx)
retransmed_xx = jordan_wigner(transmed_xx)
expected1 = (FermionOperator(((3, 1), ), 2.) - FermionOperator(
((3, 0), ), 2.))
expected2 = (FermionOperator(((4, 1), ), 1.) + FermionOperator(
((4, 0), ), 1.))
expected = expected1 * expected2
self.assertTrue(xx.isclose(retransmed_xx))
self.assertTrue(
normal_ordered(transmed_xx).isclose(normal_ordered(expected)))
def test_yx(self):
yx = QubitOperator(((0, 'Y'), (1, 'X')), -0.5)
transmed_yx = reverse_jordan_wigner(yx)
retransmed_yx = jordan_wigner(transmed_yx)
expected1 = 1j * (FermionOperator(((0, 1), )) + FermionOperator(
((0, 0), )))
expected2 = -0.5 * (FermionOperator(((1, 1), )) + FermionOperator(
((1, 0), )))
expected = expected1 * expected2
self.assertTrue(yx.isclose(retransmed_yx))
self.assertTrue(
normal_ordered(transmed_yx).isclose(normal_ordered(expected)))
def dual_basis_jellium_hamiltonian(grid_length,
dimension=3,
wigner_seitz_radius=10.,
n_particles=None,
spinless=True):
"""Return the jellium Hamiltonian with the given parameters.
Args:
grid_length (int): The number of spatial orbitals per dimension.
dimension (int): The dimension of the system.
wigner_seitz_radius (float): The radius per particle in Bohr.
n_particles (int): The number of particles in the system.
Defaults to half filling if not specified.
"""
n_qubits = grid_length**dimension
if not spinless:
n_qubits *= 2
if n_particles is None:
# Default to half filling fraction.
n_particles = n_qubits // 2
if not (0 <= n_particles <= n_qubits):
raise ValueError('n_particles must be between 0 and the number of'
' spin-orbitals.')
# Compute appropriate length scale.
length_scale = wigner_seitz_length_scale(wigner_seitz_radius, n_particles,
dimension)
grid = Grid(dimension, grid_length, length_scale)
hamiltonian = jellium_model(grid, spinless=spinless, plane_wave=False)
hamiltonian = normal_ordered(hamiltonian)
hamiltonian.compress()
return hamiltonian
def double_commutator(op1,
op2,
op3,
indices2=None,
indices3=None,
is_hopping_operator2=None,
is_hopping_operator3=None):
"""Return the double commutator [op1, [op2, op3]].
Assumes the operators are from the dual basis Hamiltonian.
Args:
op1, op2, op3 (FermionOperators): operators for the commutator.
indices2, indices3 (set): The indices op2 and op3 act on.
is_hopping_operator2 (bool): Whether op2 is a hopping operator.
is_hopping_operator3 (bool): Whether op3 is a hopping operator.
Returns:
The double commutator of the given operators.
"""
if is_hopping_operator2 and is_hopping_operator3:
indices2 = set(indices2)
indices3 = set(indices3)
# Determine which indices both op2 and op3 act on.
try:
intersection, = indices2.intersection(indices3)
except ValueError:
return FermionOperator.zero()
# Remove the intersection from the set of indices, since it will get
# cancelled out in the final result.
indices2.remove(intersection)
indices3.remove(intersection)
# Find the indices of the final output hopping operator.
index2, = indices2
index3, = indices3
coeff2 = op2.terms[list(op2.terms)[0]]
coeff3 = op3.terms[list(op3.terms)[0]]
commutator23 = (FermionOperator(
((index2, 1), (index3, 0)), coeff2 * coeff3) + FermionOperator(
((index3, 1), (index2, 0)), -coeff2 * coeff3))
else:
commutator23 = normal_ordered(commutator(op2, op3))
return normal_ordered(commutator(op1, commutator23))
def get_qubit_expectations(self, qubit_operator):
"""Return expectations of QubitOperator in new QubitOperator.
Args:
qubit_operator: QubitOperator instance to be evaluated on
this InteractionRDM.
Returns:
QubitOperator: QubitOperator with coefficients
corresponding to expectation values of those operators.
Raises:
InteractionRDMError: Observable not contained in 1-RDM or 2-RDM.
"""
from fermilib.transforms import reverse_jordan_wigner
qubit_operator_expectations = copy.deepcopy(qubit_operator)
del qubit_operator_expectations.terms[()]
for qubit_term in qubit_operator_expectations.terms:
expectation = 0.
# Map qubits back to fermions.
reversed_fermion_operators = reverse_jordan_wigner(
QubitOperator(qubit_term), self.n_qubits)
reversed_fermion_operators = normal_ordered(
reversed_fermion_operators)
# Loop through fermion terms.
for fermion_term in reversed_fermion_operators.terms:
coefficient = reversed_fermion_operators.terms[fermion_term]
# Handle molecular term.
if FermionOperator(fermion_term).is_molecular_term():
if not fermion_term:
expectation += coefficient
else:
indices = [operator[0] for operator in fermion_term]
rdm_element = self[indices]
expectation += rdm_element * coefficient
# Handle non-molecular terms.
elif len(fermion_term) > 4:
raise InteractionRDMError('Observable not contained '
'in 1-RDM or 2-RDM.')
qubit_operator_expectations.terms[qubit_term] = expectation
return qubit_operator_expectations
def get_interaction_operator(fermion_operator, n_qubits=None):
"""Convert a 2-body fermionic operator to InteractionOperator.
