Low Level Data Structure and Algorithms#

Matrix Product (MP)#

class renormalizer.mps.mp.MatrixProduct[source]#

_array2mt(array, idx, allow_dump=True)[source]#

_get_big_qn(cidx: List[int], swap=False)[source]#

get the quantum number of L-block and R-block renormalized basis

Parameters:

cidx (list) – a list of center(active) site index. For 1site/2site algorithm, cidx has one/two elements.

Returns:

qnbigl (np.ndarray) – super-L-block (L-block + active site if necessary) quantum number
qnbigr (np.ndarray) – super-R-block (active site + R-block if necessary) quantum number
qnmat (np.ndarray) – L-block + active site + R-block quantum number

_get_sigmaqn(idx)[source]#

_push_cano(idx)[source]#

_switch_direction()[source]#

_update_mps(cstruct, cidx, qnbigl, qnbigr, percent=0)[source]#

update mps with basis selection algorithm of J. Chem. Phys. 120, 3172 (2004).

Parameters:

cstruct (ndarray, List[ndarray]) – The active site coefficient.
cidx (list) – The List of active site index.
qnbigl (ndarray) – The super-L-block quantum number.
qnbigr (ndarray) – The super-R-block quantum number.
percent (float, int) – The percentage of renormalized basis which is equally selected from each quantum number section rather than according to singular values. percent is defined in procedure of renormalizer.utils.configs.OptimizeConfig and vprocedure of renormalizer.utils.configs.CompressConfig.

Returns:

if cstruct is a list, averaged_ms is a list of rotated ms of: each element in cstruct as a single site calculation. It is used for better initial guess in SA-DMRG algorithm. Otherwise, None is returned. self is overwritten inplace.

Return type:

averaged_ms

_update_ms(idx: int, u: ndarray, vt: ndarray, sigma=None, qnlset=None, qnrset=None, m_trunc=None)[source]#: update mps directly after svd

add(other: MatrixProduct)[source]#

angle(other)[source]#

append(array)[source]#

property bond_dims: List#

property bond_dims_exact: ndarray#

property bond_dims_mean: int#

property bond_list: List#

build_empty_mp(num)[source]#

build_empty_qn()[source]#

build_none_qn()[source]#

canonicalise(stop_idx: Optional[int] = None)[source]#

check_left_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)[source]#: check L-canonical

check_right_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)[source]#: check R-canonical

compress(temp_m_trunc=None, ret_s=False)[source]#

inp: canonicalise MPS (or MPO)

Parameters:

temp_m_trunc (int or list of int) – Temporary truncation bond dimension. Overwrites the compression configuration in CompressConfig.
ret_s (bool) – Whether return the singular values at the bonds. The singular values are padded to a rectangular array with zero values.

Return type:

truncated MPS

conj()[source]#: complex conjugate

copy()[source]#

distance(other) → float[source]#

dot(other: MatrixProduct) → complex[source]#: dot product of two mps / mpo

dot_ob(other: MatrixProduct) → complex[source]#: dot product of two mps / mpo with open boundary, but the boundary of mps/mpo is larger than 1, different from the normal mps/mpo

dump(fname, other_attrs=None)[source]#

ensure_left_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)[source]#

ensure_right_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)[source]#

classmethod from_mp(model, mplist)[source]#

property is_complex#

property is_left_canonical#: check the qn center in the L-canonical structure

property is_mpdm#

property is_mpo#

property is_mps#

property is_right_canonical#: check the qn center in the R-canonical structure

iter_idx_list(full: bool, stop_idx: Optional[int] = None)[source]#

classmethod load(model: Model, fname: str)[source]#

metacopy() → MatrixProduct[source]#

move_qnidx(dstidx: int)[source]#: Quantum number has a boundary site, left hand of the site is L system qn, right hand of the side is R system qn, the sum of quantum number of L system and R system is tot.

