def fill_pool(self):
""" This function populates an operator pool with SQOperator objects.
"""
if self._pool_type in {'sa_SD', 'GSD', 'SD', 'SDT', 'SDTQ', 'SDTQP', 'SDTQPH'}:
self._pool_obj = qf.SQOpPool()
self._pool_obj.set_orb_spaces(self._ref)
self._pool_obj.fill_pool(self._pool_type)
elif isinstance(self._pool_type, qf.SQOpPool):
self._pool_obj = self._pool_type
else:
raise ValueError('Invalid operator pool type specified.')
# If possible, impose symmetry restriction to operator pool
# Currently, symmetry is supported for system_type='molecule' and build_type='psi4'
if hasattr(self._sys, 'point_group'):
temp_sq_pool = qf.SQOpPool()
for sq_operator in self._pool_obj.terms():
create = sq_operator[1].terms()[0][1]
annihilate = sq_operator[1].terms()[0][2]
if sq_op_find_symmetry(self._sys.orb_irreps_to_int, create, annihilate) == self._irrep:
temp_sq_pool.add(sq_operator[0], sq_operator[1])
self._pool_obj = temp_sq_pool
self._Nm = [len(operator.jw_transform().terms()) for _, operator in self._pool_obj]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._curr_energy = 0
self._Nm = []
self._tamps = []
self._tops = []
self._pool_obj = qf.SQOpPool()
kwargs.setdefault('irrep', None)
if hasattr(self._sys, 'point_group'):
irreps = list(range(len(self._sys.point_group[1])))
if kwargs['irrep'] is None:
print('\nWARNING: The {0} point group was detected, but no irreducible representation was specified.\n'
' Proceeding with totally symmetric.\n'.format(self._sys.point_group[0].capitalize()))
self._irrep = 0
elif kwargs['irrep'] in irreps:
self._irrep = kwargs['irrep']
else:
raise ValueError("{0} is not an irreducible representation of {1}.\n"
" Choose one of {2} corresponding to the\n"
" {3} irreducible representations of {1}".format(kwargs['irrep'],
self._sys.point_group[0].capitalize(),
irreps,
self._sys.point_group[1]))
elif kwargs['irrep'] is not None:
print('\nWARNING: Point group information not found.\n'
' Ignoring "irrep" and proceeding without symmetry.\n')
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._curr_energy = 0
self._Nm = []
self._tamps = []
self._tops = []
self._pool = []
self._pool_obj = qf.SQOpPool()
def build_expansion_pool(self):
print('\n==> Building expansion pool <==')
self._sig = qf.QuantumOpPool()
if (self._expansion_type == 'complete_qubit'):
if (self._nqb > 6):
raise ValueError(
'Using complete qubits expansion will result in a very large number of terms!'
)
self._sig.fill_pool("complete_qubit", self._ref)
elif (self._expansion_type == 'cqoy'):
self._sig.fill_pool("cqoy", self._ref)
elif (self._expansion_type
in {'SD', 'GSD', 'SDT', 'SDTQ', 'SDTQP', 'SDTQPH'}):
P = qf.SQOpPool()
P.set_orb_spaces(self._ref)
P.fill_pool(self._expansion_type)
sig_temp = P.get_quantum_operator("commuting_grp_lex", False)
# Filter the generated operators, so that only those with an odd number of Y gates are allowed.
# See section "Real Hamiltonians and states" in the SI of Motta for theoretical justification.
# Briefly, this method solves Ax=b, but all b elements with an odd number of Y gates are imaginary and
# thus vanish. This method will not be correct for non-real Hamiltonians or states.
for alph, rho in sig_temp.terms():
nygates = 0
temp_rho = qf.QuantumCircuit()
for gate in rho.gates():
temp_rho.add_gate(
qf.gate(gate.gate_id(), gate.target(), gate.control()))
if (gate.gate_id() == "Y"):
nygates += 1
if (nygates % 2 == 1):
rho_op = qf.QuantumOperator()
rho_op.add_term(1.0, temp_rho)
self._sig.add_term(1.0, rho_op)
elif (self._expansion_type == 'test'):
self._sig.fill_pool("test", self._ref)
else:
raise ValueError('Invalid expansion type specified.')
self._NI = len(self._sig.terms())
def fill_pool(self):
self._pool_obj = qf.SQOpPool()
self._pool_obj.set_orb_spaces(self._ref)
if self._pool_type in {
'sa_SD', 'GSD', 'SD', 'SDT', 'SDTQ', 'SDTQP', 'SDTQPH'
}:
self._pool_obj.fill_pool(self._pool_type)
else:
raise ValueError('Invalid operator pool type specified.')
self._pool = self._pool_obj.terms()
self._Nm = [
len(operator.jw_transform().terms()) for _, operator in self._pool
]
def ansatz_circuit(self, amplitudes=None):
""" This function returns the Circuit object built
from the appropriate amplitudes.