This function should only be called on fermionic operators which
consist of only a_p^\dagger a_q and a_p^\dagger a_q^\dagger a_r a_s
terms. The one-body terms are stored in a matrix, one_body[p, q], and
the two-body terms are stored in a tensor, two_body[p, q, r, s].
Returns:
interaction_operator: An instance of the InteractionOperator class.
Raises:
TypeError: Input must be a FermionOperator.
TypeError: FermionOperator does not map to InteractionOperator.
Warning:
Even assuming that each creation or annihilation operator appears
at most a constant number of times in the original operator, the
runtime of this method is exponential in the number of qubits.
"""
if not isinstance(fermion_operator, FermionOperator):
raise TypeError('Input must be a FermionOperator.')
if n_qubits is None:
n_qubits = count_qubits(fermion_operator)
if n_qubits < count_qubits(fermion_operator):
n_qubits = count_qubits(fermion_operator)
# Normal order the terms and initialize.
fermion_operator = normal_ordered(fermion_operator)
constant = 0.
one_body = numpy.zeros((n_qubits, n_qubits), complex)
two_body = numpy.zeros((n_qubits, n_qubits, n_qubits, n_qubits), complex)
# Loop through terms and assign to matrix.
for term in fermion_operator.terms:
coefficient = fermion_operator.terms[term]
# Handle constant shift.
if len(term) == 0:
constant = coefficient
elif len(term) == 2:
# Handle one-body terms.
if [operator[1] for operator in term] == [1, 0]:
p, q = [operator[0] for operator in term]
one_body[p, q] = coefficient
else:
raise InteractionOperatorError('FermionOperator does not map '
'to InteractionOperator.')
elif len(term) == 4:
# Handle two-body terms.
if [operator[1] for operator in term] == [1, 1, 0, 0]:
p, q, r, s = [operator[0] for operator in term]
two_body[p, q, r, s] = coefficient
else:
raise InteractionOperatorError('FermionOperator does not map '
'to InteractionOperator.')
else:
# Handle non-molecular Hamiltonian.
raise InteractionOperatorError('FermionOperator does not map '
'to InteractionOperator.')
# Form InteractionOperator and return.
interaction_operator = InteractionOperator(constant, one_body, two_body)
return interaction_operator
def hartree_fock_state_jellium(grid,
n_electrons,
spinless=True,
plane_wave=False):
"""Give the Hartree-Fock state of jellium.
Args:
grid (Grid): The discretization to use.
n_electrons (int): Number of electrons in the system.
spinless (bool): Whether to use the spinless model or not.
plane_wave (bool): Whether to return the Hartree-Fock state in
the plane wave (True) or dual basis (False).
Notes:
The jellium model is built up by filling the lowest-energy
single-particle states in the plane-wave Hamiltonian until
n_electrons states are filled.
"""
# Get the jellium Hamiltonian in the plane wave basis.
hamiltonian = jellium_model(grid, spinless, plane_wave=True)
hamiltonian = normal_ordered(hamiltonian)
hamiltonian.compress()
# Enumerate the single-particle states.
n_single_particle_states = (grid.length**grid.dimensions)
if not spinless:
n_single_particle_states *= 2
# Compute the energies for each of the single-particle states.
single_particle_energies = numpy.zeros(n_single_particle_states,
dtype=float)
for i in range(n_single_particle_states):
single_particle_energies[i] = hamiltonian.terms.get(((i, 1), (i, 0)),
0.0)
# The number of occupied single-particle states is the number of electrons.
# Those states with the lowest single-particle energies are occupied first.
occupied_states = single_particle_energies.argsort()[:n_electrons]
if plane_wave:
# In the plane wave basis the HF state is a single determinant.
hartree_fock_state_index = numpy.sum(2**occupied_states)
hartree_fock_state = csr_matrix(
([1.0], ([hartree_fock_state_index], [0])),
shape=(2**n_single_particle_states, 1))
else:
# Inverse Fourier transform the creation operators for the state to get
# to the dual basis state, then use that to get the dual basis state.
hartree_fock_state_creation_operator = FermionOperator.identity()
for state in occupied_states[::-1]:
hartree_fock_state_creation_operator *= (FermionOperator(
((int(state), 1), )))
dual_basis_hf_creation_operator = inverse_fourier_transform(
hartree_fock_state_creation_operator, grid, spinless)
dual_basis_hf_creation = normal_ordered(
dual_basis_hf_creation_operator)
# Initialize the HF state as a sparse matrix.
hartree_fock_state = csr_matrix(([], ([], [])),
shape=(2**n_single_particle_states, 1),
dtype=complex)
# Populate the elements of the HF state in the dual basis.
for term in dual_basis_hf_creation.terms:
index = 0
for operator in term:
index += 2**operator[0]
hartree_fock_state[index, 0] = dual_basis_hf_creation.terms[term]
return hartree_fock_state
