`quimb.tensor.tensor_2d`¶

Classes and algorithms related to 2D tensor networks.

Module Contents¶

Classes¶

`Rotator2D`	Object for rotating coordinates and various contraction functions so
`TensorNetwork2D`	Mixin class for tensor networks with a square lattice two-dimensional
`TensorNetwork2DVector`	Mixin class for a 2D square lattice vector TN, i.e. one with a single
`TensorNetwork2DOperator`	Mixin class for a 2D square lattice TN operator, i.e. one with both
`TensorNetwork2DFlat`	Mixin class for a 2D square lattice tensor network with a single tensor
`PEPS`	Projected Entangled Pair States object (2D):
`PEPO`	Projected Entangled Pair Operator object:

Functions¶

`manhattan_distance`(coo_a, coo_b)
`nearest_neighbors`(coo)
`gen_2d_bonds`(Lx, Ly[, steppers, coo_filter, cyclic])	Convenience function for tiling pairs of bond coordinates on a 2D
`gen_2d_plaquette`(coo0, steps)	Generate a plaquette at site `coo0` by stepping first in `steps` and
`gen_2d_plaquettes`(Lx, Ly, tiling)	Generate a tiling of plaquettes in a square 2D lattice.
`gen_2d_strings`(Lx, Ly)	Generate all length-wise strings in a square 2D lattice.
`parse_boundary_sequence`(sequence)	Ensure `sequence` is a tuple of boundary sequence strings from
`is_lone_coo`(where)	Check if `where` has been specified as a single coordinate pair.
`gate_string_split_`(TG, where, string, original_ts, ...)
`gate_string_reduce_split_`(TG, where, string, ...)
`show_2d`(tn_2d[, show_lower, show_upper])	Base function for printing a unicode schematic of flat 2D TNs.
`calc_plaquette_sizes`(coo_groups[, autogroup])	Find a sequence of plaquette blocksizes that will cover all the terms
`plaquette_to_sites`(p)	Turn a plaquette `((i0, j0), (di, dj))` into the sites it contains.
`calc_plaquette_map`(plaquettes)	Generate a dictionary of all the coordinate pairs in `plaquettes`
`gen_long_range_path`(ij_a, ij_b[, sequence])	Generate a string of coordinates, in order, from `ij_a` to `ij_b`.
`gen_long_range_swap_path`(ij_a, ij_b[, sequence])	Generate the coordinates or a series of swaps that would bring `ij_a`
`swap_path_to_long_range_path`(swap_path, ij_a)	Generates the ordered long-range path - a sequence of coordinates - from
`get_swap`(dp, dtype, backend)

Attributes¶

`BOUNDARY_SEQUENCE_VALID`
`BOUNDARY_SEQUENCE_MAP`

quimb.tensor.tensor_2d.manhattan_distance(coo_a, coo_b)[source]¶

quimb.tensor.tensor_2d.nearest_neighbors(coo)[source]¶

quimb.tensor.tensor_2d.gen_2d_bonds(Lx, Ly, steppers=None, coo_filter=None, cyclic=False)[source]¶

Convenience function for tiling pairs of bond coordinates on a 2D lattice given a function like lambda i, j: (i + 1, j + 1).

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
steppers (callable or sequence of callable, optional) – Function(s) that take args (i, j) and generate another coordinate, thus defining a bond. Only valid steps are taken. If not given, defaults to nearest neighbor bonds.
coo_filter (callable) – Function that takes args (i, j) and only returns True if this is to be a valid starting coordinate.

Yields:

bond (tuple[tuple[int, int], tuple[int, int]]) – A pair of coordinates.

Examples

Generate nearest neighbor bonds:

>>> for bond in gen_2d_bonds(2, 2, [lambda i, j: (i, j + 1),
>>>                                 lambda i, j: (i + 1, j)]):
>>>     print(bond)
((0, 0), (0, 1))
((0, 0), (1, 0))
((0, 1), (1, 1))
((1, 0), (1, 1))

Generate next nearest neighbor digonal bonds:

>>> for bond in gen_2d_bonds(2, 2, [lambda i, j: (i + 1, j + 1),
>>>                                 lambda i, j: (i + 1, j - 1)]):
>>>     print(bond)
((0, 0), (1, 1))
((0, 1), (1, 0))

quimb.tensor.tensor_2d.gen_2d_plaquette(coo0, steps)[source]¶

Generate a plaquette at site coo0 by stepping first in steps and then the reverse steps.

Parameters:

coo0 (tuple) – The coordinate of the first site in the plaquette.
steps (tuple) – The steps to take to generate the plaquette. Each element should be one of ('x+', 'x-', 'y+', 'y-').

Yields:

coo (tuple) – The coordinates of the sites in the plaquette, including the last site which will be the same as the first.

quimb.tensor.tensor_2d.gen_2d_plaquettes(Lx, Ly, tiling)[source]¶

Generate a tiling of plaquettes in a square 2D lattice.

Parameters:

Lx (int) – The length of the lattice in the x direction.
Ly (int) – The length of the lattice in the y direction.
tiling ({'1', '2', 'full'}) –
The tiling to use:
- ’1’: plaquettes in a checkerboard pattern, such that each edge
  is covered by a maximum of one plaquette.
- ’2’ or ‘full’: dense tiling of plaquettes. All bulk edges will
  be covered twice.

Yields:

plaquette (tuple[tuple[int]]) – The coordinates of the sites in each plaquette, including the last site which will be the same as the first.

quimb.tensor.tensor_2d.gen_2d_strings(Lx, Ly)[source]¶: Generate all length-wise strings in a square 2D lattice.

class quimb.tensor.tensor_2d.Rotator2D(tn, xrange, yrange, from_which, stepsize=1)[source]¶

Object for rotating coordinates and various contraction functions so that the core algorithms only have to written once, but nor does the actual TN have to be modified.

Parameters:

tn (TensorNetwork2D) – The tensor network to rotate coordinates for.
xrange (tuple[int, int]) – The range of x-coordinates to range over.
yrange (tuple[int, int]) – The range of y-coordinates to range over.
from_which ({'xmin', 'xmax', 'ymin', 'ymax'}) – The direction to sweep from.
stepsize (int, optional) – The step size to use when sweeping.

sweep_other()¶

cyclic_x()¶

cyclic_y()¶

get_jnext(j)[source]¶

get_opposite_env_fn()[source]¶: Get the function and location label for contracting boundaries in the opposite direction to main sweep.

quimb.tensor.tensor_2d.BOUNDARY_SEQUENCE_VALID¶

quimb.tensor.tensor_2d.BOUNDARY_SEQUENCE_MAP¶

quimb.tensor.tensor_2d.parse_boundary_sequence(sequence)[source]¶: Ensure sequence is a tuple of boundary sequence strings from {'xmin', 'xmax', 'ymin', 'ymax'}

class quimb.tensor.tensor_2d.TensorNetwork2D(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: quimb.tensor.tensor_arbgeom.TensorNetworkGen

Mixin class for tensor networks with a square lattice two-dimensional structure, indexed by [{row},{column}] so that:

             'Y{j}'
                v

i=Lx-1 ●──●──●──●──●──●──   ──●
       |  |  |  |  |  |       |
             ...
       |  |  |  |  |  | 'I{i},{j}' = 'I3,5' e.g.
i=3    ●──●──●──●──●──●──
       |  |  |  |  |  |       |
i=2    ●──●──●──●──●──●──   ──●    <== 'X{i}'
       |  |  |  |  |  |  ...  |
i=1    ●──●──●──●──●──●──   ──●
       |  |  |  |  |  |       |
i=0    ●──●──●──●──●──●──   ──●

     j=0, 1, 2, 3, 4, 5    j=Ly-1

This implies the following conventions:

the ‘up’ bond is coordinates (i, j), (i + 1, j)

the ‘down’ bond is coordinates (i, j), (i - 1, j)

the ‘right’ bond is coordinates (i, j), (i, j + 1)

the ‘left’ bond is coordinates (i, j), (i, j - 1)

property Lx¶: The number of rows.

property Ly¶: The number of columns.

property nsites¶: The total number of sites.

property x_tag_id¶: The string specifier for tagging each row of this 2D TN.

property x_tags¶: A tuple of all of the Lx different row tags.

property y_tag_id¶: The string specifier for tagging each column of this 2D TN.

property y_tags¶: A tuple of all of the Ly different column tags.

