# mpnum reference¶

mpnum: A matrix-product-representation library for Python

## Basic matrix product array functionality¶

Module containing routines for dealing with general matrix product arrays.

References:

class mpnum.mparray.MPArray(ltens)

Bases: object

Efficient representation of a general N-partite array A in matrix product form with open boundary conditions:

(1)$A_{i_1, \ldots, i_N} = A^{[1]}_{i_1} \ldots A^{[N]}_{i_N}$

where the $$A^{[k]}$$ are local tensors (with N legs). The matrix products in (1) are taken with respect to the left and right leg and the multi-index $$i_k$$ corresponds to the physical legs. Open boundary conditions imply that $$A^{[1]}$$ is 1-by-something and $$A^{[N]}$$ is something-by-1.

By convention, the 0th and last dimension of the local tensors are reserved for the auxillary legs.

Todo

As it is now, e.g. MPArray.__imul__() modifies items from self._ltens. This requires e.g. outer() to take copies of the local tensors. The data model seems to be that an MPArray instance owns its local tensors and everyone else, including each new MPArray instance, must take copies. Is this correct?

Todo

If we enable all special members (e.g. __len__) to be shown, we get things like __dict__ with very long contents. Therefore, special members are hidden at the moment, but we should show the interesting one.

__init__(ltens)
Parameters: ltens (LocalTensors) – local tensors as instance of mpstruct.LocalTensors
T

Transpose (=reverse order of) physical legs

_adapt_to(target, num_sweeps, var_sites)

Iteratively minimize the l2 distance between self and target. This is especially important for variational compression, where self is the initial guess and target the MPA to be compressed.

Parameters: target – MPS to compress; i.e. MPA with only one physical leg per site

Other parameters and references: See MPArray.compress().

_compress_svd(bdim=None, relerr=None, direction=None, normalize=True, svdfunc=<function truncated_svd>)

Compress self using SVD [Sch11, Sec. 4.5.1]

Parameters: See MPArray.compress().

_compress_svd_l(bdim, relerr, svdfunc)

Compresses the MPA in place from right to left using SVD; yields a right-canonical state

See MPArray.compress() for parameters

_compress_svd_r(bdim, relerr, svdfunc)

Compresses the MPA in place from left to right using SVD; yields a left-canonical state

See MPArray.compress() for parameters

_compression_var(startmpa=None, bdim=None, randstate=<module 'numpy.random' from '/usr/lib/python3/dist-packages/numpy/random/__init__.py'>, num_sweeps=5, var_sites=1)

Return a compression from variational compression [Sch11, Sec. 4.5.2]

Parameters and return value: See MPArray.compression().

_lnormalize(to_site)

Left-normalizes all local tensors _ltens[:to_site] in place

Parameters: to_site – Index of the site up to which normalization is to be performed
_rnormalize(to_site)

Right-normalizes all local tensors _ltens[to_site:] in place

Parameters: to_site – Index of the site up to which normalization is to be performed
adj()

bdim

Largest bond dimension across the chain

bdims

Tuple of bond dimensions

bleg2pleg(pos)

Transforms the bond leg between site pos and pos + 1 into physical legs at those sites. The new leg will be the rightmost one at site pos and the leftmost one at site pos + 1. The new bond dimension is 1.

Also see pleg2bleg().

Parameters: pos – Number of the bond to perform the transformation read-only MPA with transformed bond
compress(method='svd', **kwargs)

Compress self, modifying it in-place.

Let $$\vert u \rangle$$ the original vector and let $$\vert c \rangle$$ the compressed vector. The compressions we return have the property (cf. [Sch11, Sec. 4.5.2])

$\langle u \vert c \rangle = \langle c \vert c \rangle \in (0, \infty).$

It is a useful property because it ensures

$\begin{split}\min_{\phi \in \mathbb R} \| u - r e^{i \phi} c \| &= \| u - r c \|, \quad r > 0, \\ \min_{\mu \in \mathbb C} \| u - \mu c \| &= \| u - c \|\end{split}$

for the vector 2-norm. Users of this function can compute norm differences between u and a normalized c via

$\| u - r c \|^2 = \| u \|^2 + r (r - 2) \langle u \vert c \rangle, \quad r \ge 0.$

In the special case of $$\|u\| = 1$$ and $$c_0 = c/\| c \|$$ (pure quantum states as MPS), we obtain

$\| u - c_0 \|^2 = 2(1 - \sqrt{\langle u \vert c \rangle})$
Returns: Inner product $$\langle u \vert c \rangle \in (0, \infty)$$ of the original u and its compression c. method – ‘svd’ or ‘var’

Parameters for ‘svd’:

Parameters: bdim – Maximal bond dimension of the result. Default None. relerr – Maximal fraction of discarded singular values. Default 0. If both bdim and relerr are given, the smaller resulting bond dimension is used. direction – right (sweep from left to right), left (inverse) or None (choose depending on normalization). Default None. normalize – SVD compression works best when the MPA is brought into full left-/right-cannonical form first. This variable determines whether cannonical form is enforced before compression (default: True)

Parameters for ‘var’:

Parameters: startmpa – Start vector, also fixes the bond dimension of the result. Default: Random, with same norm as self. bdim – Maximal bond dimension for the result. Either startmpa or bdim is required. randstate – numpy.random.RandomState instance used for random start vector. Default: numpy.random. num_sweeps – Maximum number of sweeps to do. Default 5. var_sites – Number of sites to modify simultaneausly. Default 1.