property mp_norm: float#

if self.is_left_canon:: assert self.check_left_canonical() return np.linalg.norm(np.ravel(self[-1]))
else:: assert self.check_right_canonical() return np.linalg.norm(np.ravel(self[0]))

property pbond_dims#

property pbond_list#

scale(val, inplace=False)[source]#

property site_num#

property threshold#

to_complex(inplace=False)[source]#

property total_bytes#

variational_compress(mpo=None, guess=None)[source]#

Variational compress an mps/mpdm/mpo

Parameters:

mpo (renormalizer.mps.Mpo, optional) – Default is None. if mpo is not None, the returned mps is an approximation of mpo @ self
guess (renormalizer.mps.MatrixProduct, optional) – Initial guess of compressed mps/mpdm/mpo. Default is None.

Note

the variational compress related configurations is defined in self if guess=None, otherwise is defined in guess

Returns:: mp – a new compressed mps/mpdm/mpo, self is not overwritten. guess is overwritten.
Return type:: renormalizer.mps.MatrixProduct

property vbond_dims: List#

property vbond_list: List#

Matrix Product State (MPS)#

class renormalizer.mps.Mps[source]#

add(other)[source]#

angle(other)#

append(array)#

property bond_dims: List#

property bond_dims_exact: ndarray#

property bond_dims_mean: int#

property bond_list: List#

build_empty_mp(num)#

build_empty_qn()#

build_none_qn()#

calc_1site_rdm(idx=None) → Dict[int, ndarray][source]#

Calculate 1-site reduced density matrix

\(\rho_i = \textrm{Tr}_{j \neq i} | \Psi \rangle \langle \Psi|\)

Parameters:: idx (int, list, tuple, optional) – site index of 1site_rdm. Default is None, which mean all the rdms are calculated.
Returns:: rdm – \(\{0:\rho_0, 1:\rho_1, \cdots\}\). The key is the index of the site.
Return type:: Dict

calc_2site_mutual_entropy() → ndarray[source]#

Calculate mutual entropy between two sites. Also known as mutual information

\(m_{ij} = (s_i + s_j - s_{ij})/2\)

See Chemical Physics 323 (2006) 519–531

Returns:: mutual_entropy – mutual entropy with shape (nsite, nsite)
Return type:: 2d np.ndarry

calc_2site_rdm()[source]#

Calculate 2-site reduced density matrix

\(\rho_{ij} = \textrm{Tr}_{k \neq i, k \neq j} | \Psi \rangle \langle \Psi |\).

Returns:: rdm – \(\{(0,1):\rho_{01}, (0,2):\rho_{02}, \cdots\}\). The key is a tuple of index of the site.
Return type:: Dict

calc_bond_entropy(s_array: Optional[array] = None) → ndarray[source]#

Calculate von Neumann entropy at each bond according to \(S = -\textrm{Tr}(\rho \ln \rho)\) where \(\rho\) is the reduced density matrix of either block.

Parameters:: s_array (np.ndarray, optional) – The singular values array. Calculated by Mps.calc_bond_singular_values().
Returns:: S – a NumPy array containing the entropy values.
Return type:: 1D array

calc_bond_singular_values() → ndarray[source]#

Calculate the singular values at each bond.

Returns:: Singular values
Return type:: 2D array

calc_edof_rdm() → ndarray[source]#

Calculate the reduced density matrix of electronic DoF

\(\rho_{ij} = \langle \Psi | a_i^\dagger a_j | \Psi \rangle\)

Note

This function is designed for single-electron system. Fermionic commutation relation is not considered.

calc_entropy(entropy_type)[source]#

Calculate 1site, 2site, mutual and bond Von Neumann entropy

\(\textrm{entropy} = -\textrm{Tr}(\rho \ln \rho)\) where \(\ln\) stands for natural logarithm.

1site entropy is the entropy between any site and the other (N-1) sites. 2site entropy is the entropy between any two sites and the other (N-2) sites. mutual entropy characterize the entropy between any two sites. bond entropy is the entropy between L-block and R-block.