_NDIMS = 2¶

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly')¶

row_tag[source]¶

row_tags¶

col_tag[source]¶

col_tags¶

flatten_[source]¶

contract_boundary_from_[source]¶

contract_boundary_from_xmin_[source]¶

contract_boundary_from_xmax_[source]¶

contract_boundary_from_ymin_[source]¶

contract_boundary_from_ymax_[source]¶

contract_boundary_[source]¶

contract_mps_sweep_[source]¶

compute_xmin_environments[source]¶: Compute the self.Lx 1D boundary tensor networks describing the lower environments of each row in this 2D tensor network. See compute_x_environments() for full details.

compute_xmax_environments[source]¶: Compute the self.Lx 1D boundary tensor networks describing the upper environments of each row in this 2D tensor network. See compute_x_environments() for full details.

compute_ymin_environments[source]¶: Compute the self.Ly 1D boundary tensor networks describing the left environments of each column in this 2D tensor network. See compute_y_environments() for full details.

compute_ymax_environments[source]¶: Compute the self.Ly 1D boundary tensor networks describing the right environments of each column in this 2D tensor network. See compute_y_environments() for full details.

coarse_grain_hotrg_[source]¶

contract_hotrg_[source]¶

contract_ctmrg_[source]¶

_compatible_2d(other)[source]¶: Check whether self and other are compatible 2D tensor networks such that they can remain a 2D tensor network when combined.

combine(other, *, virtual=False, check_collisions=True)[source]¶

Combine this tensor network with another, returning a new tensor network. If the two are compatible, cast the resulting tensor network to a TensorNetwork2D instance.

Parameters:

other (TensorNetwork2D or TensorNetwork) – The other tensor network to combine with.
virtual (bool, optional) – Whether the new tensor network should copy all the incoming tensors (False, the default), or view them as virtual (True).
check_collisions (bool, optional) – Whether to check for index collisions between the two tensor networks before combining them. If True (the default), any inner indices that clash will be mangled.

Return type:

TensorNetwork2D or TensorNetwork

site_tag(i, j=None)[source]¶: The name of the tag specifiying the tensor at site (i, j).

x_tag(i)[source]¶

y_tag(j)[source]¶

maybe_convert_coo(x)[source]¶: Check if x is a tuple of two ints and convert to the corresponding site tag if so.

_get_tids_from_tags(tags, which='all')[source]¶: This is the function that lets coordinates such as (i, j) be used for many ‘tag’ based functions.

gen_site_coos()[source]¶: Generate coordinates for all the sites in this 2D TN.

gen_bond_coos()[source]¶: Generate pairs of coordinates for all the bonds in this 2D TN.

gen_horizontal_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i, j + 1).

gen_horizontal_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i, j + 1) where j is even, which thus don’t overlap at all.

gen_horizontal_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i, j + 1) where j is odd, which thus don’t overlap at all.

gen_vertical_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j).

gen_vertical_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j) where i is even, which thus don’t overlap at all.

gen_vertical_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j) where i is odd, which thus don’t overlap at all.

gen_diagonal_left_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j - 1).

gen_diagonal_left_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j - 1) where j is even, which thus don’t overlap at all.

gen_diagonal_left_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j - 1) where j is odd, which thus don’t overlap at all.

gen_diagonal_right_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j + 1).

gen_diagonal_right_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j + 1) where i is even, which thus don’t overlap at all.

gen_diagonal_right_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j + 1) where i is odd, which thus don’t overlap at all.

gen_diagonal_bond_coos()[source]¶: Generate all next nearest neighbor diagonal coordinate pairs.

valid_coo(coo, xrange=None, yrange=None)[source]¶

Check whether coo is in-bounds.

Parameters:

coo ((int, int, int), optional) – The coordinates to check.
xrange ((int, int), optional) – The range of allowed values for the x and y coordinates.
yrange ((int, int), optional) – The range of allowed values for the x and y coordinates.

Return type:

bool

get_ranges_present()[source]¶

Return the range of site coordinates present in this TN.

Returns:: xrange, yrange – The minimum and maximum site coordinates present in each direction.
Return type:: tuple[tuple[int, int]]

is_cyclic_x(j=None, imin=None, imax=None)[source]¶: Check if the x dimension is cyclic (periodic), specifically whether a bond exists between (imin, j) and (imax, j), with default values of imin = 0 and imax = Lx - 1, and j at the center of the lattice. If imin and imax are adjacent then this is considered False, since there is no ‘extra’ connectivity.

is_cyclic_y(i=None, jmin=None, jmax=None)[source]¶: Check if the y dimension is cyclic (periodic), specifically whether a bond exists between (i, jmin) and (i, jmax), with default values of jmin = 0 and jmax = Ly - 1, and i at the center of the lattice. If jmin and jmax are adjacent then this is considered False, since there is no ‘extra’ connectivity.

__getitem__(key)[source]¶: Key based tensor selection, checking for integer based shortcut.

show()[source]¶: Print a unicode schematic of this 2D TN and its bond dimensions.

_repr_info()[source]¶: General info to show in various reprs. Sublasses can add more relevant info to this dict.

flatten(fuse_multibonds=True, inplace=False)[source]¶: Contract all tensors corresponding to each site into one.

gen_pairs(xrange=None, yrange=None, xreverse=False, yreverse=False, coordinate_order='xy', xstep=None, ystep=None, stepping_order='xy', step_only=None)[source]¶

Helper function for generating pairs of cooordinates for all bonds within a certain range, optionally specifying an order.

Parameters:

xrange ((int, int), optional) – The range of allowed values for the x and y coordinates.
yrange ((int, int), optional) – The range of allowed values for the x and y coordinates.
xreverse (bool, optional) – Whether to reverse the order of the x and y sweeps.
yreverse (bool, optional) – Whether to reverse the order of the x and y sweeps.
coordinate_order (str, optional) – The order in which to sweep the x and y coordinates. Earlier dimensions will change slower. If the corresponding range has size 1 then that dimension doesn’t need to be specified.
xstep (int, optional) – When generating a bond, step in this direction to yield the neighboring coordinate. By default, these follow xreverse and yreverse respectively.
ystep (int, optional) – When generating a bond, step in this direction to yield the neighboring coordinate. By default, these follow xreverse and yreverse respectively.
stepping_order (str, optional) – The order in which to step the x and y coordinates to generate bonds. Does not need to include all dimensions.
step_only (int, optional) – Only perform the ith steps in stepping_order, used to interleave canonizing and compressing for example.

Yields:

coo_a, coo_b (((int, int), (int, int)))

canonize_plane(xrange, yrange, equalize_norms=False, canonize_opts=None, **gen_pair_opts)[source]¶: Canonize every pair of tensors within a subrange, optionally specifying a order to visit those pairs in.

canonize_row(i, sweep, yrange=None, **canonize_opts)[source]¶

Canonize all or part of a row.

If sweep == 'right' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─  ==>  ─●──>──>──>──>──o──●─ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstart      jstop           jstart      jstop

If sweep == 'left' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─  ==>  ─●──o──<──<──<──<──●─ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstop       jstart          jstop       jstart

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

i (int) – Which row to canonize.
sweep ({'right', 'left'}) – Which direction to sweep in.
jstart (int or None) – Starting column, defaults to whole row.
jstop (int or None) – Stopping column, defaults to whole row.
canonize_opts – Supplied to canonize_between.

canonize_column(j, sweep, xrange=None, **canonize_opts)[source]¶

Canonize all or part of a column.

