def ucc(geometry,
basis="sto-3g",
multiplicity=1,
charge=1,
theta_thresh=1e-7,
pool=operator_pools.singlet_GSD(),
spin_adapt=True,
psi4_filename="psi4_%12.12f" % random.random()):
# {{{
molecule = openfermion.hamiltonians.MolecularData(geometry, basis,
multiplicity)
molecule.filename = psi4_filename
molecule = openfermionpsi4.run_psi4(molecule,
run_scf=1,
run_mp2=1,
run_cisd=0,
run_ccsd=0,
run_fci=1,
delete_input=1)
pool.init(molecule)
print(" Basis: ", basis)
print(' HF energy %20.16f au' % (molecule.hf_energy))
print(' MP2 energy %20.16f au' % (molecule.mp2_energy))
#print(' CISD energy %20.16f au' %(molecule.cisd_energy))
#print(' CCSD energy %20.16f au' %(molecule.ccsd_energy))
print(' FCI energy %20.16f au' % (molecule.fci_energy))
#Build p-h reference and map it to JW transform
reference_ket = scipy.sparse.csc_matrix(
openfermion.jw_configuration_state(
list(range(0, molecule.n_electrons)),
molecule.n_qubits)).transpose()
reference_bra = reference_ket.transpose().conj()
#JW transform Hamiltonian computed classically with OFPsi4
hamiltonian_op = molecule.get_molecular_hamiltonian()
hamiltonian = openfermion.transforms.get_sparse_operator(hamiltonian_op)
#Thetas
parameters = [0] * pool.n_ops
pool.generate_SparseMatrix()
ucc = UCC(hamiltonian, pool.spmat_ops, reference_ket, parameters)
opt_result = scipy.optimize.minimize(ucc.energy,
parameters,
options={
'gtol': 1e-6,
'disp': True
},
method='BFGS',
callback=ucc.callback)
print(" Finished: %20.12f" % ucc.curr_energy)
parameters = opt_result['x']
for p in parameters:
print(p)
def test():
r = 1.5
geometry = [('H', (0,0,1*r)), ('H', (0,0,2*r)), ('H', (0,0,3*r)), ('H', (0,0,4*r))]
charge = 0
spin = 0
basis = 'sto-3g'
[n_orb, n_a, n_b, h, g, mol, E_nuc, E_scf, C, S] = pyscf_helper.init(geometry,charge,spin,basis)
print(" n_orb: %4i" %n_orb)
print(" n_a : %4i" %n_a)
print(" n_b : %4i" %n_b)
sq_ham = pyscf_helper.SQ_Hamiltonian()
sq_ham.init(h, g, C, S)
print(" HF Energy: %12.8f" %(E_nuc + sq_ham.energy_of_determinant(range(n_a),range(n_b))))
ehf = E_nuc + sq_ham.energy_of_determinant(range(n_a),range(n_b))
assert(abs(ehf - -1.82913741) < 1e-7)
fermi_ham = sq_ham.export_FermionOperator()
hamiltonian = openfermion.transforms.get_sparse_operator(fermi_ham)
s2 = vqe_methods.Make_S2(n_orb)
#build reference configuration
occupied_list = []
for i in range(n_a):
occupied_list.append(i*2)
for i in range(n_b):
occupied_list.append(i*2+1)
print(" Build reference state with %4i alpha and %4i beta electrons" %(n_a,n_b), occupied_list)
reference_ket = scipy.sparse.csc_matrix(openfermion.jw_configuration_state(occupied_list, 2*n_orb)).transpose()
[e,v] = scipy.sparse.linalg.eigsh(hamiltonian.real,1,which='SA',v0=reference_ket.todense())
for ei in range(len(e)):
S2 = v[:,ei].conj().T.dot(s2.dot(v[:,ei]))
print(" State %4i: %12.8f au <S2>: %12.8f" %(ei,e[ei]+E_nuc,S2))
fermi_ham += FermionOperator((),E_nuc)
pyscf.molden.from_mo(mol, "full.molden", sq_ham.C)
# Francesco, change this to singlet_GSD() if you want generalized singles and doubles
pool = operator_pools.singlet_SD()
pool.init(n_orb, n_occ_a=n_a, n_occ_b=n_b, n_vir_a=n_orb-n_a, n_vir_b=n_orb-n_b)
[e,v,params] = vqe_methods.adapt_vqe(fermi_ham, pool, reference_ket, theta_thresh=1e-9)
print(" Final ADAPT-VQE energy: %12.8f" %e)
print(" <S^2> of final state : %12.8f" %(v.conj().T.dot(s2.dot(v))[0,0].real))
assert(abs(-1.99471290 - e) < 1e-7)
def old_setup(
geometry,
basis="sto-3g",
multiplicity=1, #same as george, non-degenerate orbitals
charge=1, #doesn't seem to matter in hamiltonian (weird)
adapt_conver='norm', #doesn't matter for setup, but looks like daniel used a different method to determine convergence, but only in energy-case
adapt_thresh=1e-3, #gradient threshold
theta_thresh=1e-7,
adapt_maxiter=200, #maximum number of iterations