Parameters
----------
amplitudes : list
A list of parameters that define the variational degrees of freedom in
the state preparation circuit Uvqc. This is needed for the scipy minimizer.
"""
temp_pool = qf.SQOpPool()
tamps = self._tamps if amplitudes is None else amplitudes
for tamp, top in zip(tamps, self._tops):
temp_pool.add(tamp, self._pool_obj[top][1])
A = temp_pool.get_qubit_operator('commuting_grp_lex')
U, phase1 = trotterize(A, trotter_number=self._trotter_number)
if phase1 != 1.0 + 0.0j:
raise ValueError("Encountered phase change, phase not equal to (1.0 + 0.0i)")
return U
def run(self,
spqe_thresh=1.0e-2,
spqe_maxiter=20,
dt=0.001,
M_omega='inf',
opt_thresh=1.0e-5,
opt_maxiter=30,
use_cumulative_thresh=True):
if (self._state_prep_type != 'occupation_list'):
raise ValueError(
"SPQE implementation can only handle occupation_list Hartree-Fock reference."
)
self._spqe_thresh = spqe_thresh
self._spqe_maxiter = spqe_maxiter
self._dt = dt
if (M_omega != 'inf'):
self._M_omega = int(M_omega)
else:
self._M_omega = M_omega
self._use_cumulative_thresh = use_cumulative_thresh
self._opt_thresh = opt_thresh
self._opt_maxiter = opt_maxiter
self._nbody_counts = []
self._n_classical_params_lst = []
self._results = []
self._energies = []
self._grad_norms = []
self._tops = []
self._tamps = []
self._converged = False
self._res_vec_evals = 0
self._res_m_evals = 0
self._curr_energy = 0.0
self._n_classical_params = 0
self._n_cnot = 0
self._n_cnot_lst = []
self._n_pauli_trm_measures = 0
self._n_pauli_trm_measures_lst = []
self.print_options_banner()
self._Nm = []
self._pool_type = 'full'
self._eiH, self._eiH_phase = trotterize(
self._qb_ham,
factor=self._dt * (0.0 + 1.0j),
trotter_number=self._trotter_number)
for occupation in self._ref:
if occupation:
self._nbody_counts.append(0)
self._pool_obj = qf.SQOpPool()
for I in range(2**self._nqb):
self._pool_obj.add_term(0.0, self.get_op_from_basis_idx(I))
self.build_orb_energies()
spqe_iter = 0
hit_maxiter = 0
if (self._print_summary_file):
f = open("summary.dat", "w+", buffering=1)
f.write(
f"#{'Iter(k)':>8}{'E(k)':>14}{'N(params)':>17}{'N(CNOT)':>18}{'N(measure)':>20}\n"
)
f.write(
'#-------------------------------------------------------------------------------\n'
)
while not self._converged:
print('\n\n -----> SPQE iteration ', spqe_iter, ' <-----\n')
self.update_ansatz()
if self._converged:
break
if (self._verbose):
print('\ntoperators included from pool: \n', self._tops)
print('\ntamplitudes for tops: \n', self._tamps)
self.solve()
if (self._verbose):
print('\ntamplitudes for tops post solve: \n',
np.real(self._tamps))
if (self._print_summary_file):
f.write(
f' {spqe_iter:7} {self._energies[-1]:+15.9f} {len(self._tamps):8} {self._n_cnot_lst[-1]:10} {sum(self._n_pauli_trm_measures_lst):12}\n'
)
spqe_iter += 1
if spqe_iter > self._spqe_maxiter - 1:
hit_maxiter = 1
break
if (self._print_summary_file):
f.close()
if hit_maxiter:
self._Egs = self.get_final_energy(hit_max_spqe_iter=1)
self._Egs = self.get_final_energy()
print("\n\n")
print("---> Final n-body excitation counts in SPQE ansatz <---")
print("\n")
print(f"{'Excitaion order':>20}{'Number of operators':>30}")
print('---------------------------------------------------------')
for l, nl in enumerate(self._nbody_counts):
print(f"{l+1:12} {nl:14}")
print('\n\n')
print(
f"{'Iter(k)':>8}{'E(k)':>14}{'N(params)':>17}{'N(CNOT)':>18}{'N(measure)':>20}"
)
print(
'-------------------------------------------------------------------------------'
)
for k, Ek in enumerate(self._energies):
print(
f' {k:7} {Ek:+15.9f} {self._n_classical_params_lst[k]:8} {self._n_cnot_lst[k]:10} {sum(self._n_pauli_trm_measures_lst[:k+1]):12}'
)
self._n_classical_params = len(self._tamps)
self._n_cnot = self._n_cnot_lst[-1]
self._n_pauli_trm_measures = sum(self._n_pauli_trm_measures_lst)
self.print_summary_banner()
self.verify_run()
def get_residual_vector(self, trial_amps):
if (self._pool_type == 'sa_SD'):
raise ValueError(
'Must use single term particle-hole nbody operators for residual calculation'
)
temp_pool = qforte.SQOpPool()
for param, top in zip(trial_amps, self._tops):
temp_pool.add_term(param, self._pool[top][1])
A = temp_pool.get_quantum_operator('commuting_grp_lex')
U, U_phase = trotterize(A, trotter_number=self._trotter_number)
if U_phase != 1.0 + 0.0j:
raise ValueError(
"Encountered phase change, phase not equal to (1.0 + 0.0i)")
qc_res = qforte.QuantumComputer(self._nqb)
qc_res.apply_circuit(self._Uprep)
qc_res.apply_circuit(U)
qc_res.apply_operator(self._qb_ham)
qc_res.apply_circuit(U.adjoint())
coeffs = qc_res.get_coeff_vec()
residuals = []
for m in self._tops:
# 1. Identify the excitation operator
sq_op = self._pool[m][1]