If sweep='up' then:

 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
─●──●──●─       ─●──o──●─ istop
 |  |  |   ==>   |  |  |
─●──●──●─       ─●──^──●─
 |  |  |         |  |  |
─●──●──●─       ─●──^──●─ istart
 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
    .               .
    j               j

If sweep='down' then:

 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
─●──●──●─       ─●──v──●─ istart
 |  |  |   ==>   |  |  |
─●──●──●─       ─●──v──●─
 |  |  |         |  |  |
─●──●──●─       ─●──o──●─ istop
 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
    .               .
    j               j

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

j (int) – Which column to canonize.
sweep ({'up', 'down'}) – Which direction to sweep in.
xrange (None or (int, int), optional) – The range of columns to canonize.
canonize_opts – Supplied to canonize_between.

canonize_row_around(i, around=(0, 1))[source]¶

compress_plane(xrange, yrange, max_bond=None, cutoff=1e-10, equalize_norms=False, compress_opts=None, **gen_pair_opts)[source]¶: Compress every pair of tensors within a subrange, optionally specifying a order to visit those pairs in.

compress_row(i, sweep, yrange=None, max_bond=None, cutoff=1e-10, equalize_norms=False, compress_opts=None)[source]¶

Compress all or part of a row.

If sweep == 'right' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━  ━━>  ━●━━>──>──>──>──o━━●━ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstart      jstop           jstart      jstop

If sweep == 'left' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━  ━━>  ━●━━o──<──<──<──<━━●━ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstop       jstart          jstop       jstart

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

i (int) – Which row to compress.
sweep ({'right', 'left'}) – Which direction to sweep in.
yrange (tuple[int, int] or None) – The range of columns to compress.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
compress_opts (None or dict, optional) – Supplied to compress_between().

compress_column(j, sweep, xrange=None, max_bond=None, cutoff=1e-10, equalize_norms=False, compress_opts=None)[source]¶

Compress all or part of a column.

If sweep='up' then:

 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──o──●─  .
 ┃  ┃  ┃   ==>   ┃  |  ┃   .
─●──●──●─       ─●──^──●─  . xrange
 ┃  ┃  ┃         ┃  |  ┃   .
─●──●──●─       ─●──^──●─  .
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
    .               .
    j               j

If sweep='down' then:

 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──v──●─ .
 ┃  ┃  ┃   ==>   ┃  |  ┃  .
─●──●──●─       ─●──v──●─ . xrange
 ┃  ┃  ┃         ┃  |  ┃  .
─●──●──●─       ─●──o──●─ .
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
    .               .
    j               j

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

j (int) – Which column to compress.
sweep ({'up', 'down'}) – Which direction to sweep in.
xrange (None or (int, int), optional) – The range of rows to compress.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
compress_opts (None or dict, optional) – Supplied to compress_between().

_contract_boundary_core_via_1d(xrange, yrange, from_which, max_bond, cutoff=1e-10, method='dm', layer_tags=None, **compress_opts)[source]¶

_contract_boundary_core(xrange, yrange, from_which, max_bond, cutoff=1e-10, canonize=True, layer_tags=None, compress_late=True, sweep_reverse=False, equalize_norms=False, compress_opts=None, canonize_opts=None)[source]¶

_contract_boundary_full_bond(xrange, yrange, from_which, max_bond, cutoff=0.0, method='eigh', renorm=False, optimize='auto-hq', opposite_envs=None, equalize_norms=False, contract_boundary_opts=None)[source]¶

Contract the boundary of this 2D TN using the ‘full bond’ environment information obtained from a boundary contraction in the opposite direction.

Parameters:

xrange ((int, int) or None, optional) – The range of rows to contract and compress.
yrange ((int, int)) – The range of columns to contract and compress.
from_which ({'xmin', 'ymin', 'xmax', 'ymax'}) – Which direction to contract the rectangular patch from.
max_bond (int) – The maximum boundary dimension, AKA ‘chi’. By default used for the opposite direction environment contraction as well.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction - only for the opposite direction environment contraction.
method ({'eigh', 'eig', 'svd', 'biorthog'}, optional) – Which similarity decomposition method to use to compress the full bond environment.
renorm (bool, optional) – Whether to renormalize the isometric projection or not.
optimize (str or PathOptimize, optimize) – Contraction optimizer to use for the exact contractions.
opposite_envs (dict, optional) – If supplied, the opposite environments will be fetched or lazily computed into this dict depending on whether they are missing.
contract_boundary_opts – Other options given to the opposite direction environment contraction.

_contract_boundary_projector(xrange, yrange, from_which, max_bond=None, cutoff=1e-10, lazy=False, equalize_norms=False, optimize='auto-hq', compress_opts=None)[source]¶

Contract the boundary of this 2D tensor network by explicitly computing and inserting explicit local projector tensors, which can optionally be left uncontracted. Multilayer networks are naturally supported.

Parameters:

xrange (tuple) – The range of x indices to contract.
yrange (tuple) – The range of y indices to contract.
from_which ({'xmin', 'xmax', 'ymin', 'ymax'}) – From which boundary to contract.
max_bond (int, optional) – The maximum bond dimension to contract to. If None (default), compression is left to cutoff.
cutoff (float, optional) – The cutoff to use for boundary compression.
lazy (bool, optional) – Whether to leave the boundary tensors uncontracted. If False (the default), the boundary tensors are contracted and the resulting boundary has a single tensor per site.
equalize_norms (bool, optional) – Whether to actively absorb the norm of modified tensors into self.exponent.
optimize (str or PathOptimizer, optional) – The contract path optimization to use when forming the projector tensors.
compress_opts (dict, optional) – Other options to pass to svd_truncated().

See also

TensorNetwork.insert_compressor_between_regions

contract_boundary_from(xrange, yrange, from_which, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶: Unified entrypoint for contracting any rectangular patch of tensors from any direction, with any boundary method.

contract_boundary_from_xmin(xrange, yrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the bottom, canonizing and compressing (left to right) along the way. If layer_tags is None this looks like:

a) contract

│  │  │  │  │
●──●──●──●──●       │  │  │  │  │
│  │  │  │  │  -->  ●══●══●══●══●
●──●──●──●──●

b) optionally canonicalize

│  │  │  │  │
●══●══<══<══<

c) compress in opposite direction

│  │  │  │  │  -->  │  │  │  │  │  -->  │  │  │  │  │
>──●══●══●══●  -->  >──>──●══●══●  -->  >──>──>──●══●
.  .           -->     .  .        -->        .  .

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

│ ││ ││ ││ ││ │       │ ││ ││ ││ ││ │
●─○●─○●─○●─○●─○       ●─○●─○●─○●─○●─○
│ ││ ││ ││ ││ │  ==>   ╲│ ╲│ ╲│ ╲│ ╲│
●─○●─○●─○●─○●─○         ●══●══●══●══●

b) contract and compress a single layer only

│ ││ ││ ││ ││ │
│ ○──○──○──○──○
│╱ │╱ │╱ │╱ │╱
●══<══<══<══<

c) contract and compress the next layer

╲│ ╲│ ╲│ ╲│ ╲│
 >══>══>══>══●

Parameters:

xrange ((int, int)) – The range of rows to compress (inclusive).
yrange ((int, int) or None, optional) – The range of columns to compress (inclusive), sweeping along with canonization and compression. Defaults to all columns.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

contract_boundary_from_xmax(xrange, yrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, inplace=False, sweep_reverse=False, compress_opts=None, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the top, canonizing and compressing (right to left) along the way. If layer_tags is None this looks like:

a) contract

●──●──●──●──●
|  |  |  |  |  -->  ●══●══●══●══●
●──●──●──●──●       |  |  |  |  |
|  |  |  |  |

b) optionally canonicalize

●══●══<══<══<
|  |  |  |  |

c) compress in opposite direction

>──●══●══●══●  -->  >──>──●══●══●  -->  >──>──>──●══●
|  |  |  |  |  -->  |  |  |  |  |  -->  |  |  |  |  |
.  .           -->     .  .        -->        .  .