pool=operator_pools.singlet_GS(
), #generalized singles and doubles as default, though he's not sure if he changed it- nick seems to think that he used GS
reference='rhf', #is default in MolecularData anyway
brueckner=0, #mattered for psi4 but is 0 as default anyway
ref_state=None,
spin_adapt=True,
fci_nat_orb=0,
cisd_nat_orb=0,
hf_stability='none',
single_vqe=False, #will be true in our case
chk_ops=[],
energy_thresh=1e-6, #threshold for single_vqe
fci_overlap=False,
psi4_filename="psi4_%12.12f" % random.random()):
# designed to set up the problem in the same way as Daniel, did have to substitute openfermionpyscf for psi4 but shouldn't make a difference
#still need to investiage versioning for the pool init and singlet GSD methods as these require the current version of MolecularData objects to have:
# n_orbitals (y)
# get_n_alpha_electrons (y)
# get_n_beta_electrons (y)
# FermionOperator class (y)
# requried attributes? (~)
#
#
# hermitian_conjugate function (~)
# normal_ordered function (~)
# {{{
start_time = time.time()
molecule = openfermion.hamiltonians.MolecularData(geometry, basis,
multiplicity)
if cisd_nat_orb == 1:
cisd = 1
else:
cisd = 0
molecule = openfermionpyscf.run_pyscf(molecule, run_fci=True)
pool.init(molecule)
if fci_overlap == False:
print(" Basis: ", basis)
print(' HF energy %20.16f au' % (molecule.hf_energy))
#print(' MP2 energy %20.16f au' %(molecule.mp2_energy))
#print(' CISD energy %20.16f au' %(molecule.cisd_energy))
#print(' CCSD energy %20.16f au' %(molecule.ccsd_energy))
if brueckner == 1:
print(' BCCD energy %20.16f au' % (molecule.bccd_energy))
if cisd == 1:
print(' CISD energy %20.16f au' % (molecule.cisd_energy))
if reference == 'rhf':
print(' FCI energy %20.16f au' % (molecule.fci_energy))
# if we are going to transform to FCI NOs, it doesn't make sense to transform to CISD NOs
if cisd_nat_orb == 1 and fci_nat_orb == 0:
print(' Basis transformed to the CISD natural orbitals')
if fci_nat_orb == 1:
print(' Basis transformed to the FCI natural orbitals')
#Build p-h reference and map it to JW transform
if ref_state == None:
ref_state = list(range(0, molecule.n_electrons))
reference_ket = scipy.sparse.csc_matrix(
openfermion.jw_configuration_state(ref_state,
molecule.n_qubits)).transpose()
reference_bra = reference_ket.transpose().conj()
#JW transform Hamiltonian computed classically with OFPsi4
hamiltonian_op = molecule.get_molecular_hamiltonian()
hamiltonian = openfermion.transforms.get_sparse_operator(hamiltonian_op)
if fci_overlap:
e, fci_vec = openfermion.get_ground_state(hamiltonian)
fci_state = scipy.sparse.csc_matrix(fci_vec).transpose()
index = scipy.sparse.find(reference_ket)[0]
print(" Basis: ", basis)
print(' HF energy %20.16f au' % (molecule.hf_energy))
if brueckner == 1:
print(' BCCD energy %20.16f au' % (molecule.bccd_energy))
print(' FCI energy %20.16f au' % e)
print(' <FCI|HF> %20.16f' % np.absolute(fci_vec[index]))
print(' Orbitals')
print(molecule.canonical_orbitals)
#Thetas
parameters = []
#pool.generate_SparseMatrix()
pool.gradient_print_thresh = theta_thresh
ansatz_ops = [] #SQ operator strings in the ansatz
ansatz_mat = [] #Sparse Matrices for operators in ansatz
op_indices = []
parameters = []
#curr_state = 1.0*reference_ket
curr_energy = molecule.hf_energy
molecule = openfermion.transforms.jordan_wigner(hamiltonian_op)
pool_list = []
for op in pool.fermi_ops:
pool_list.append(1j * openfermion.transforms.jordan_wigner(op))
pool_list = PauliPool().from_openfermion_qubit_operator_list(pool_list)
qiskit_molecule = openfermion_to_qiskit(molecule)
return pool_list, qiskit_molecule
n_spinorbitals = int(molecule.n_orbitals*2)
print('HF energy %20.16f au' %(molecule.hf_energy))
print('MP2 energy %20.16f au' %(molecule.mp2_energy))
print('CISD energy %20.16f au' %(molecule.cisd_energy))
