def build_sparse_S_b(self, b):
b_sparse = []
idx_sparse = []
for I, bI in enumerate(b):
if (np.abs(bI) > self._b_thresh):
idx_sparse.append(I)
b_sparse.append(bI)
Idim = len(idx_sparse)
self._n_pauli_trm_measures += int(Idim * (Idim + 1) * 0.5)
S = np.zeros((len(b_sparse), len(b_sparse)), dtype=complex)
Ipsi_qc = qf.QuantumComputer(self._nqb)
Ipsi_qc.set_coeff_vec(copy.deepcopy(self._qc.get_coeff_vec()))
CI = np.zeros(shape=(Idim, int(2**self._nqb)), dtype=complex)
for i in range(Idim):
S[i][
i] = 1.0 # With Pauli strings, this is always the inner product
Ii = idx_sparse[i]
Ipsi_qc.apply_operator(self._sig.terms()[Ii][1])
CI[i, :] = copy.deepcopy(Ipsi_qc.get_coeff_vec())
for j in range(i):
S[i][j] = S[j][i] = np.vdot(CI[i, :], CI[j, :])
Ipsi_qc.set_coeff_vec(copy.deepcopy(self._qc.get_coeff_vec()))
return idx_sparse, np.real(S), np.real(b_sparse)
def build_b(self):
"""
Construct the vector b (eq. 5b) of Motta, with h[m] the full Hamiltonian.
"""
b = np.zeros(self._NI, dtype=complex)
denom = np.sqrt(1 - 2 * self._db * self._Ekb[-1])
prefactor = -1.0j / denom
self._n_pauli_trm_measures += self._Nl * self._NI
Hpsi_qc = qf.QuantumComputer(self._nqb)
Hpsi_qc.set_coeff_vec(copy.deepcopy(self._qc.get_coeff_vec()))
Hpsi_qc.apply_operator(self._qb_ham)
C_Hpsi_qc = copy.deepcopy(Hpsi_qc.get_coeff_vec())
for I, (op_coefficient, operator) in enumerate(self._sig.terms()):
Hpsi_qc.apply_operator(operator)
exp_val = np.vdot(self._qc.get_coeff_vec(),
Hpsi_qc.get_coeff_vec())
b[I] = prefactor * op_coefficient * exp_val
Hpsi_qc.set_coeff_vec(C_Hpsi_qc)
return np.real(b)
def build_classical_CI_mats(self):
"""Builds a classical configuration interaction out of single determinants.
"""
num_tot_basis = len(self._pre_sa_ref_lst)
h_CI = np.zeros((num_tot_basis,num_tot_basis), dtype=complex)
omega_lst = []
Homega_lst = []
for i, ref in enumerate(self._pre_sa_ref_lst):
# NOTE: do NOT use Uprep here (is determinant specific).
Un = qforte.QuantumCircuit()
for j in range(self._nqb):
if ref[j] == 1:
Un.add_gate(qforte.gate('X', j, j))
QC = qforte.QuantumComputer(self._nqb)
QC.apply_circuit(Un)
omega_lst.append(np.asarray(QC.get_coeff_vec(), dtype=complex))
Homega = np.zeros((2**self._nqb), dtype=complex)
QC.apply_operator(self._qb_ham)
Homega_lst.append(np.asarray(QC.get_coeff_vec(), dtype=complex))
for j in range(len(omega_lst)):
h_CI[i][j] = np.vdot(omega_lst[i], Homega_lst[j])
h_CI[j][i] = np.conj(h_CI[i][j])
return h_CI
def perfect_experimental_avg(self, params):
"""
calculates the exact experimental result of the operator the Experiment object was initialized with
:param params: (list) the list of parameters for the state preparation ansatz.