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

●─○●─○●─○●─○●─○         ●══●══●══●══●
│ ││ ││ ││ ││ │  ==>   ╱│ ╱│ ╱│ ╱│ ╱│
●─○●─○●─○●─○●─○       ●─○●─○●─○●─○●─○
│ ││ ││ ││ ││ │       │ ││ ││ ││ ││ │

b) contract and compress a single layer only

●══<══<══<══<
│╲ │╲ │╲ │╲ │╲
│ ○──○──○──○──○
│ ││ ││ ││ ││ │

c) contract and compress the next layer

 ●══●══●══●══●
╱│ ╱│ ╱│ ╱│ ╱│

Parameters:

xrange ((int, int)) – The range of rows to compress (inclusive).
yrange ((int, int) or None, optional) – The range of columns to compress (inclusive), sweeping along with canonization and compression. Defaults to all columns.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or str, optional) – If None, all tensors at each coordinate pair [(i, j), (i - 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i - 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

contract_boundary_from_ymin(yrange, xrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the left, canonizing and compressing (bottom to top) along the way. If layer_tags is None this looks like:

a) contract

●──●──       ●──
│  │         ║
●──●──  ==>  ●──
│  │         ║
●──●──       ●──

b) optionally canonicalize

●──       v──
║         ║
●──  ==>  v──
║         ║
●──       ●──

c) compress in opposite direction

v──       ●──
║         │
v──  ==>  ^──
║         │
●──       ^──

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

○──○──           ●──○──
│╲ │╲            │╲ │╲
●─○──○──         ╰─●──○──
 ╲│╲╲│╲     ==>    │╲╲│╲
  ●─○──○──         ╰─●──○──
   ╲│ ╲│             │ ╲│
    ●──●──           ╰──●──

b) contract and compress a single layer only

   ○──
 ╱╱ ╲
●─── ○──
 ╲ ╱╱ ╲
  ^─── ○──
   ╲ ╱╱
    ^─────

c) contract and compress the next layer

●──
│╲
╰─●──
  │╲
  ╰─●──
    │
    ╰──

Parameters:

yrange ((int, int)) – The range of columns to compress (inclusive).
xrange ((int, int) or None, optional) – The range of rows to compress (inclusive), sweeping along with canonization and compression. Defaults to all rows.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or str, optional) – If None, all tensors at each coordinate pair [(i, j), (i, j + 1)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

contract_boundary_from_ymax(yrange, xrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the left, canonizing and compressing (top to bottom) along the way. If layer_tags is None this looks like:

a) contract

──●──●       ──●
  │  │         ║
──●──●  ==>  ──●
  │  │         ║
──●──●       ──●

b) optionally canonicalize

──●       ──v
  ║         ║
──●  ==>  ──v
  ║         ║
──●       ──●

c) compress in opposite direction

──v       ──●
  ║         │
──v  ==>  ──^
  ║         │
──●       ──^

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

    ──○──○           ──○──●
     ╱│ ╱│            ╱│ ╱│
  ──○──○─●         ──○──●─╯
   ╱│╱╱│╱   ==>     ╱│╱╱│
──○──○─●         ──○──●─╯
  │╱ │╱            │╱ │
──●──●           ──●──╯

b) contract and compress a single layer only

    ──○
     ╱ ╲╲
  ──○────v
   ╱ ╲╲ ╱
──○────v
   ╲╲ ╱
─────●

c) contract and compress the next layer

       ╲
    ────v
     ╲ ╱
  ────v
   ╲ ╱
────●

Parameters:

yrange ((int, int)) – The range of columns to compress (inclusive).
xrange ((int, int) or None, optional) – The range of rows to compress (inclusive), sweeping along with canonization and compression. Defaults to all rows.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or str, optional) – If None, all tensors at each coordinate pair [(i, j), (i, j - 1)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

_contract_interleaved_boundary_sequence(*, contract_boundary_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, max_separation=1, max_unfinished=1, around=None, equalize_norms=False, canonize=False, canonize_opts=None, final_contract=True, final_contract_opts=None, progbar=False, inplace=False)[source]¶: Unified handler for performing iterleaved contractions in a sequence of inwards boundary directions.

contract_boundary(max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, compress_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, max_separation=1, max_unfinished=1, around=None, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=None, inplace=False, **contract_boundary_opts)[source]¶

Contract the boundary of this 2D tensor network inwards:

●──●──●──●       ●──●──●──●       ●──●──●
│  │  │  │       │  │  │  │       ║  │  │
●──●──●──●       ●──●──●──●       ^──●──●       >══>══●       >──v
│  │ij│  │  ==>  │  │ij│  │  ==>  ║ij│  │  ==>  │ij│  │  ==>  │ij║
●──●──●──●       ●══<══<══<       ^──<──<       ^──<──<       ^──<
│  │  │  │
●──●──●──●

Optionally from any or all of the boundary, in multiple layers, and stopping around a region. The default is to contract the boundary from the two shortest opposing sides.

Parameters:

around (None or sequence of (int, int), optional) – If given, don’t contract the square of sites bounding these coordinates.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or sequence of str, optional) – If given, perform a multilayer contraction, contracting the inner sites in each layer into the boundary individually.
compress_opts (None or dict, optional) – Other low level options to pass to compress_between().
sequence (sequence of {'xmin', 'xmax', 'ymin', 'ymax'}, optional) – Which directions to cycle throught when performing the inwards contractions, i.e. from that direction. If around is specified you will likely need all of these! Default is to contract from the two shortest opposing sides.
xmin (int, optional) – The initial bottom boundary row, defaults to 0.
xmax (int, optional) – The initial top boundary row, defaults to Lx - 1.
ymin (int, optional) – The initial left boundary column, defaults to 0.
ymax (int, optional) – The initial right boundary column, defaults to Ly - 1..
max_separation (int, optional) – If around is None, when any two sides become this far apart simply contract the remaining tensor network.
max_unfinished (int, optional) – If around is None, when this many sides are still not within max_separation simply contract the remaining tensor network.
around – If given, don’t contract the square of sites bounding these coordinates.
equalize_norms (bool or float, optional) – Whether to equalize the norms of the boundary tensors after each contraction, gathering the overall scaling coefficient, log10, in tn.exponent.
final_contract (bool, optional) – Whether to exactly contract the remaining tensor network after the boundary contraction.
final_contract_opts (None or dict, optional) – Options to pass to contract(), optimize defaults to 'auto-hq'.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the contraction in place or not.
contract_boundary_opts – Supplied to contract_boundary_from(), including compression and canonization options.

contract_mps_sweep(max_bond=None, *, cutoff=1e-10, canonize=True, direction=None, **contract_boundary_opts)[source]¶

Contract this 2D tensor network by sweeping an MPS across from one side to the other.

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
direction ({'xmin', 'xmax', 'ymin', 'ymax'}, optional) – Which direction to sweep from. If None (default) then the shortest boundary is chosen.
contract_boundary_opts – Supplied to contract_boundary_from(), including compression and canonization options.

contract_full_bootstrap(n, *, optimize='auto-hq', **kwargs)[source]¶

compute_environments(from_which, xrange=None, yrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, dense=False, compress_opts=None, envs=None, **contract_boundary_opts)[source]¶

Compute the 1D boundary tensor networks describing the environments of rows and columns.

Parameters:

from_which ({'xmin', 'xmax', 'ymin', 'ymax'}) – Which boundary to compute the environments from.
xrange (tuple[int], optional) – The range of rows to compute the environments for.
yrange (tuple[int], optional) – The range of columns to compute the environments for.
max_bond (int, optional) – The maximum bond dimension of the environments.
cutoff (float, optional) – The cutoff for the singular values of the environments.
canonize (bool, optional) – Whether to canonicalize along each MPS environment before compressing.
mode ({'mps', 'projector', 'full-bond'}, optional) – Which contraction method to use for the environments.
layer_tags (str or iterable[str], optional) – If this 2D TN is multi-layered (e.g. a bra and a ket), and mode == 'mps', contract and compress each specified layer separately, for a cheaper contraction.
dense (bool, optional) – Whether to use dense tensors for the environments.
compress_opts (dict, optional) – Other options to pass to tensor_compress_bond().
envs (dict, optional) – An existing dictionary to store the environments in.
contract_boundary_opts – Other options to pass to contract_boundary_from().

Returns:

envs – A dictionary of the environments, with keys of the form (from_which, row_or_col_index).