Parameters:: entropy_type (str) – “1site”, “2site”, “mutual”, “bond”
Returns:: entropy – if entropy_type = “1site” or “2site”, a dictionary is returned and the key is the index or the tuple of index of mps sites, else an ndarray is returned.
Return type:: dict, ndarray

canonicalise(stop_idx: Optional[int] = None)#

check_left_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#: check L-canonical

check_right_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#: check R-canonical

compress(temp_m_trunc=None, ret_s=False)#

inp: canonicalise MPS (or MPO)

Parameters:

temp_m_trunc (int or list of int) – Temporary truncation bond dimension. Overwrites the compression configuration in CompressConfig.
ret_s (bool) – Whether return the singular values at the bonds. The singular values are padded to a rectangular array with zero values.

Return type:

truncated MPS

conj() → Mps[source]#: complex conjugate

copy()#

property digest#

distance(other) → float[source]#

dot(other: MatrixProduct) → complex#: dot product of two mps / mpo

dot_ob(other: MatrixProduct) → complex#: dot product of two mps / mpo with open boundary, but the boundary of mps/mpo is larger than 1, different from the normal mps/mpo

dump(fname)[source]#

property e_occupations#: Electronic occupations \(a^\dagger_i a_i\) for each electronic DoF. The order is defined by e_dofs.

ensure_left_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#

ensure_right_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#

evolve(mpo, evolve_dt, normalize=True) → Mps[source]#

evolve_exact(h_mpo, evolve_dt, space)[source]#

expand_bond_dimension(hint_mpo=None, coef=1e-10, include_ex=True)[source]#: expand bond dimension as required in compress_config

expectation(mpo: Union[Mpo, Op, OpSum], self_conj: Optional[Mps] = None) → Union[float, complex][source]#

Calculate the expectation value of the operator mpo with respect to the MPS.

Parameters:

mpo (Union[MPO, Op, OpSum]) – The operator. Can be provided as: - MPO: Matrix Product Operator - Op: Single symbolic operator - OpSum: Sum of symbolic operators
self_conj (Mps, optional) – The MPS for the bra vector. If not provided, uses the conjugate of self.

Returns:

expectation – The expectation value or transition amplitude. Returns a float if the imaginary part is negligible.

Return type:

Union[float, complex]

Examples

>>> from renormalizer import Mps, Mpo, Model, Op, BasisHalfSpin
>>> model = Model([BasisHalfSpin(0)], [])
>>> mps = Mps.hartree_product_state(model, condition={})
>>> mpo = Mpo(model, Op("X", 0))
>>> mps.expectation(mpo)
0.0
>>> mps.expectation(Op("X", 0))  # direct Op input
0.0

>>> mps2 = Mps.hartree_product_state(model, condition={0: [0,1]})
>>> mps.expectation(mpo, self_conj=mps2)
1.0

expectations(mpos: List[Union[Mpo, Op, OpSum]], self_conj: Optional[Mps] = None, opt: bool = True) → ndarray[source]#

classmethod from_dense(model, wfn: ndarray)[source]#

classmethod from_mp(model, mplist)#

classmethod ground_state(model: Model, max_entangled: bool, normalize: bool = True, condition: Optional[Dict] = None)[source]#

Obtain ground state at \(T = 0\) or \(T = \infty\) (maximum entangled). Electronic DOFs are always at ground state. and vibrational DOFs depend on max_entangled. For Spin-Boson model the electronic DOF also depends on max_entangled.

Parameters:

model (Model) – system information.
max_entangled (bool) – temperature of the vibrational DOFs. If set to True, \(T = \infty\) and if set to False, \(T = 0\).
normalize (bool, optional) – if the returned Mps are normalized when max_entangled=True. Default is True. If normalize=False, the vibrational part is identity.
condition (dict, optional) – the same as hartree_product_state. only used in ba.BasisMultiElectron cases to define the occupation. Default is None.