"""
if(self.prepare_each_time_==False):
#1 initialize a quantum computer
qc = qforte.QuantumComputer(self.n_qubits_)
#2 build/update generator with params
# self.generator_.set_parameters(params)
#3 apply generator (once if prepare_each_time = False, N_sample times if)
qc.apply_circuit(self.generator_)
#4 measure operator
n_terms = len(self.operator_.terms())
term_sum = 0.0
for k in range(n_terms):
term_sum += self.operator_.terms()[k][0] * qc.perfect_measure_circuit(self.operator_.terms()[k][1]);
return numpy.real(term_sum)
elif(self.prepare_each_time_==True):
raise Exception('No support yet for measurement with multiple state preparations')
def test_build_from_openfermion(self):
print('\n')
trial_state = qforte.QuantumComputer(4)
trial_prep = [None] * 5
trial_prep[0] = qforte.gate('H', 0, 0)
trial_prep[1] = qforte.gate('H', 1, 1)
trial_prep[2] = qforte.gate('H', 2, 2)
trial_prep[3] = qforte.gate('H', 3, 3)
trial_prep[4] = qforte.gate('cX', 0, 1)
trial_circ = qforte.QuantumCircuit()
#prepare the circuit
for gate in trial_prep:
trial_circ.add_gate(gate)
# use circuit to prepare trial state
trial_state.apply_circuit(trial_circ)
test_operator = QubitOperator('X2 Y1', 0.0 - 0.25j)
test_operator += QubitOperator('Y2 Y1', 0.25)
test_operator += QubitOperator('X2 X1', 0.25)
test_operator += QubitOperator('Y2 X1', 0.0 + 0.25j)
print(test_operator)
qforte_operator = qforte.build_from_openfermion(test_operator)
qforte.smart_print(qforte_operator)
exp = trial_state.direct_op_exp_val(qforte_operator)
print(exp)
self.assertAlmostEqual(exp, 0.2499999999999999 + 0.0j)
def build_orb_energies(self):
self._orb_e = []
print('\nBuilding single particle energies list:')
print('---------------------------------------')
qc = qforte.QuantumComputer(self._nqb)
qc.apply_circuit(build_Uprep(self._ref, 'reference'))
E0 = qc.direct_op_exp_val(self._qb_ham)
for i in range(self._nqb):
qc = qforte.QuantumComputer(self._nqb)
qc.apply_circuit(build_Uprep(self._ref, 'reference'))
qc.apply_gate(qforte.gate('X', i, i))
Ei = qc.direct_op_exp_val(self._qb_ham)
if (i < sum(self._ref)):
ei = E0 - Ei
else:
ei = Ei - E0
print(f' {i:3} {ei:+16.12f}')
self._orb_e.append(ei)
def run(self,
beta=1.0,
db=0.2,
expansion_type='SD',
sparseSb=True,
b_thresh=1.0e-6,
x_thresh=1.0e-10,
do_lanczos=False,
lanczos_gap=2):
self._beta = beta
self._db = db
self._nbeta = int(beta / db) + 1
self._sq_ham = self._sys.get_sq_hamiltonian()
self._expansion_type = expansion_type
self._sparseSb = sparseSb
self._total_phase = 1.0 + 0.0j
self._Uqite = qf.QuantumCircuit()
self._b_thresh = b_thresh
self._x_thresh = x_thresh
self._n_classical_params = 0
self._n_cnot = 0
self._n_pauli_trm_measures = 0
self._do_lanczos = do_lanczos
self._lanczos_gap = lanczos_gap
qc_ref = qf.QuantumComputer(self._nqb)
qc_ref.apply_circuit(self._Uprep)
self._Ekb = [np.real(qc_ref.direct_op_exp_val(self._qb_ham))]
# Print options banner (should done for all algorithms).
self.print_options_banner()
# Build expansion pool.
self.build_expansion_pool()
# Do the imaginary time evolution.
self.evolve()
if (self._do_lanczos):
self.do_qlanczos()
# Print summary banner (should done for all algorithms).
self.print_summary_banner()
# verify that required attributes were defined
# (should done for all algorithms).
self.verify_run()
def ref_state(self):
""" Return a qforte.QuantumComputer instance which represents the Hartree-Fock state of the input molecule
"""
hf_qc = qforte.QuantumComputer(self.molecule.n_qubits)
hf_cir = qforte.QuantumCircuit()
for i_a in self.occ_idx_alpha:
X_ia = qforte.make_gate('X', i_a, i_a)
hf_cir.add_gate(X_ia)
for i_b in self.occ_idx_beta:
X_ib = qforte.make_gate('X', i_b, i_b)
hf_cir.add_gate(X_ib)
hf_qc.apply_circuit(hf_cir)
return hf_qc
def build_S(self):
"""
Construct the matrix S (eq. 5a) of Motta.