Return type:

dict

compute_x_environments(max_bond=None, *, cutoff=1e-10, canonize=True, dense=False, mode='mps', layer_tags=None, compress_opts=None, envs=None, **contract_boundary_opts)[source]¶

Compute the 2 * self.Lx 1D boundary tensor networks describing the lower and upper environments of each row in this 2D tensor network, assumed to represent the norm.

The top or ‘xmax’ environment for row i will be a contraction of all rows i + 1, i + 2, ... etc:

 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●
╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲

The bottom or ‘xmin’ environment for row i will be a contraction of all rows i - 1, i - 2, ... etc:

╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱
 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●

Such that envs['xmax', i] & self.select(self.x_tag(i)) & envs['xmin', i] would look like:

 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●
╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲
o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o
╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱
 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●

And be (an approximation of) the norm centered around row i

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
dense (bool, optional) – If true, contract the boundary in as a single dense tensor.
mode ({'mps', 'full-bond'}, optional) – How to perform the boundary compression.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
compress_opts (None or dict, optional) – Supplied to compress_between().
envs (dict, optional) – Supply an existing dictionary to store the environments in.
contract_boundary_opts – Supplied to contract_boundary_from_xmin() and contract_boundary_from_xmax() .

Returns:

x_envs – The two environment tensor networks of row i will be stored in x_envs['xmin', i] and x_envs['xmax', i].

Return type:

dict[(str, int), TensorNetwork]

compute_y_environments(max_bond=None, *, cutoff=1e-10, canonize=True, dense=False, mode='mps', layer_tags=None, compress_opts=None, envs=None, **contract_boundary_opts)[source]¶

Compute the 2 * self.Ly 1D boundary tensor networks describing the left (‘ymin’) and right (‘ymax’) environments of each column in this 2D tensor network, assumed to represent the norm.

The left or ‘ymin’ environment for column j will be a contraction of all columns j - 1, j - 2, ... etc:

●<
┃
●<
┃
●<
┃
●<

The right or ‘ymax’ environment for row j will be a contraction of all rows j + 1, j + 2, ... etc:

>●
 ┃
>●
 ┃
>●
 ┃
>●

Such that envs['ymin', j] & self.select(self.y_tag(j)) & envs['ymax', j] would look like:

   ╱o
●< o| >●
┃  |o  ┃
●< o| >●
┃  |o  ┃
●< o| >●
┃  |o  ┃
●< o╱ >●

And be (an approximation of) the norm centered around column j

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
dense (bool, optional) – If true, contract the boundary in as a single dense tensor.
mode ({'mps', 'full-bond'}, optional) – How to perform the boundary compression.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
compress_opts (None or dict, optional) – Supplied to compress_between().
contract_boundary_opts – Supplied to contract_boundary_from_ymin() and contract_boundary_from_ymax() .

Returns:

y_envs – The two environment tensor networks of column j will be stored in y_envs['ymin', j] and y_envs['ymax', j].

Return type:

dict[(str, int), TensorNetwork]

_compute_plaquette_environments_x_first(x_bsz, y_bsz, max_bond=None, cutoff=1e-10, canonize=True, layer_tags=None, second_dense=None, x_envs=None, **compute_environment_opts)[source]¶

_compute_plaquette_environments_y_first(x_bsz, y_bsz, max_bond=None, cutoff=1e-10, canonize=True, layer_tags=None, second_dense=None, y_envs=None, **compute_environment_opts)[source]¶

compute_plaquette_environments(x_bsz=2, y_bsz=2, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, first_contract=None, second_dense=None, compress_opts=None, **compute_environment_opts)[source]¶

Compute all environments like:

second_dense=False   second_dense=True (& first_contract='columns')

  ●──●                  ╭───●───╮
 ╱│  │╲                 │  ╱ ╲  │
●─.  .─●    ┬           ●─ . . ─●    ┬
│      │    ┊ x_bsz     │       │    ┊ x_bsz
●─.  .─●    ┴           ●─ . . ─●    ┴
 ╲│  │╱                 │  ╲ ╱  │
  ●──●                  ╰───●───╯

  <-->                    <->
 y_bsz                   y_bsz

Use two boundary contractions sweeps.

Parameters:

x_bsz (int, optional) – The size of the plaquettes in the x-direction (number of rows).
y_bsz (int, optional) – The size of the plaquettes in the y-direction (number of columns).
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the boundary compression.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
first_contract ({None, 'x', 'y'}, optional) – The environments can either be generated with initial sweeps in the row (‘x’) or column (‘y’) direction. Generally it makes sense to perform this approximate step in whichever is smaller (the default).
second_dense (None or bool, optional) – Whether to perform the second set of contraction sweeps (in the rotated direction from whichever first_contract is) using a dense tensor or boundary method. By default this is only turned on if the bsz in the corresponding direction is 1.
compress_opts (None or dict, optional) – Supplied to compress_between().
compute_environment_opts – Supplied to compute_y_environments() or compute_x_environments() .

Returns:

The plaquette environments. The key is two tuples of ints, the startings coordinate of the plaquette being the first and the size of the plaquette being the second pair.

Return type:

dict[((int, int), (int, int)), TensorNetwork]

coarse_grain_hotrg(direction, max_bond=None, cutoff=1e-10, lazy=False, equalize_norms=False, optimize='auto-hq', compress_opts=None, inplace=False)[source]¶

Coarse grain this tensor network in direction using HOTRG. This inserts oblique projectors between tensor pairs and then optionally contracts them into new sites for form a lattice half the size.

Parameters:

direction ({'x', 'y'}) – The direction to coarse grain in.
max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
equalize_norms (bool, optional) – Whether to equalize the norms of the tensors in the coarse grained lattice.
optimize (str, optional) – The optimization method to use when contracting the coarse grained lattice, if lazy=False.
compress_opts (None or dict, optional) – Supplied to insert_compressor_between_regions().
inplace (bool, optional) – Whether to perform the coarse graining in place.

Returns:

The coarse grained tensor network, with size halved in direction.

Return type:

TensorNetwork2D

See also

contract_hotrg, TensorNetwork.insert_compressor_between_regions

contract_hotrg(max_bond=None, *, cutoff=1e-10, canonize=False, canonize_opts=None, sequence=('x', 'y'), max_separation=1, max_unfinished=1, lazy=False, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=False, inplace=False, **coarse_grain_opts)[source]¶

Contract this tensor network using the finite version of HOTRG. See https://arxiv.org/abs/1201.1144v4 and https://arxiv.org/abs/1905.02351 for the more optimal computaton of the projectors used here. The TN is contracted sequentially in sequence directions by inserting oblique projectors between plaquettes, and then optionally contracting these new effective sites. The algorithm stops when only one direction has a length larger than 2, and thus exact contraction can be used.

Parameters:

max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
canonize (bool, optional) – Whether to canonize all tensors before each contraction, via gauge_all().
canonize_opts (None or dict, optional) – Additional options to pass to gauge_all().
sequence (tuple of str, optional) – The directions to contract in. Default is to contract in all directions.
max_separation (int, optional) – The maximum distance between sides (i.e. length - 1) of the tensor network before that direction is considered finished.
max_unfinished (int, optional) – The maximum number of directions that can be unfinished (i.e. are still longer than max_separation + 1) before the coarse graining terminates.
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
equalize_norms (bool or float, optional) – Whether to equalize the norms of all tensors after each contraction, gathering the overall scaling coefficient, log10, in tn.exponent.
final_contract (bool, optional) – Whether to exactly contract the remaining tensor network after the coarse graining contractions.
final_contract_opts (None or dict, optional) – Options to pass to contract(), optimize defaults to 'auto-hq'.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the coarse graining in place.
coarse_grain_opts – Additional options to pass to coarse_grain_hotrg().

Returns:

The contracted tensor network, which will have no more than one direction of length > 2.

Return type:

TensorNetwork2D

See also

coarse_grain_hotrg, contract_ctmrg, TensorNetwork.insert_compressor_between_regions

contract_ctmrg(max_bond=None, *, cutoff=1e-10, canonize=False, canonize_opts=None, lazy=False, mode='projector', compress_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, max_separation=1, around=None, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=False, inplace=False, **contract_boundary_opts)[source]¶

Contract this 2D tensor network using the finite analog of the CTMRG algorithm - https://arxiv.org/abs/cond-mat/9507087. The TN is contracted sequentially in sequence directions by inserting oblique projectors between boundary pairs, and then optionally contracting these new effective sites. The algorithm stops when only one direction has a length larger than 2, and thus exact contraction can be used.