print('CCSD energy %20.16f au' %(molecule.ccsd_energy))
print('FCI energy %20.16f au' %(molecule.fci_energy))
global global_der
global global_energy
global global_iter
global_der = np.array([])
global_energy = 0.0
global_iter = 0
#Build p-h reference and map it to JW transform
reference_ket = scipy.sparse.csc_matrix(openfermion.jw_configuration_state(list(range(0,molecule.n_electrons)), molecule.n_qubits)).transpose()
reference_bra = reference_ket.transpose().conj()
#JW transform Hamiltonian computed classically with OFPsi4
tensor_hamiltonian = molecule.get_molecular_hamiltonian()
singles_hamiltonian = tensor_hamiltonian.one_body_tensor
doubles_hamiltonian = tensor_hamiltonian.two_body_tensor
hamiltonian = openfermion.transforms.get_sparse_operator(tensor_hamiltonian)
#Thetas
parameters = []
#Second_quantized operations (not Jordan-Wignered)
SQ_CC_ops = []
def ops_pqrs():
def test_lexical(geometry,
basis="sto-3g",
multiplicity=1,
charge=1,
adapt_conver='norm',
adapt_thresh=1e-3,
theta_thresh=1e-7,
adapt_maxiter=200,
pool=operator_pools.singlet_GSD(),
spin_adapt=True,
psi4_filename="psi4_%12.12f" % random.random()):
# {{{
molecule = openfermion.hamiltonians.MolecularData(geometry, basis,
multiplicity)
molecule.filename = psi4_filename
molecule = openfermionpsi4.run_psi4(molecule,
run_scf=1,
run_mp2=1,
run_cisd=0,
run_ccsd=0,
run_fci=1,
delete_input=1)
pool.init(molecule)
print(" Basis: ", basis)
print(' HF energy %20.16f au' % (molecule.hf_energy))
print(' MP2 energy %20.16f au' % (molecule.mp2_energy))
#print(' CISD energy %20.16f au' %(molecule.cisd_energy))
#print(' CCSD energy %20.16f au' %(molecule.ccsd_energy))
print(' FCI energy %20.16f au' % (molecule.fci_energy))
#Build p-h reference and map it to JW transform
reference_ket = scipy.sparse.csc_matrix(
openfermion.jw_configuration_state(
list(range(0, molecule.n_electrons)),
molecule.n_qubits)).transpose()
reference_bra = reference_ket.transpose().conj()
#JW transform Hamiltonian computed classically with OFPsi4
hamiltonian_op = molecule.get_molecular_hamiltonian()
hamiltonian = openfermion.transforms.get_sparse_operator(hamiltonian_op)
#Thetas
parameters = []
pool.generate_SparseMatrix()
ansatz_ops = [] #SQ operator strings in the ansatz
ansatz_mat = [] #Sparse Matrices for operators in ansatz
print(" Start ADAPT-VQE algorithm")
op_indices = []
parameters = []
curr_state = 1.0 * reference_ket
print(" Now start to grow the ansatz")
for n_iter in range(0, adapt_maxiter):
print("\n\n\n")
print(
" --------------------------------------------------------------------------"
)
print(" ADAPT-VQE iteration: ", n_iter)
print(
" --------------------------------------------------------------------------"
)
next_index = None
next_deriv = 0
curr_norm = 0
print(" Check each new operator for coupling")
next_term = []
print(" Measure commutators:")
sig = hamiltonian.dot(curr_state)
for op_trial in range(pool.n_ops):
opA = pool.spmat_ops[op_trial]
com = 2 * (curr_state.transpose().conj().dot(opA.dot(sig))).real
assert (com.shape == (1, 1))
com = com[0, 0]
assert (np.isclose(com.imag, 0))
com = com.real
opstring = ""
for t in pool.fermi_ops[op_trial].terms:
opstring += str(t)
break
if abs(com) > adapt_thresh:
print(" %4i %40s %12.8f" % (op_trial, opstring, com))
curr_norm += com * com
if abs(com) > abs(next_deriv):
next_deriv = com
next_index = op_trial
next_index = n_iter % pool.n_ops
curr_norm = np.sqrt(curr_norm)
min_options = {'gtol': theta_thresh, 'disp': False}
max_of_com = next_deriv
print(" Norm of <[A,H]> = %12.8f" % curr_norm)
print(" Max of <[A,H]> = %12.8f" % max_of_com)
converged = False
if adapt_conver == "norm":
if curr_norm < adapt_thresh:
converged = True
else:
print(" FAIL: Convergence criterion not defined")
exit()
if converged:
print(" Ansatz Growth Converged!")