"""
Idim = self._NI
S = np.zeros((Idim, Idim), dtype=complex)
Ipsi_qc = qf.QuantumComputer(self._nqb)
Ipsi_qc.set_coeff_vec(copy.deepcopy(self._qc.get_coeff_vec()))
# CI[I][J] = (σ_I Ψ)_J
CI = np.zeros(shape=(Idim, int(2**self._nqb)), dtype=complex)
for i in range(Idim):
S[i][
i] = 1.0 # With Pauli strings, this is always the inner product
Ipsi_qc.apply_operator(self._sig.terms()[i][1])
CI[i, :] = copy.deepcopy(Ipsi_qc.get_coeff_vec())
for j in range(i):
S[i][j] = S[j][i] = np.vdot(CI[i, :], CI[j, :])
Ipsi_qc.set_coeff_vec(copy.deepcopy(self._qc.get_coeff_vec()))
return np.real(S)
def evolve(self):
self._Uqite.add_circuit(self._Uprep)
self._qc = qf.QuantumComputer(self._nqb)
self._qc.apply_circuit(self._Uqite)
if (self._do_lanczos):
self._lanczos_vecs = []
self._Hlanczos_vecs = []
self._lanczos_vecs.append(copy.deepcopy(self._qc.get_coeff_vec()))
qcSig_temp = qf.QuantumComputer(self._nqb)
qcSig_temp.set_coeff_vec(copy.deepcopy(self._qc.get_coeff_vec()))
qcSig_temp.apply_operator(self._qb_ham)
self._Hlanczos_vecs.append(
copy.deepcopy(qcSig_temp.get_coeff_vec()))
print(
f"{'beta':>7}{'E(beta)':>18}{'N(params)':>14}{'N(CNOT)':>18}{'N(measure)':>20}"
)
print(
'-------------------------------------------------------------------------------'
)
print(
f' {0.0:7.3f} {self._Ekb[0]:+15.9f} {self._n_classical_params:8} {self._n_cnot:10} {self._n_pauli_trm_measures:12}'
)
if (self._print_summary_file):
f = open("summary.dat", "w+", buffering=1)
f.write(
f"#{'beta':>7}{'E(beta)':>18}{'N(params)':>14}{'N(CNOT)':>18}{'N(measure)':>20}\n"
)
f.write(
'#-------------------------------------------------------------------------------\n'
)
f.write(
f' {0.0:7.3f} {self._Ekb[0]:+15.9f} {self._n_classical_params:8} {self._n_cnot:10} {self._n_pauli_trm_measures:12}\n'
)
for kb in range(1, self._nbeta):
self.do_qite_step()
if (self._do_lanczos):
if (kb % self._lanczos_gap == 0):
self._lanczos_vecs.append(
copy.deepcopy(self._qc.get_coeff_vec()))
qcSig_temp = qf.QuantumComputer(self._nqb)
qcSig_temp.set_coeff_vec(
copy.deepcopy(self._qc.get_coeff_vec()))
qcSig_temp.apply_operator(self._qb_ham)
self._Hlanczos_vecs.append(
copy.deepcopy(qcSig_temp.get_coeff_vec()))
print(
f' {kb*self._db:7.3f} {self._Ekb[kb]:+15.9f} {self._n_classical_params:8} {self._n_cnot:10} {self._n_pauli_trm_measures:12}'
)
if (self._print_summary_file):
f.write(
f' {kb*self._db:7.3f} {self._Ekb[kb]:+15.9f} {self._n_classical_params:8} {self._n_cnot:10} {self._n_pauli_trm_measures:12}\n'
)
self._Egs = self._Ekb[-1]
if (self._print_summary_file):
f.close()
def run(self,
t = 1.0,
nruns = 20,
success_prob = 0.5,
num_precise_bits = 4,
return_phases=False):
self._t = t
self._nruns = nruns
self._success_prob = success_prob
self._num_precise_bits = num_precise_bits
self._return_phases = return_phases
self._Uqpe = qforte.QuantumCircuit()
self._n_state_qubits = self._nqb
eps = 1 - success_prob
self._n_ancilla = num_precise_bits + int(np.log2(2 + (1.0/eps)))