Parameters:

max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
canonize (bool, optional) – Whether to canonize the boundary tensors before each contraction, via gauge_all().
canonize_opts (None or dict, optional) – Additional options to pass to gauge_all().
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
mode (str, optional) – The method to perform the boundary contraction. Defaults to 'projector'.
compress_opts (None or dict, optional) – Other low level options to pass to insert_compressor_between_regions().
sequence (sequence of {'xmin', 'xmax', 'ymin', 'ymax'}, optional) – Which directions to cycle throught when performing the inwards contractions, i.e. from that direction. If around is specified you will likely need all of these! Default is to contract in all directions.
xmin (int, optional) – The initial bottom boundary row, defaults to 0.
xmax (int, optional) – The initial top boundary row, defaults to Lx - 1.
ymin (int, optional) – The initial left boundary column, defaults to 0.
ymax (int, optional) – The initial right boundary column, defaults to Ly - 1..
max_separation (int, optional) – If around is None, when any two sides become this far apart simply contract the remaining tensor network.
around (None or sequence of (int, int), optional) – If given, don’t contract the square of sites bounding these coordinates.
equalize_norms (bool or float, optional) – Whether to equalize the norms of the boundary tensors after each contraction, gathering the overall scaling coefficient, log10, in tn.exponent.
final_contract (bool, optional) – Whether to exactly contract the remaining tensor network after the boundary contraction.
final_contract_opts (None or dict, optional) – Options to pass to contract(), optimize defaults to 'auto-hq'.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the boundary contraction in place.
contract_boundary_opts – Additional options to pass to contract_boundary_from().

Returns:

Either the fully contracted scalar (if final_contract=True and around=None) or the partially contracted tensor network.

Return type:

scalar or TensorNetwork2D

See also

contract_boundary_from, contract_hotrg, TensorNetwork.insert_compressor_between_regions

quimb.tensor.tensor_2d.is_lone_coo(where)[source]¶: Check if where has been specified as a single coordinate pair.

quimb.tensor.tensor_2d.gate_string_split_(TG, where, string, original_ts, bonds_along, reindex_map, site_ix, info, **compress_opts)[source]¶

quimb.tensor.tensor_2d.gate_string_reduce_split_(TG, where, string, original_ts, bonds_along, reindex_map, site_ix, info, **compress_opts)[source]¶

class quimb.tensor.tensor_2d.TensorNetwork2DVector(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: TensorNetwork2D, quimb.tensor.tensor_arbgeom.TensorNetworkGenVector

Mixin class for a 2D square lattice vector TN, i.e. one with a single physical index per site.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly', '_site_ind_id')¶

reindex_sites_[source]¶

gate_[source]¶

normalize_[source]¶

site_ind(i, j=None)[source]¶: Return the physical index of site (i, j).

reindex_sites(new_id, where=None, inplace=False)[source]¶

Modify the site indices for all or some tensors in this vector tensor network (without changing the site_ind_id).

Parameters:

new_id (str) – A string with a format placeholder to accept a site, e.g. “ket{}”.
where (None or sequence) – Which sites to update the index labels on. If None (default) all sites.
inplace (bool) – Whether to reindex in place.

phys_dim(i=None, j=None)[source]¶: Get the size of the physical indices / a specific physical index.

gate(G, where, contract=False, tags=None, propagate_tags='sites', inplace=False, info=None, long_range_use_swaps=False, long_range_path_sequence=None, **compress_opts)[source]¶

Apply the dense gate G, maintaining the physical indices of this 2D vector tensor network.

Parameters:

G (array_like) – The gate array to apply, should match or be factorable into the shape (phys_dim,) * (2 * len(where)).
where (sequence of tuple[int, int] or tuple[int, int]) – Which site coordinates to apply the gate to.
contract ({'reduce-split', 'split', False, True}, optional) –
How to contract the gate into the 2D tensor network:
- False: gate is added to network and nothing is contracted, tensor network structure is thus not maintained.
- True: gate is contracted with all tensors involved, tensor network structure is thus only maintained if gate acts on a single site only.
- ’split’: contract all involved tensors then split the result back into two.
- ’reduce-split’: factor the two physical indices into ‘R-factors’ using QR decompositions on the original site tensors, then contract the gate, split it and reabsorb each side. Much cheaper than 'split'.
The final two methods are relevant for two site gates only, for single site gates they use the contract=True option which also maintains the structure of the TN. See below for a pictorial description of each method.
tags (str or sequence of str, optional) – Tags to add to the new gate tensor.
propagate_tags ({'sites', 'register', True, False}, optional) –
If contract==False, which tags to propagate to the new gate tensor from the tensors it was applied to:
- If 'sites', then only propagate tags matching e.g. ‘I{},{}’ and ignore all others. I.e. assuming unitary gates just propagate the causal lightcone.
- If 'register', then only propagate tags matching the sites of where this gate was actually applied. I.e. ignore the lightcone, just keep track of which ‘registers’ the gate was applied to.
- If False, propagate nothing.
- If True, propagate all tags.
inplace (bool, optional) – Whether to perform the gate operation inplace on the tensor network or not.
info (None or dict, optional) – Used to store extra optional information such as the singular values if not absorbed.
compress_opts – Supplied to tensor_split() for any contract methods that involve splitting. Ignored otherwise.

Returns:

G_psi – The new 2D vector TN like IIIGII @ psi etc.

Return type:

TensorNetwork2DVector

Notes

The contract options look like the following (for two site gates).

contract=False:

  │   │
  GGGGG
  │╱  │╱
──●───●──
 ╱   ╱

contract=True:

  │╱  │╱
──GGGGG──
 ╱   ╱

contract='split':

  │╱  │╱          │╱  │╱
──GGGGG──  ==>  ──G┄┄┄G──
 ╱   ╱           ╱   ╱
 <SVD>

contract='reduce-split':

   │   │             │ │
   GGGGG             GGG               │ │
   │╱  │╱   ==>     ╱│ │  ╱   ==>     ╱│ │  ╱          │╱  │╱
 ──●───●──       ──>─●─●─<──       ──>─GGG─<──  ==>  ──G┄┄┄G──
  ╱   ╱           ╱     ╱           ╱     ╱           ╱   ╱
<QR> <LQ>                            <SVD>

For one site gates when one of the ‘split’ methods is supplied contract=True is assumed.

compute_norm(layer_tags=('KET', 'BRA'), **contract_opts)[source]¶: Compute the norm of this vector via boundary contraction.

compute_local_expectation(terms, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=('KET', 'BRA'), normalized=False, autogroup=True, contract_optimize='auto-hq', return_all=False, plaquette_envs=None, plaquette_map=None, **plaquette_env_options)[source]¶

Compute the sum of many local expecations by essentially forming the reduced density matrix of all required plaquettes. If you supply normalized=True each expecation is locally normalized, which a) is usually more accurate and b) doesn’t require a separate normalization boundary contraction.

Parameters:

terms (dict[tuple[tuple[int], array]) – A dictionary mapping site coordinates to raw operators, which will be supplied to gate(). The keys should either be a single coordinate - (i, j) - describing a single site operator, or a pair of coordinates - ((i_a, j_a), (i_b, j_b)) describing a two site operator.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or sequence of str, optional) – If given, perform a multilayer contraction, contracting the inner sites in each layer into the boundary individually.
normalized (bool, optional) – If True, normalize the value of each local expectation by the local norm: $langle O_i rangle = Tr[rho_p O_i] / Tr[rho_p]$.
autogroup (bool, optional) – If True (the default), group terms into horizontal and vertical sets to be computed separately (usually more efficient) if possible.
contract_optimize (str, optional) – Contraction path finder to use for contracting the local plaquette expectation (and optionally normalization).
return_all (bool, optional) – Whether to the return all the values individually as a dictionary of coordinates to tuple[local_expectation, local_norm].
plaquette_envs (None or dict, optional) – Supply precomputed plaquette environments.
plaquette_map (None, dict, optional) – Supply the mapping of which plaquettes (denoted by ((x0, y0), (dx, dy))) to use for which coordinates, it will be calculated automatically otherwise.
plaquette_env_options – Supplied to compute_plaquette_environments() to generate the plaquette environments, equivalent to approximately performing the partial trace.