Returns:

mps

Return type:

renormalizer.mps.Mps

classmethod hartree_product_state(model, condition: Optional[Dict] = None, qn_idx: Optional[int] = None)[source]#

Construct a Hartree product state

Parameters:

model (Model) – Model information.
condition (Dict) –
Dict with format {dof:local_state}. The default local state for dofs not specified is the “0” state. An example is {"e_1":1, "v_0":2, "v_3":[0, 0.707, 0.707]}.

Note

If there are bases that contain multiple dofs in the model, the value of the dict is the state of all dofs of the basis. For example, if a basis contains "e_1", "e_2" and "e_3", {"e_1": 2} ({"e_1": [0, 0, 1]}) means "e_3" is occupied and {"e_1": 1} ({"e_1": [0, 1, 0]}) means "e_2" is occupied. Be aware that in renormalizer.BasisMultiElectronVac the vacuum state is added to the 0 index.
qn_idx (int) – the site index of the quantum number center.

Returns:

Constructed mps (Mps)

property is_complex#

property is_left_canonical#: check the qn center in the L-canonical structure

property is_mpdm#

property is_mpo#

property is_mps#

property is_right_canonical#: check the qn center in the R-canonical structure

iter_idx_list(full: bool, stop_idx: Optional[int] = None)#

classmethod load(model: Model, fname: str)[source]#

metacopy() → Mps[source]#

move_qnidx(dstidx: int)#: Quantum number has a boundary site, left hand of the site is L system qn, right hand of the side is R system qn, the sum of quantum number of L system and R system is tot.

property mp_norm: float#

if self.is_left_canon:: assert self.check_left_canonical() return np.linalg.norm(np.ravel(self[-1]))
else:: assert self.check_right_canonical() return np.linalg.norm(np.ravel(self[0]))

property nexciton#

property norm#: the norm of the total wavefunction

normalize(kind)[source]#

normalize the wavefunction

Parameters:: kind (str) – “mps_only”: the mps part is normalized and coeff is not modified; “mps_norm_to_coeff”: the mps part is normalized and the norm is multiplied to coeff; “mps_and_coeff”: both mps and coeff is normalized
Return type:: self is overwritten.

property pbond_dims#

property pbond_list#

property ph_occupations#: phonon occupations \(b^\dagger_i b_i\) for each electronic DoF. The order is defined by v_dofs.

classmethod random(model: Model, qntot, m_max, percent=1.0) → Mps[source]#

scale(val, inplace=False)#

property site_num#

property threshold#

to_complex(inplace=False) → Mps[source]#

todense() → array[source]#

property total_bytes#

variational_compress(mpo=None, guess=None)#

Variational compress an mps/mpdm/mpo

Parameters:

mpo (renormalizer.mps.Mpo, optional) – Default is None. if mpo is not None, the returned mps is an approximation of mpo @ self
guess (renormalizer.mps.MatrixProduct, optional) – Initial guess of compressed mps/mpdm/mpo. Default is None.

Note

the variational compress related configurations is defined in self if guess=None, otherwise is defined in guess

Returns:: mp – a new compressed mps/mpdm/mpo, self is not overwritten. guess is overwritten.
Return type:: renormalizer.mps.MatrixProduct

property vbond_dims: List#

property vbond_list: List#

Matrix Product Operator (MPO)#

class renormalizer.mps.Mpo(model: ~typing.Optional[~renormalizer.model.model.Model] = None, terms: ~typing.Optional[~typing.Union[~renormalizer.model.op.Op, ~typing.List[~renormalizer.model.op.Op]]] = None, offset: ~renormalizer.utils.quantity.Quantity = <renormalizer.utils.quantity.Quantity object>, algo='qr')[source]#

Matrix product operator (MPO)

add(other: MatrixProduct)#

angle(other)#

append(array)#

apply(mp: MatrixProduct, canonicalise: bool = False) → MatrixProduct[source]#

property bond_dims: List#

property bond_dims_exact: ndarray#

property bond_dims_mean: int#

property bond_list: List#

build_empty_mp(num)#

build_empty_qn()#

build_none_qn()#

canonicalise(stop_idx: Optional[int] = None)#

check_left_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#: check L-canonical

check_right_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#: check R-canonical

compress(temp_m_trunc=None, ret_s=False)#

inp: canonicalise MPS (or MPO)

Parameters:

temp_m_trunc (int or list of int) – Temporary truncation bond dimension. Overwrites the compression configuration in CompressConfig.
ret_s (bool) – Whether return the singular values at the bonds. The singular values are padded to a rectangular array with zero values.