self._n_tot_qubits = self._n_state_qubits + self._n_ancilla
self._abegin = self._n_state_qubits
self._aend = self._n_tot_qubits - 1
self._n_classical_params = 0
self._n_pauli_trm_measures = nruns
# Print options banner (should done for all algorithms).
self.print_options_banner()
######### QPE ########
# add hadamard circ to split ancilla register
self._Uqpe.add_circuit(self.get_Uhad())
# add initial state preparation circuit
self._Uqpe.add_circuit(self._Uprep)
# add controll e^-iHt circuit
self._Uqpe.add_circuit(self.get_dynamics_circ())
# add reverse QFT
self._Uqpe.add_circuit(self.get_qft_circuit('reverse'))
computer = qforte.QuantumComputer(self._n_tot_qubits)
computer.apply_circuit(self._Uqpe)
self._n_cnot = self._Uqpe.get_num_cnots()
if(self._fast):
z_readouts = computer.measure_z_readouts_fast(self._abegin, self._aend, self._nruns)
else:
Zcirc = self.get_z_circuit()
z_readouts = computer.measure_readouts(Zcirc, self._nruns)
self._phases = []
for readout in z_readouts:
val = 0.0
i = 1
for z in readout:
val += z / (2**i)
i += 1
self._phases.append(val)
# find final binary string of phase readouts:
final_readout = []
final_readout_aves = []
for i in range(self._n_ancilla):
iave = 0.0
for readout in z_readouts:
iave += readout[i]
iave /= nruns
final_readout_aves.append(iave)
if (iave > (1.0/2)):
final_readout.append(1)
else:
final_readout.append(0)
self._final_phase = 0.0
counter = 0
for i, z in enumerate(final_readout):
self._final_phase += z / (2**(i+1))
Eqpe = -2 * np.pi * self._final_phase / t
res = stats.mode(np.asarray(self._phases))
self._mode_phase = res.mode[0]
self._mode_energy = -2 * np.pi * self._mode_phase / t
print('\n ==> QPE readout averages <==')
print('------------------------------------------------')
for i, ave in enumerate(final_readout_aves):
print(' bit ', i, ': ', ave)
print('\n Final bit readout: ', final_readout)
######### QPE ########
# set Egs
self._Egs = Eqpe
# set Umaxdepth
self._Umaxdepth = self._Uqpe
# Print summary banner (should done for all algorithms).
self.print_summary_banner()
# verify that required attributes were defined
# (should done for all algorithms).
self.verify_run()
def build_sa_qk_mats(self):
# TODO (cleanup): imporve/update docs
"""Returns matrices P and Q with dimension
(nstates_per_ref * len(ref_lst) X nstates_per_ref * len(ref_lst))
based on the evolution of two unitary operators Um = exp(-i * m * dt * H)
and Un = exp(-i * n * dt *H).
This is done for all spin adapted refrerences |Phi_K> in ref_lst,
with (Q) and without (P) measuring with respect to the operator H.
Elements P_mn are given by <Phi_I| Um^dag Un | Phi_J>.
Elements Q_mn are given by <Phi_I| Um^dag H Un | Phi_J>.
This function builds P and Q in an efficient manor and gives the same result
as M built from 'matrix_element', but is unphysical for a quantum computer.
Arguments
---------
ref_lst : list of lists
A list containing all of the spin adapted references |Phi_K> to perfrom evolutions on.
Is specifically a list of lists of pairs containing coefficient vales
and a lists pertaning to single determinants.
As an example,
ref_lst = [ [ (1.0, [1,1,0,0]) ], [ (0.7071, [0,1,1,0]), (0.7071, [1,0,0,1]) ] ].
nstates_per_ref : int
The number of Krylov basis states to generate for each reference.
dt_lst : list
List of time steps to use for each reference (ususally the same for
all references).