Return type:

scalar or dict

normalize(max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=('KET', 'BRA'), balance_bonds=False, equalize_norms=False, inplace=False, **contract_boundary_opts)[source]¶

Normalize this PEPS.

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or sequence of str, optional) – If given, perform a multilayer contraction, contracting the inner sites in each layer into the boundary individually.
balance_bonds (bool, optional) – Whether to balance the bonds after normalization, a form of conditioning.
equalize_norms (bool, optional) – Whether to set all the tensor norms to the same value after normalization, another form of conditioning.
inplace (bool, optional) – Whether to perform the normalization inplace or not.
contract_boundary_opts – Supplied to contract_boundary(), by default, two layer contraction will be used.

class quimb.tensor.tensor_2d.TensorNetwork2DOperator(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: TensorNetwork2D, quimb.tensor.tensor_arbgeom.TensorNetworkGenOperator

Mixin class for a 2D square lattice TN operator, i.e. one with both ‘upper’ and ‘lower’ site (physical) indices.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly', '_upper_ind_id', '_lower_ind_id')¶

reindex_lower_sites_[source]¶

reindex_upper_sites_[source]¶

reindex_lower_sites(new_id, where=None, inplace=False)[source]¶

Update the lower site index labels to a new string specifier.

Parameters:

new_id (str) – A string with a format placeholder to accept an int, e.g. "ket{},{}".
where (None or slice) – Which sites to update the index labels on. If None (default) all sites.
inplace (bool) – Whether to reindex in place.

reindex_upper_sites(new_id, where=None, inplace=False)[source]¶

Update the upper site index labels to a new string specifier.

Parameters:

new_id (str) – A string with a format placeholder to accept an int, e.g. "ket{},{}".
where (None or slice) – Which sites to update the index labels on. If None (default) all sites.
inplace (bool) – Whether to reindex in place.

lower_ind(i, j=None)[source]¶: Get the lower index for a given site.

upper_ind(i, j=None)[source]¶: Get the upper index for a given site.

phys_dim(i=0, j=0, which='upper')[source]¶: Get a physical index size of this 2D operator.

class quimb.tensor.tensor_2d.TensorNetwork2DFlat(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: TensorNetwork2D

Mixin class for a 2D square lattice tensor network with a single tensor per site, for example, both PEPS and PEPOs.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly')¶

bond(coo1, coo2)[source]¶: Get the name of the index defining the bond between sites at coo1 and coo2.

bond_size(coo1, coo2)[source]¶: Return the (combined) size of the bond(s) between sites at coo1 and coo2.

expand_bond_dimension(new_bond_dim, inplace=True, bra=None, rand_strength=0.0)[source]¶

Increase the bond dimension of this flat, 2D, tensor network, padding the tensor data with either zeros or random entries.

Parameters:

new_bond_dim (int) – The new dimension. If smaller or equal to the current bond dimension nothing will happend.
inplace (bool, optional) – Whether to expand in place (the default), or return a new TN.
bra (TensorNetwork2DFlat, optional) – Expand this TN with the same data also, assuming it to be the conjugate, bra, TN.
rand_strength (float, optional) – If greater than zero, pad the data arrays with gaussian noise of this strength.

Returns:

Return type:

TensorNetwork2DFlat

compress(max_bond=None, cutoff=1e-10, equalize_norms=False, row_sweep='right', col_sweep='up', **compress_opts)[source]¶

Compress all bonds in this flat 2D tensor network.

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
compress_opts (None or dict, optional) – Supplied to compress_between().

class quimb.tensor.tensor_2d.PEPS(arrays, *, shape='urdlp', tags=None, site_ind_id='k{},{}', site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', **tn_opts)[source]¶

Bases: TensorNetwork2DVector, TensorNetwork2DFlat

Projected Entangled Pair States object (2D):

             ...
 │    │    │    │    │    │
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │    │    │    │    │    │
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │    │    │    │    │    │   ...
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │    │    │    │    │    │
 ●────●────●────●────●────●──
╱    ╱    ╱    ╱    ╱    ╱

Parameters:

arrays (sequence of sequence of array_like) – The core tensor data arrays.
shape (str, optional) – Which order the dimensions of the arrays are stored in, the default 'urdlp' stands for (‘up’, ‘right’, ‘down’, ‘left’, ‘physical’). Arrays on the edge of lattice are assumed to be missing the corresponding dimension.
tags (set[str], optional) – Extra global tags to add to the tensor network.
site_ind_id (str, optional) – String specifier for naming convention of site indices.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row (‘x’) tags.
y_tag_id (str, optional) – String specifier for naming convention of column (‘y’) tags.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly', '_site_ind_id')¶

add_PEPS_[source]¶

classmethod from_fill_fn(fill_fn, Lx, Ly, bond_dim, phys_dim=2, cyclic=False, shape='urdlp', **peps_opts)[source]¶

Create a 2D PEPS from a filling function with signature fill_fn(shape).

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
phys_dim (int, optional) – The physical index dimension.
cyclic (bool or tuple[bool, bool], optional) – Whether the lattice is cyclic in the x and y directions.
shape (str, optional) – How to layout the indices of the tensors, the default is (up, right, down, left, phys) == 'urdlbk'. This is the order of the shape supplied to the filling function.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

PEPS

classmethod empty(Lx, Ly, bond_dim, phys_dim=2, like='numpy', **peps_opts)[source]¶

Create an empty 2D PEPS.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

PEPS

See also

PEPS.from_fill_fn

classmethod ones(Lx, Ly, bond_dim, phys_dim=2, like='numpy', **peps_opts)[source]¶

Create a 2D PEPS whose tensors are filled with ones.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

PEPS

See also

PEPS.from_fill_fn

classmethod rand(Lx, Ly, bond_dim, phys_dim=2, dist='normal', loc=0.0, dtype='float64', seed=None, **peps_opts)[source]¶

Create a random (un-normalized) PEPS.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
dist ({'normal', 'uniform', 'rademacher', 'exp'}, optional) – Type of random number to generate, defaults to ‘normal’.
loc (float, optional) – An additive offset to add to the random numbers.
dtype (dtype, optional) – The dtype to create the arrays with, default is real double.
seed (int, optional) – A random seed.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

PEPS

See also

PEPS.from_fill_fn

add_PEPS(other, inplace=False)[source]¶

show()[source]¶: Print a unicode schematic of this PEPS and its bond dimensions.

class quimb.tensor.tensor_2d.PEPO(arrays, *, shape='urdlbk', tags=None, upper_ind_id='k{},{}', lower_ind_id='b{},{}', site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', **tn_opts)[source]¶

Bases: TensorNetwork2DOperator, TensorNetwork2DFlat

Projected Entangled Pair Operator object:

             ...
 │╱   │╱   │╱   │╱   │╱   │╱
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │╱   │╱   │╱   │╱   │╱   │╱
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │╱   │╱   │╱   │╱   │╱   │╱   ...
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │╱   │╱   │╱   │╱   │╱   │╱
 ●────●────●────●────●────●──
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Parameters:

arrays (sequence of sequence of array) – The core tensor data arrays.
shape (str, optional) – Which order the dimensions of the arrays are stored in, the default 'urdlbk' stands for (‘up’, ‘right’, ‘down’, ‘left’, ‘bra’, ‘ket’). Arrays on the edge of lattice are assumed to be missing the corresponding dimension.
tags (set[str], optional) – Extra global tags to add to the tensor network.
upper_ind_id (str, optional) – String specifier for naming convention of upper site indices.
lower_ind_id (str, optional) – String specifier for naming convention of lower site indices.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row (‘x’) tags.
y_tag_id (str, optional) – String specifier for naming convention of column (‘y’) tags.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly', '_upper_ind_id', '_lower_ind_id')¶

rand_herm¶

add_PEPO_[source]¶

classmethod from_fill_fn(fill_fn, Lx, Ly, bond_dim, phys_dim=2, cyclic=False, shape='urdlbk', **pepo_opts)[source]¶

Create a PEPO and fill the tensor entries with a supplied function matching signature fill_fn(shape) -> array.