# occ => i,j,k,...
# vir => a,b,c,...
# sq_op is 1.0(a^ b^ i j) - 1.0(j^ i^ b a)
temp_idx = sq_op.terms()[0][1][-1]
# TODO: This code assumes that the first N orbitals are occupied, and the others are virtual.
# Use some other mechanism to identify the occupied orbitals, so we can use use PQE on excited
# determinants.
if temp_idx < int(
sum(self._ref) / 2): # if temp_idx is an occupied idx
sq_ex_op = sq_op.terms()[0][1]
else:
sq_ex_op = sq_op.terms()[1][1]
# 2. Get the bit representation of the sq_ex_op acting on the reference.
# We determine the projective condition for this amplitude by zero'ing this residual.
nbody = int(len(sq_ex_op) / 2)
# `destroyed` exists solely for error catching.
destroyed = False
excited_det = qforte.QuantumBasis(self._nqb)
for k, occ in enumerate(self._ref):
excited_det.set_bit(k, occ)
# loop over annihilators
for p in reversed(range(nbody, 2 * nbody)):
if (excited_det.get_bit(sq_ex_op[p]) == 0):
destroyed = True
break
excited_det.set_bit(sq_ex_op[p], 0)
# then over creators
for p in reversed(range(0, nbody)):
if (excited_det.get_bit(sq_ex_op[p]) == 1):
destroyed = True
break
excited_det.set_bit(sq_ex_op[p], 1)
if destroyed:
raise ValueError(
"no ops should destroy reference, something went wrong!!")
I = excited_det.add()
# 3. Compute the phase of the operator, relative to its determinant.
qc_temp = qforte.QuantumComputer(self._nqb)
qc_temp.apply_circuit(self._Uprep)
qc_temp.apply_operator(sq_op.jw_transform())
phase_factor = qc_temp.get_coeff_vec()[I]
# 4. Get the residual element, after accounting for numerical noise.
res_m = coeffs[I] * phase_factor
if (np.imag(res_m) != 0.0):
raise ValueError(
"residual has imaginary component, something went wrong!!")
if (self._noise_factor > 1e-12):
res_m = np.random.normal(np.real(res_m), self._noise_factor)
residuals.append(res_m)
return residuals
def update_ansatz(self):
self._n_pauli_measures_k = 0
x0 = copy.deepcopy(self._tamps)
init_gues_energy = self.energy_feval(x0)
# do U^dag e^iH U |Phi_o> = |Phi_res>
temp_pool = qf.SQOpPool()
for param, top in zip(self._tamps, self._tops):
temp_pool.add_term(param, self._pool[top][1])
A = temp_pool.get_quantum_operator('commuting_grp_lex')
U, U_phase = trotterize(A, trotter_number=self._trotter_number)
if U_phase != 1.0 + 0.0j:
raise ValueError(
"Encountered phase change, phase not equal to (1.0 + 0.0i)")
qc_res = qf.QuantumComputer(self._nqb)
qc_res.apply_circuit(self._Uprep)
qc_res.apply_circuit(U)
qc_res.apply_circuit(self._eiH)
qc_res.apply_circuit(U.adjoint())
res_coeffs = qc_res.get_coeff_vec()
lgrst_op_factor = 0.0
# ned to sort the coeffs to psi_tilde
temp_order_resids = []
# build different res_sq list using M_omega
if (self._M_omega != 'inf'):
res_sq_tmp = [
np.real(np.conj(res_coeffs[I]) * res_coeffs[I])
for I in range(len(res_coeffs))
]
# Nmu_lst => [ det1, det2, det3, ... det_M_omega]
det_lst = np.random.choice(len(res_coeffs),
self._M_omega,
p=res_sq_tmp)
print(f'|Co|dt^2 : {np.amax(res_sq_tmp):12.14f}')
print(
f'mu_o : {np.where(res_sq_tmp == np.amax(res_sq_tmp))[0][0]}'
)
No_idx = np.where(res_sq_tmp == np.amax(res_sq_tmp))[0][0]
print(f'\nNo_idx {No_idx:4}')
No = np.count_nonzero(det_lst == No_idx)
print(f'\nNo {No:10}')
res_sq = []
Nmu_lst = []
for mu in range(len(res_coeffs)):