print(" Number of operators in ansatz: ", len(ansatz_ops))
print(" *Finished: %20.12f" % trial_model.curr_energy)
print(" -----------Final ansatz----------- ")
print(" %4s %40s %12s" % ("#", "Term", "Coeff"))
for si in range(len(ansatz_ops)):
s = ansatz_ops[si]
opstring = ""
for t in s.terms:
opstring += str(t)
break
print(" %4i %40s %12.8f" % (si, opstring, parameters[si]))
break
print(" Add operator %4i" % next_index)
parameters.insert(0, 0)
ansatz_ops.insert(0, pool.fermi_ops[next_index])
ansatz_mat.insert(0, pool.spmat_ops[next_index])
trial_model = tUCCSD(hamiltonian, ansatz_mat, reference_ket,
parameters)
opt_result = scipy.optimize.minimize(trial_model.energy,
parameters,
jac=trial_model.gradient,
options=min_options,
method='BFGS',
callback=trial_model.callback)
parameters = list(opt_result['x'])
curr_state = trial_model.prepare_state(parameters)
print(" Finished: %20.12f" % trial_model.curr_energy)
print(" -----------New ansatz----------- ")
print(" %4s %40s %12s" % ("#", "Term", "Coeff"))
for si in range(len(ansatz_ops)):
s = ansatz_ops[si]
opstring = ""
for t in s.terms:
opstring += str(t)
break
print(" %4i %40s %12.8f" % (si, opstring, parameters[si]))
return
run_mp2=0,
run_cisd=0,
run_ccsd=0,
run_fci=1,
delete_input=0)
#molecule = openfermionpsi4.run_psi4(molecule, run_scf = 1, run_mp2=1, run_cisd=1, run_ccsd = 1, run_fci=1, delete_input=0)
n_spinorbitals = int(molecule.n_orbitals * 2)
print('HF energy %20.16f au' % (molecule.hf_energy))
#print('MP2 energy %20.16f au' %(molecule.mp2_energy))
#print('CISD energy %20.16f au' %(molecule.cisd_energy))
#print('CCSD energy %20.16f au' %(molecule.ccsd_energy))
print('FCI energy %20.16f au' % (molecule.fci_energy))
#Build p-h reference and map it to JW transform
reference_ket = scipy.sparse.csc_matrix(
openfermion.jw_configuration_state(list(range(0, molecule.n_electrons)),
molecule.n_qubits)).transpose()
reference_bra = reference_ket.transpose().conj()
#JW transform Hamiltonian computed classically with OFPsi4
hamiltonian_op = molecule.get_molecular_hamiltonian()
hamiltonian = openfermion.transforms.get_sparse_operator(hamiltonian_op)
print(reference_bra.dot(hamiltonian.dot(reference_ket)))
#print(" Reference energy: %12.8f" %reference_bra.dot(hamiltonian).dot(reference_ket)[0,0].real)
#Thetas
parameters = []
#Second_quantized operations (not Jordan-Wignered)
SQ_CC_ops = []
alpha_orbs = list(range(int(n_spinorbitals / 2)))
def __init__(self, molecule, **kwargs):
self.ecp = kwargs.get('ecp', 'False')
#Associate a Hamiltonian with this system
self.molecule = molecule
print(self.ecp)
if self.ecp == 'False':
self.hamiltonian = molecule.get_molecular_hamiltonian()
else:
core = list(range(0, int(self.ecp)))
valence = list(range(int(self.ecp), self.n_orbitals))
self.hamiltonian = molecule.get_molecular_hamiltonian(
occupied_indices=core, active_indices=valence)
wfile = open('CASCI.out', 'a')
print('Hi')
eigs = ((np.linalg.eigh(self.hamiltonian))[0])
print(eigs)
wfile.write(str(sorted(eigs)[0]) + '\n')