Return type:

truncated MPS

conj()#: complex conjugate

conj_trans()[source]#

contract(mps, algo='svd')[source]#

an approximation of mpo @ mps/mpdm/mpo

Parameters:

mps (Mps, Mpo, MpDm) –
algo (str, optional) – The algorithm to compress mpo @ mps/mpdm/mpo. It could be svd (default) and variational.

Returns:

new_mps – an approximation of mpo @ mps/mpdm/mpo. The input mps is not overwritten.

Return type:

Mps

Matrix Product Density Matrix (MPDM)#

class renormalizer.mps.MpDm[source]#

add(other)#

angle(other)#

append(array)#

apply(mp, canonicalise=False) → MpDmBase[source]#

property bond_dims: List#

property bond_dims_exact: ndarray#

property bond_dims_mean: int#

property bond_list: List#

build_empty_mp(num)#

build_empty_qn()#

build_none_qn()#

calc_1site_rdm(idx=None) → Dict[int, ndarray]#

Calculate 1-site reduced density matrix

\(\rho_i = \textrm{Tr}_{j \neq i} | \Psi \rangle \langle \Psi|\)

Parameters:: idx (int, list, tuple, optional) – site index of 1site_rdm. Default is None, which mean all the rdms are calculated.
Returns:: rdm – \(\{0:\rho_0, 1:\rho_1, \cdots\}\). The key is the index of the site.
Return type:: Dict

calc_2site_mutual_entropy() → ndarray#

Calculate mutual entropy between two sites. Also known as mutual information

\(m_{ij} = (s_i + s_j - s_{ij})/2\)

See Chemical Physics 323 (2006) 519–531

Returns:: mutual_entropy – mutual entropy with shape (nsite, nsite)
Return type:: 2d np.ndarry

calc_2site_rdm()#

Calculate 2-site reduced density matrix

\(\rho_{ij} = \textrm{Tr}_{k \neq i, k \neq j} | \Psi \rangle \langle \Psi |\).

Returns:: rdm – \(\{(0,1):\rho_{01}, (0,2):\rho_{02}, \cdots\}\). The key is a tuple of index of the site.
Return type:: Dict

calc_bond_entropy(s_array: Optional[array] = None) → ndarray#

Calculate von Neumann entropy at each bond according to \(S = -\textrm{Tr}(\rho \ln \rho)\) where \(\rho\) is the reduced density matrix of either block.

Parameters:: s_array (np.ndarray, optional) – The singular values array. Calculated by Mps.calc_bond_singular_values().
Returns:: S – a NumPy array containing the entropy values.
Return type:: 1D array

calc_bond_singular_values() → ndarray#

Calculate the singular values at each bond.

Returns:: Singular values
Return type:: 2D array

calc_edof_rdm() → ndarray#

Calculate the reduced density matrix of electronic DoF

\(\rho_{ij} = \langle \Psi | a_i^\dagger a_j | \Psi \rangle\)

Note

This function is designed for single-electron system. Fermionic commutation relation is not considered.

calc_entropy(entropy_type)#

Calculate 1site, 2site, mutual and bond Von Neumann entropy

\(\textrm{entropy} = -\textrm{Tr}(\rho \ln \rho)\) where \(\ln\) stands for natural logarithm.

1site entropy is the entropy between any site and the other (N-1) sites. 2site entropy is the entropy between any two sites and the other (N-2) sites. mutual entropy characterize the entropy between any two sites. bond entropy is the entropy between L-block and R-block.