H : QuantumOperator
The operator to time evolove and measure with respect to
(usually the Hamiltonain).
nqubits : int
The number of qubits
trot_number : int
The number of trotter steps (m) to perform when approximating the matrix
exponentials (Um or Un). For the exponential of two non commuting terms
e^(A + B), the approximate operator C(m) = (e^(A/m) * e^(B/m))^m is
exact in the infinite m limit.
Returns
-------
s_mat : ndarray
A numpy array containing the elements P_mn
h_mat : ndarray
A numpy array containing the elements Q_mn
"""
num_tot_basis = len(self._sa_ref_lst) * self._nstates_per_ref
h_mat = np.zeros((num_tot_basis,num_tot_basis), dtype=complex)
s_mat = np.zeros((num_tot_basis,num_tot_basis), dtype=complex)
omega_lst = []
Homega_lst = []
for i, ref in enumerate(self._sa_ref_lst):
for m in range(self._nstates_per_ref):
# TODO (cleanup): will need to consider gate count for this part.
Um = qforte.QuantumCircuit()
phase1 = 1.0
if(m>0):
fact = (0.0-1.0j) * m * self._mr_dt
expn_op1, phase1 = trotterize(self._qb_ham, factor=fact, trotter_number=self._trotter_number)
Um.add_circuit(expn_op1)
QC = qforte.QuantumComputer(self._nqb)
state_prep_lst = []
for term in ref:
coeff = term[0]
det = term[1]
idx = ref_to_basis_idx(det)
state = qforte.QuantumBasis(idx)
state_prep_lst.append( (state, coeff) )
QC.set_state(state_prep_lst)
QC.apply_circuit(Um)
QC.apply_constant(phase1)
omega_lst.append(np.asarray(QC.get_coeff_vec(), dtype=complex))
QC.apply_operator(self._qb_ham)
Homega_lst.append(np.asarray(QC.get_coeff_vec(), dtype=complex))
if(self._diagonalize_each_step):
print('\n\n')
print(f"{'k(S)':>7}{'E(Npar)':>19}{'N(params)':>14}{'N(CNOT)':>18}{'N(measure)':>20}")
print('-------------------------------------------------------------------------------')
# TODO (opt): add this to previous loop
for p in range(num_tot_basis):
for q in range(p, num_tot_basis):
h_mat[p][q] = np.vdot(omega_lst[p], Homega_lst[q])
h_mat[q][p] = np.conj(h_mat[p][q])
s_mat[p][q] = np.vdot(omega_lst[p], omega_lst[q])
s_mat[q][p] = np.conj(s_mat[p][q])
if (self._diagonalize_each_step):
# TODO (cleanup): have this print to a separate file
evals, evecs = canonical_geig_solve(s_mat[0:p+1, 0:p+1],
h_mat[0:p+1, 0:p+1],
print_mats=False,
sort_ret_vals=True)
scond = np.linalg.cond(s_mat[0:p+1, 0:p+1])
cs_str = '{:.2e}'.format(scond)
k = p+1
self._n_classical_params = k
if(k==1):
self._n_cnot = self._srqk._n_cnot
else:
self._n_cnot = 2 * Um.get_num_cnots()
self._n_pauli_trm_measures = k * self._Nl + self._srqk._n_pauli_trm_measures
self._n_pauli_trm_measures += k * (k-1) * self._Nl
self._n_pauli_trm_measures += k * (k-1)
print(f' {scond:7.2e} {np.real(evals[self._target_root]):+15.9f} {self._n_classical_params:8} {self._n_cnot:10} {self._n_pauli_trm_measures:12}')
return s_mat, h_mat
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))
def build_qk_mats(self):
"""Returns matrices S and H needed for the MRSQK algorithm using the Trotterized
form of the unitary operators U_n = exp(-i n dt H)
The mathematical operations of this function are unphysical for a quantum
computer, but efficient for a simulator.