exit()
self.two_index_hamiltonian = self.hamiltonian.one_body_tensor
self.four_index_hamiltonian = self.hamiltonian.two_body_tensor
self.JW_hamiltonian = openfermion.transforms.get_sparse_operator(
self.hamiltonian)
#Construct spaces
self.aoccs = [
i for i in range(0, self.molecule.n_electrons) if i % 2 == 0
]
self.anoccs = [
i for i in range(self.molecule.n_electrons,
self.molecule.n_orbitals * 2) if i % 2 == 0
]
self.boccs = [
i for i in range(0, self.molecule.n_electrons) if i % 2 == 1
]
self.bnoccs = [
i for i in range(self.molecule.n_electrons,
self.molecule.n_orbitals * 2) if i % 2 == 1
]
self.alphas = self.aoccs + self.anoccs
self.betas = self.boccs + self.bnoccs
#Construct reference ket
self.HF_ket = scipy.sparse.csc_matrix(
openfermion.jw_configuration_state(
list(range(0, molecule.n_electrons)),
molecule.n_qubits)).transpose()
#Parse kwargs
self.include_pqrs = kwargs.get('include_pqrs', 'False')
self.screen_commutators = kwargs.get('screen_commutators', 'False')
self.sort = kwargs.get('sort', None)
self.spin_adapt = kwargs.get('spin_adapt', 'False')
#Initialize op list
self.SQ_Singles = []
self.SQ_Doubles = []
self.Singles = []
self.Doubles = []
self.Full_JW_Ops = []
#Get unfiltered list
if self.include_pqrs == 'True':
self.PQRS()
else:
self.IJAB()
self.Full_Ops = self.Singles + self.Doubles
self.Full_SQ_Ops = self.SQ_Singles + self.SQ_Doubles
#Spin adapt
if self.spin_adapt == 'True':
print('Spin adapting operators...')
self.Spin_Adapt()
print(self.Full_Ops)
for op in self.Full_SQ_Ops:
op = openfermion.normal_ordered(op)
if op.many_body_order() > 0:
self.Full_JW_Ops.append(
(1 / np.sqrt(2)) *
openfermion.transforms.get_sparse_operator(
op, n_qubits=self.molecule.n_qubits))
#Apply filters
if self.screen_commutators == 'True':
print('Screening by commutators with Hamiltonian (HF ansatz)...')
self.Screen_Commutators()
#Apply sorting method
if self.sort == None:
print('Operators not sorted!')
pass
elif self.sort == 'commutators':
print('Sorting operators by increasing commutators...')
self.Sort_Commutators()
else:
print('Sorting operators by seed ' + str(self.sort) + '...')
self.Sort_Random(self.sort)
hamiltonian = openfermion.transforms.get_sparse_operator(fermi_ham)
s2 = Make_S2(n_orb)
#build reference configuration
occupied_list = []
for i in range(n_a):
occupied_list.append(i * 2)
for i in range(n_b):
occupied_list.append(i * 2 + 1)
print(
" Build reference state with %4i alpha and %4i beta electrons" %
(n_a, n_b), occupied_list)
reference_ket = scipy.sparse.csc_matrix(
openfermion.jw_configuration_state(occupied_list,
2 * n_orb)).transpose()
[e, v] = scipy.sparse.linalg.eigsh(hamiltonian.real,
1,
which='SA',
v0=reference_ket.todense())
for ei in range(len(e)):
S2 = v[:, ei].conj().T.dot(s2.dot(v[:, ei]))
print(" State %4i: %12.8f au <S2>: %12.8f" % (ei, e[ei] + E_nuc, S2))
fermi_ham += FermionOperator((), E_nuc)
pyscf.molden.from_mo(mol, "full.molden", sq_ham.C)
pool = operator_pools.singlet_SD()
pool.init(n_orb,
n_occ_a=n_a,
n_occ_b=n_b,