Parameters:: entropy_type (str) – “1site”, “2site”, “mutual”, “bond”
Returns:: entropy – if entropy_type = “1site” or “2site”, a dictionary is returned and the key is the index or the tuple of index of mps sites, else an ndarray is returned.
Return type:: dict, ndarray

canonicalise(stop_idx: Optional[int] = None)#

check_left_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#: check L-canonical

check_right_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#: check R-canonical

compress(temp_m_trunc=None, ret_s=False)#

inp: canonicalise MPS (or MPO)

Parameters:

temp_m_trunc (int or list of int) – Temporary truncation bond dimension. Overwrites the compression configuration in CompressConfig.
ret_s (bool) – Whether return the singular values at the bonds. The singular values are padded to a rectangular array with zero values.

Return type:

truncated MPS

conj() → Mps#: complex conjugate

conj_trans()[source]#

contract(mps, algo='svd')#

an approximation of mpo @ mps/mpdm/mpo

Parameters:

mps (Mps, Mpo, MpDm) –
algo (str, optional) – The algorithm to compress mpo @ mps/mpdm/mpo. It could be svd (default) and variational.

Returns:

new_mps – an approximation of mpo @ mps/mpdm/mpo. The input mps is not overwritten.

Return type:

Mps

See also

renormalizer.mps.mp.MatrixProduct.compress: svd compression.
renormalizer.mps.mp.MatrixProduct.variational_compress: variational compression.

copy()#

property digest#

distance(other) → float#

dot(other: MatrixProduct) → complex#: dot product of two mps / mpo

dot_ob(other: MatrixProduct) → complex#: dot product of two mps / mpo with open boundary, but the boundary of mps/mpo is larger than 1, different from the normal mps/mpo

property dummy_qn#

dump(fname)#

property e_occupations#: Electronic occupations \(a^\dagger_i a_i\) for each electronic DoF. The order is defined by e_dofs.

ensure_left_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#

ensure_right_canonical(rtol: Optional[float] = None, atol: Optional[float] = None)#

evolve(mpo, evolve_dt, normalize=True) → Mps#

evolve_exact(h_mpo, evolve_dt, space)[source]#

classmethod exact_propagator(model: HolsteinModel, x, space='GS', shift=0.0)#: construct the GS space propagator e^{xH} exact MPO H=sum_{in} omega_{in} b^dagger_{in} b_{in} fortunately, the H is local. so e^{xH} = e^{xh1}e^{xh2}…e^{xhn} the bond dimension is 1 shift is the a constant for H+shift

expand_bond_dimension(hint_mpo=None, coef=1e-10, include_ex=True)#: expand bond dimension as required in compress_config

expectation(mpo: Union[Mpo, Op, OpSum], self_conj: Optional[Mps] = None) → Union[float, complex]#

Calculate the expectation value of the operator mpo with respect to the MPS.

Parameters:

mpo (Union[MPO, Op, OpSum]) – The operator. Can be provided as: - MPO: Matrix Product Operator - Op: Single symbolic operator - OpSum: Sum of symbolic operators
self_conj (Mps, optional) – The MPS for the bra vector. If not provided, uses the conjugate of self.

Returns:

expectation – The expectation value or transition amplitude. Returns a float if the imaginary part is negligible.

Return type:

Union[float, complex]

Examples

>>> from renormalizer import Mps, Mpo, Model, Op, BasisHalfSpin
>>> model = Model([BasisHalfSpin(0)], [])
>>> mps = Mps.hartree_product_state(model, condition={})
>>> mpo = Mpo(model, Op("X", 0))
>>> mps.expectation(mpo)
0.0
>>> mps.expectation(Op("X", 0))  # direct Op input
0.0