Parameters:

fill_fn (callable) – A function that takes a shape tuple and returns a data array.
Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
phys_dim (int, optional) – The physical indices dimension.
cyclic (bool or tuple[bool, bool], optional) – Whether the lattice is cyclic in the x and y directions.
shape (str, optional) – How to layout the indices of the tensors, the default is (up, right, down, left bra, ket) == 'urdlbk'.
pepo_opts – Supplied to PEPO.

classmethod rand(Lx, Ly, bond_dim, phys_dim=2, herm=False, dist='normal', loc=0.0, dtype='float64', seed=None, **pepo_opts)[source]¶

Create a random PEPO.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
herm (bool, optional) – Whether to symmetrize the tensors across the physical bonds to make the overall operator hermitian.
dtype (dtype, optional) – The dtype to create the arrays with, default is real double.
seed (int, optional) – A random seed.
pepo_opts – Supplied to PEPO.

Returns:

Return type:

PEPO

classmethod zeros(Lx, Ly, bond_dim, phys_dim=2, dtype='float64', backend='numpy', **pepo_opts)[source]¶

Create a PEPO with all zero entries.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
dtype (dtype, optional) – The dtype to create the arrays with, default is real double.
backend (str, optional) – Which backend to use, default is 'numpy'.
pepo_opts – Supplied to PEPO.

add_PEPO(other, inplace=False)[source]¶

_apply_peps(other, compress=False, contract=True, **compress_opts)[source]¶

apply(other, compress=False, **compress_opts)[source]¶

Act with this PEPO on other, returning a new TN like other with the same outer indices.

Parameters:

other (PEPS) – The TN to act on.
compress (bool, optional) – Whether to compress the resulting TN.
compress_opts – Supplied to compress().

Return type:

TensorNetwork2DFlat

show()[source]¶: Print a unicode schematic of this PEPO and its bond dimensions.

quimb.tensor.tensor_2d.show_2d(tn_2d, show_lower=False, show_upper=False)[source]¶: Base function for printing a unicode schematic of flat 2D TNs.

quimb.tensor.tensor_2d.calc_plaquette_sizes(coo_groups, autogroup=True)[source]¶

Find a sequence of plaquette blocksizes that will cover all the terms (coordinate pairs) in pairs.

Parameters:

coo_groups (sequence of tuple[tuple[int]] or tuple[int]) – The sequence of 2D coordinates pairs describing terms. Each should either be a single 2D coordinate or a sequence of 2D coordinates.
autogroup (bool, optional) – Whether to return the minimal sequence of blocksizes that will cover all terms or merge them into a single ((x_bsz, y_bsz),).

Returns:

bszs – Pairs of blocksizes.

Return type:

tuple[tuple[int]]

Examples

Some nearest neighbour interactions:

>>> H2 = {None: qu.ham_heis(2)}
>>> ham = qtn.LocalHam2D(10, 10, H2)
>>> calc_plaquette_sizes(ham.terms.keys())
((1, 2), (2, 1))

>>> calc_plaquette_sizes(ham.terms.keys(), autogroup=False)
((2, 2),)

If we add any next nearest neighbour interaction then we are going to need the (2, 2) blocksize in any case:

>>> H2[(1, 1), (2, 2)] = 0.5 * qu.ham_heis(2)
>>> ham = qtn.LocalHam2D(10, 10, H2)
>>> calc_plaquette_sizes(ham.terms.keys())
((2, 2),)

If we add longer range interactions (non-diagonal next nearest) we again can benefit from multiple plaquette blocksizes:

>>> H2[(1, 1), (1, 3)] = 0.25 * qu.ham_heis(2)
>>> H2[(1, 1), (3, 1)] = 0.25 * qu.ham_heis(2)
>>> ham = qtn.LocalHam2D(10, 10, H2)
>>> calc_plaquette_sizes(ham.terms.keys())
((1, 3), (2, 2), (3, 1))

Or choose the plaquette blocksize that covers all terms:

>>> calc_plaquette_sizes(ham.terms.keys(), autogroup=False)
((3, 3),)

quimb.tensor.tensor_2d.plaquette_to_sites(p)[source]¶

Turn a plaquette ((i0, j0), (di, dj)) into the sites it contains.

Examples

>>> plaquette_to_sites([(3, 4), (2, 2)])
((3, 4), (3, 5), (4, 4), (4, 5))

quimb.tensor.tensor_2d.calc_plaquette_map(plaquettes)[source]¶

Generate a dictionary of all the coordinate pairs in plaquettes mapped to the ‘best’ (smallest) rectangular plaquette that contains them.

Examples

Consider 4 sites, with one 2x2 plaquette and two vertical (2x1) and horizontal (1x2) plaquettes each:

>>> plaquettes = [
...     # 2x2 plaquette covering all sites
...     ((0, 0), (2, 2)),
...     # horizontal plaquettes
...     ((0, 0), (1, 2)),
...     ((1, 0), (1, 2)),
...     # vertical plaquettes
...     ((0, 0), (2, 1)),
...     ((0, 1), (2, 1)),
... ]

>>> calc_plaquette_map(plaquettes)
{((0, 0), (0, 1)): ((0, 0), (1, 2)),
 ((0, 0), (1, 0)): ((0, 0), (2, 1)),
 ((0, 0), (1, 1)): ((0, 0), (2, 2)),
 ((0, 1), (1, 0)): ((0, 0), (2, 2)),
 ((0, 1), (1, 1)): ((0, 1), (2, 1)),
 ((1, 0), (1, 1)): ((1, 0), (1, 2))}

Now every of the size coordinate pairs is mapped to one of the plaquettes, but to the smallest one that contains it. So the 2x2 plaquette (specified by ((0, 0), (2, 2))) would only used for diagonal terms here.

quimb.tensor.tensor_2d.gen_long_range_path(ij_a, ij_b, sequence=None)[source]¶

Generate a string of coordinates, in order, from ij_a to ij_b.

Parameters:

ij_a ((int, int)) – Coordinate of site ‘a’.
ij_b ((int, int)) – Coordinate of site ‘b’.
sequence (None, iterable of {'v', 'h'}, or 'random', optional) – What order to cycle through and try and perform moves in, ‘v’, ‘h’ standing for move vertically and horizontally respectively. The default is ('v', 'h').

Returns:

The path, each element is a single coordinate.

Return type:

generator[tuple[int]]

quimb.tensor.tensor_2d.gen_long_range_swap_path(ij_a, ij_b, sequence=None)[source]¶

Generate the coordinates or a series of swaps that would bring ij_a and ij_b together.

Parameters:

ij_a ((int, int)) – Coordinate of site ‘a’.
ij_b ((int, int)) – Coordinate of site ‘b’.
sequence (None, it of {'av', 'bv', 'ah', 'bh'}, or 'random', optional) – What order to cycle through and try and perform moves in, ‘av’, ‘bv’, ‘ah’, ‘bh’ standing for move ‘a’ vertically, ‘b’ vertically, ‘a’ horizontally’, and ‘b’ horizontally respectively. The default is ('av', 'bv', 'ah', 'bh').

Returns:

The path, each element is two coordinates to swap.

Return type:

generator[tuple[tuple[int]]]

quimb.tensor.tensor_2d.swap_path_to_long_range_path(swap_path, ij_a)[source]¶: Generates the ordered long-range path - a sequence of coordinates - from a (long-range) swap path - a sequence of coordinate pairs.

quimb.tensor.tensor_2d.get_swap(dp, dtype, backend)[source]¶

quimb.tensor.tensor_2d¶

Module Contents¶

Classes¶

Functions¶

Attributes¶

`quimb.tensor.tensor_2d`¶