Returns
-------
s_mat : ndarray
A numpy array containing the elements S_mn = <Phi | Um^dag Un | Phi>.
_nstates by _nstates
h_mat : ndarray
A numpy array containing the elements H_mn = <Phi | Um^dag H Un | Phi>
_nstates by _nstates
"""
h_mat = np.zeros((self._nstates, self._nstates), dtype=complex)
s_mat = np.zeros((self._nstates, self._nstates), dtype=complex)
# Store these vectors for the aid of MRSQK
self._omega_lst = []
Homega_lst = []
if (self._diagonalize_each_step):
print('\n\n')
print(
f"{'k(S)':>7}{'E(Npar)':>19}{'N(params)':>14}{'N(CNOT)':>18}{'N(measure)':>20}"
)
print(
'-------------------------------------------------------------------------------'
)
if (self._print_summary_file):
f = open("summary.dat", "w+", buffering=1)
f.write(
f"#{'k(S)':>7}{'E(Npar)':>19}{'N(params)':>14}{'N(CNOT)':>18}{'N(measure)':>20}\n"
)
f.write(
'#-------------------------------------------------------------------------------\n'
)
for m in range(self._nstates):
# Compute U_m = exp(-i m dt H)
Um = qforte.QuantumCircuit()
Um.add_circuit(self._Uprep)
phase1 = 1.0
if (m > 0):
fact = (0.0 - 1.0j) * m * self._dt
expn_op1, phase1 = trotterize(
self._qb_ham,
factor=fact,
trotter_number=self._trotter_number)
Um.add_circuit(expn_op1)
# Compute U_m |φ>
QC = qforte.QuantumComputer(self._nqb)
QC.apply_circuit(Um)
QC.apply_constant(phase1)
self._omega_lst.append(
np.asarray(QC.get_coeff_vec(), dtype=complex))
# Compute H U_m |φ>
QC.apply_operator(self._qb_ham)
Homega_lst.append(np.asarray(QC.get_coeff_vec(), dtype=complex))
# Compute S_mn = <φ| U_m^\dagger U_n |φ> and H_mn = <φ| U_m^\dagger H U_n |φ>
for n in range(len(self._omega_lst)):
h_mat[m][n] = np.vdot(self._omega_lst[m], Homega_lst[n])
h_mat[n][m] = np.conj(h_mat[m][n])
s_mat[m][n] = np.vdot(self._omega_lst[m], self._omega_lst[n])
s_mat[n][m] = np.conj(s_mat[m][n])
if (self._diagonalize_each_step):
# TODO (cleanup): have this print to a separate file
k = m + 1
evals, evecs = canonical_geig_solve(s_mat[0:k, 0:k],
h_mat[0:k, 0:k],
print_mats=False,
sort_ret_vals=True)
scond = np.linalg.cond(s_mat[0:k, 0:k])
self._n_classical_params = k
self._n_cnot = 2 * Um.get_num_cnots()
self._n_pauli_trm_measures = k * self._Nl
self._n_pauli_trm_measures += k * (k - 1) * self._Nl
self._n_pauli_trm_measures += k * (k - 1)
print(
f' {scond:7.2e} {np.real(evals[self._target_root]):+15.9f} {self._n_classical_params:8} {self._n_cnot:10} {self._n_pauli_trm_measures:12}'
)
if (self._print_summary_file):
f.write(
f' {scond:7.2e} {np.real(evals[self._target_root]):+15.9f} {self._n_classical_params:8} {self._n_cnot:10} {self._n_pauli_trm_measures:12}\n'
)
if (self._diagonalize_each_step and self._print_summary_file):
f.close()
self._n_classical_params = self._nstates
self._n_cnot = 2 * Um.get_num_cnots()
# diagonal terms of Hbar
self._n_pauli_trm_measures = self._nstates * self._Nl
# off-diagonal of Hbar (<X> and <Y> of Hadamard test)
self._n_pauli_trm_measures += self._nstates * (self._nstates -
1) * self._Nl
# off-diagonal of S (<X> and <Y> of Hadamard test)
self._n_pauli_trm_measures += self._nstates * (self._nstates - 1)
return s_mat, h_mat