>>> mps2 = Mps.hartree_product_state(model, condition={0: [0,1]})
>>> mps.expectation(mpo, self_conj=mps2)
1.0

expectations(mpos: List[Union[Mpo, Op, OpSum]], self_conj: Optional[Mps] = None, opt: bool = True) → ndarray#

classmethod finiteT_cv(model, nexciton, m_max, spectratype, percent=1.0)#

classmethod from_dense(model, wfn: ndarray)[source]#

classmethod from_mp(model, mplist)#

classmethod from_mps(mps: Mps)[source]#

classmethod ground_state(model, max_entangled)[source]#

Parameters:

model (Model) – system information.
max_entangled (bool) – temperature of the vibrational DOFs. If set to True, \(T = \infty\) and if set to False, \(T = 0\).
normalize (bool, optional) – if the returned Mps are normalized when max_entangled=True. Default is True. If normalize=False, the vibrational part is identity.
condition (dict, optional) – the same as hartree_product_state. only used in ba.BasisMultiElectron cases to define the occupation. Default is None.

Returns:

mps

Return type:

renormalizer.mps.Mps

classmethod hartree_product_state(model, condition: Optional[Dict] = None, qn_idx: Optional[int] = None)#

Construct a Hartree product state

Parameters:

model (Model) – Model information.
condition (Dict) –
Dict with format {dof:local_state}. The default local state for dofs not specified is the “0” state. An example is {"e_1":1, "v_0":2, "v_3":[0, 0.707, 0.707]}.

Note

If there are bases that contain multiple dofs in the model, the value of the dict is the state of all dofs of the basis. For example, if a basis contains "e_1", "e_2" and "e_3", {"e_1": 2} ({"e_1": [0, 0, 1]}) means "e_3" is occupied and {"e_1": 1} ({"e_1": [0, 1, 0]}) means "e_2" is occupied. Be aware that in renormalizer.BasisMultiElectronVac the vacuum state is added to the 0 index.
qn_idx (int) – the site index of the quantum number center.

Returns:

Constructed mps (Mps)

classmethod identity(model: Model)#

construct the inter site MPO

Parameters:

model (HolsteinModel) – the molecular information
e_opera – the electronic operators. {imol: operator}, such as {1:”a”, 3:r”a^dagger”}
ph_opera – the vibrational operators. {(imol, iph): operator}, such as {(0,5):”b”}
scale (Quantity) – scalar to scale the mpo

Note

the operator index starts from 0,1,2…

property is_complex#

is_hermitian()#

property is_left_canonical#: check the qn center in the L-canonical structure

property is_mpdm#

property is_mpo#

property is_mps#

property is_right_canonical#: check the qn center in the R-canonical structure

iter_idx_list(full: bool, stop_idx: Optional[int] = None)#

classmethod load(model: Model, fname: str)#

classmethod max_entangled_ex(model, normalize=True)[source]#: T = infty locally maximal entangled EX state

classmethod max_entangled_gs(model) → MpDm[source]#

metacopy() → Mps#

move_qnidx(dstidx: int)#: Quantum number has a boundary site, left hand of the site is L system qn, right hand of the side is R system qn, the sum of quantum number of L system and R system is tot.

property mp_norm: float#

if self.is_left_canon:: assert self.check_left_canonical() return np.linalg.norm(np.ravel(self[-1]))
else:: assert self.check_right_canonical() return np.linalg.norm(np.ravel(self[0]))

property nexciton#

property norm#: the norm of the total wavefunction

normalize(kind)#

normalize the wavefunction

Parameters:: kind (str) – “mps_only”: the mps part is normalized and coeff is not modified; “mps_norm_to_coeff”: the mps part is normalized and the norm is multiplied to coeff; “mps_and_coeff”: both mps and coeff is normalized
Return type:: self is overwritten.

classmethod onsite(model: Model, opera, dipole=False, dof_set=None)#

property pbond_dims#

property pbond_list#

property ph_occupations#: phonon occupations \(b^\dagger_i b_i\) for each electronic DoF. The order is defined by v_dofs.

classmethod ph_onsite(model: HolsteinModel, opera: str, mol_idx: int, ph_idx=0)#

promote_mt_type(mp)#

classmethod random(mpo, qntot, m_max, percent=0)[source]#

scale(val, inplace=False)#

property site_num#

property threshold#

to_complex(inplace=False) → Mps#

todense()[source]#

property total_bytes#

try_swap_site(new_model: Model, swap_jw: bool, algo='Hopcroft-Karp')#

variational_compress(mpo=None, guess=None)#

Variational compress an mps/mpdm/mpo

Parameters:

mpo (renormalizer.mps.Mpo, optional) – Default is None. if mpo is not None, the returned mps is an approximation of mpo @ self
guess (renormalizer.mps.MatrixProduct, optional) – Initial guess of compressed mps/mpdm/mpo. Default is None.