Nmu = np.count_nonzero(det_lst == mu)
if (Nmu > 0):
print(
f'mu: {mu:8} Nmu {Nmu:10} r_mu: { Nmu / (self._M_omega):12.14f} '
)
Nmu_lst.append((Nmu, mu))
res_sq.append((Nmu / (self._M_omega), mu))
## 1. sort
Nmu_lst.sort()
res_sq.sort()
## 2. set norm
self._curr_res_sq_norm = 0.0
for rmu_sq in res_sq[:-1]:
self._curr_res_sq_norm += rmu_sq[0]
self._curr_res_sq_norm /= (self._dt * self._dt)
## 3. print stuff
print(' \n--> Begin selection opt with residual magnitudes:')
print(' Initial guess energy: ',
round(init_gues_energy, 10))
print(
f' Norm of approximate res vec: {np.sqrt(self._curr_res_sq_norm):14.12f}'
)
## 4. check conv status (need up update function with if(M_omega != 'inf'))
if (len(Nmu_lst) == 1):
print(' SPQE converged with M_omega thresh!')
self._converged = 1
self._final_energy = self._energies[-1]
self._final_result = self._results[-1]
else:
self._converged = 0
## 5. add new toperator
if not self._converged:
if self._verbose:
print('\n')
print(' op index (Imu) Number of times measured')
print(' -----------------------------------------------')
res_sq_sum = 0.0
n_ops_added = 0
for Nmu_tup in Nmu_lst[:-1]:
if (self._verbose):
print(
f" {Nmu_tup[1]:10} {np.real(Nmu_tup[0]):14}"
)
n_ops_added += 1
if (Nmu_tup[1] not in self._tops):
self._tops.insert(0, Nmu_tup[1])
self._tamps.insert(0, 0.0)
self.add_op_from_basis_idx(Nmu_tup[1])
self._n_classical_params_lst.append(len(self._tops))
else: # when M_omega == 'inf', proceed with standard SPQE
res_sq = [(np.real(np.conj(res_coeffs[I]) * res_coeffs[I]), I)
for I in range(len(res_coeffs))]
res_sq.sort()
self._curr_res_sq_norm = 0.0
for rmu_sq in res_sq[:-1]:
self._curr_res_sq_norm += rmu_sq[0]
self._curr_res_sq_norm /= (self._dt * self._dt)
print(
' \n--> Begin selection opt with residual magnitudes |r_mu|:')
print(' Initial guess energy: ', round(init_gues_energy, 10))
print(
f' Norm of res vec: {np.sqrt(self._curr_res_sq_norm):14.12f}'
)
self.conv_status()
if not self._converged:
if self._verbose:
print('\n')
print(' op index (Imu) Residual Facotr')
print(' -----------------------------------------------')
res_sq_sum = 0.0
n_ops_added = 0
# for the canonical SPQE batch addition from |r^2| as a importance criterion
if (self._use_cumulative_thresh):
temp_ops = []
for rmu_sq in res_sq[:-1]:
res_sq_sum += (rmu_sq[0] / (self._dt * self._dt))
if res_sq_sum > (self._spqe_thresh *
self._spqe_thresh):
if (self._verbose):
Ktemp = self.get_op_from_basis_idx(rmu_sq[1])
print(
f" {rmu_sq[1]:10} {np.real(rmu_sq[0])/(self._dt * self._dt):14.12f} {Ktemp.str()}"
)
n_ops_added += 1
if (rmu_sq[1] not in self._tops):
temp_ops.append(rmu_sq[1])
self.add_op_from_basis_idx(rmu_sq[1])
### consistant with op ordering inspired by traditional renormalization approaches ###
for temp_op in temp_ops[::-1]:
self._tops.insert(0, temp_op)
self._tamps.insert(0, 0.0)
else: # add individual ops
res_sq.reverse()
op_added = False
for rmu_sq in res_sq[1:]:
if (op_added):
break
print(
f" {rmu_sq[1]:10} {np.real(rmu_sq[0])/(self._dt * self._dt):14.12f}"
)
if (rmu_sq[1] not in self._tops):
print('op added!')
self._tops.insert(0, rmu_sq[1])
self._tamps.insert(0, 0.0)
self.add_op_from_basis_idx(rmu_sq[1])
op_added = True
self._n_classical_params_lst.append(len(self._tops))