Note

the variational compress related configurations is defined in self if guess=None, otherwise is defined in guess

Returns:: mp – a new compressed mps/mpdm/mpo, self is not overwritten. guess is overwritten.
Return type:: renormalizer.mps.MatrixProduct

property vbond_dims: List#

property vbond_list: List#

Thermal Propagation#

class renormalizer.mps.thermalprop.ThermalProp(init_mpdm: MpDm, h_mpo_model: Optional[Model] = None, exact: bool = False, space: str = 'GS', evolve_config: Optional[EvolveConfig] = None, dump_mps: Optional[bool] = None, dump_dir: Optional[str] = None, job_name: Optional[str] = None, properties: Optional[Property] = None, auto_expand: bool = True)[source]#

Bases: TdMpsJob

Thermally Propagate initial matrix product density operator (MpDm) in imaginary time.

Parameters:

init_mpdm (MpDm) – the initial density matrix to be propagated. Usually identity.
h_mpo_model (Model) – Model for the system Hamiltonian. Default is the same with init_mpdm.model.
exact (bool) – whether propagate assuming Hamiltonian is local \(\hat H = \sum_i \hat H_i = \sum_{in} \omega_{in} b^\dagger_{in} b_{in}\) and exact propagation is possible through \(e^{xH} = e^{xh_1}e^{xh_2} \cdots e^{xh_n}\). If set to True, properties such as occupations are not calculated during time evolution for better efficiency.
space (str) – the space of exact propagation. Possible options are "GS" or "EX". If set to "GS", then the exact propagation is performed in zero exciton space. If set to "EX", then the exact propagation is performed in one exciton space, i.e. the vibrations are regarded as displaced oscillators.
evolve_config (EvolveConfig) – config when evolving the MpDm in imaginary time.
dump_mps (str) – if dump mps when dumping, “all”, “one”, None; Default to None
dump_dir (str) – the directory for logging and numerical result output.
job_name (str) – the name of the calculation job which determines the file name of the logging and numerical result output.
properties (Property) –

dump_dict()#

property e_occupations_array#

evolve(evolve_dt=None, nsteps=None, evolve_time=None)[source]#

Parameters:

evolve_dt (float) – the time step to run evolve_single_step and process_mps.
nsteps (int) – the total number of evolution steps
evolve_time (float) – the total evolution time

Note

evolve_dt math: ` imes` nsteps = evolve_time, otherwise nsteps has a higher priority.

evolve_exact(old_mpdm: MpDm, evolve_dt)[source]#

evolve_prop(old_mpdm, evolve_dt)[source]#

evolve_single_step(evolve_dt)[source]#

Evolve the mps for a single step with step size evolve_dt.

Returns:: new mps after the evolution

property evolve_times_array#

get_dump_dict()[source]#

Obtain calculated properties to dump in dict type.

Returns:: return a (ordered) dict to dump as npz

init_mps()[source]#

Returns:: initial mps of the system

property latest_evolve_time#

property ph_occupations_array#

process_mps(mps)[source]#

Process the newly evolved the mps. Primarily for the calculation of properties. Note that currently self.latest_mps has not been updated.

Parameters:: mps – The evolved new mps of the time step.

stop_evolve_criteria()#

property vn_entropy_array#

renormalizer.mps.thermalprop.load_thermal_state(model, path: str)[source]#

Load thermal propagated state from disk. Return None if the file is not found.

Parameters:

model (MolList) – system information
path (str) – the path to load thermal state from. Should be an numpy .npz file.

Returns: Loaded MpDm