Increasing var_sites makes it less likely to get stuck in a local minimum.

References:

• ‘svd’: Singular value truncation, [Sch11, Sec. 4.5.1]
• ‘var’: Variational compression, [Sch11, Sec. 4.5.2]
compression(method='svd', **kwargs)

Return a compression of self. Does not modify self.

Parameters: See MPArray.compress().

Returns: (compressed_mpa, iprod) where iprod is the inner product returned by MPArray.compress().
conj()

Complex conjugate

copy()

Makes a deep copy of the MPA

dims

Tuple of shapes for the local tensors

dtype

Returns the dtype that should be returned by to_array

dump(target)

Serializes MPArray to h5py.Group. Recover using MPArray.load().

Parameters: target – h5py.Group the instance should be saved to or path to h5 file (it’s then serialized to /)
classmethod from_array(array, plegs=None, has_bond=False)

Create MPA from array in local form.

See mpnum._tools.global_to_local() for global vs. local form.

Computes the (exact) representation of array as MPA with open boundary conditions, i.e. bond dimension 1 at the boundary. This is done by factoring the off the left and the “physical” legs from the rest of the tensor by a QR decomposition and working its way through the tensor from the left. This yields a left-canonical representation of array. [Sch11, Sec. 4.3.1]

The result is a chain of local tensors with plegs physical legs at each location and has array.ndim // plegs number of sites.

has_bond = True allows to treat a part of the linear chain of an MPA as MPA as well. The bond dimension on the left and right can be different from one and different from each other in that case. This is useful to apply SVD compression only to part of an MPA. It is used in linalg._mineig_minimize_locally().

Parameters: array (np.ndarray) – Array representation with global structure array[(i1), ..., (iN)], i.e. the legs which are factorized into the same factor are already adjacent. (For me details see _tools.global_to_local()) plegs – Number of physical legs per site (default array.ndim) or iterable over number of physical legs has_bond (bool) – True if array already has indices for the left and right bond
classmethod from_array_global(array, plegs=None, has_bond=False)

Create MPA from array in global form.

See mpnum._tools.global_to_local() for global vs. local form.

Parameters and return value: See from_array(). has_bond=True is not supported yet.

classmethod from_kron(factors)

Returns the (exact) representation of an n-fold Kronecker (tensor) product as MPA with bond dimensions 1 and n sites.

Parameters: factors – A list of arrays with arbitrary number of physical legs The kronecker product of the factors as MPA
get_phys(pind, astype=None)

Fix values for first physical leg

Parameters: pind – Length len(self) sequence of index values for first physical leg at each site type(self) object
group_sites(sites_per_group)

Group several MPA sites into one site.

The resulting MPA has length len(self) // sites_per_group and sites_per_group * self.plegs[i] physical legs on site i. The physical legs on each sites are in local form.

Parameters: sites_per_group (int) – Number of sites to be grouped into one An MPA with sites_per_group fewer sites and more plegs
legs

Tuple of total number of legs per site

classmethod load(source)

Deserializes MPArray from h5py.Group. Serialize using MPArray.dump().

Parameters: target – h5py.Group containing serialized MPArray or path to a single h5 File containing serialized MPArray under /
lt
normal_form

Tensors which are currently in left/right-canonical form.

normalize(left=None, right=None)

Brings the MPA to canonical form in place [Sch11, Sec. 4.4]

Note that we do not support full left- or right-normalization. The right- (left- resp.) most local tensor is not normalized since this can be done by simply calculating its norm (instead of using SVD).

The following values for left and right will be needed most frequently:

Left-/Right- normalize: Do Nothing To normalize maximally
left None 'afull', len(self) - 1
right None 'afull', 1

'afull' is short for “almost full” (we do not support normalizing the outermost sites).

Arbitrary integer values of left and right have the following meaning:

• self[:left] will be left-normalized
• self[right:] will be right-normalized

In accordance with the last table, the special values None and 'afull' will be replaced by the following integers:

 None 'afull' left 0 len(self) - 1 right len(self) 1

Exceptions raised:

• Integer argument too large or small: IndexError
• Matrix would be both left- and right-normalized: ValueError
pad_bdim(bdim=None, force_bdim=False)

Increase bond dimension by padding with zeros

This function is useful to prepare initial states for variational compression. E.g. for a five-qubit pure state with bond dimensions (2, 2, 4, 2) it is desirable to increase the bond dimensions to (2, 4, 4, 2) before using it as an initial state for variational compression.

Parameters: bdim – Increase bond dimension to this value, use self.bdim if None force_bdim – Use full bond dimension even at the beginning and end of the MPS (generally not useful) MPA representation of the same array with increased bond dimension
paxis_iter(axes=0)

Returns an iterator yielding Sub-MPArrays of self by iterating over the specified physical axes.

Example: If self represents a bipartite (i.e. length 2) array with 2 physical dimensions on each site A[(k,l), (m,n)], self.paxis_iter(0) is equivalent to:

(A[(k, :), (m, :)] for m in range(...) for k in range(...))


FIXME The previous code is not highlighted because A[(k, :)] is invalid syntax. Example of working highlighting:

(x**2 for x in range(...))

Parameters: axes – Iterable or int specifiying the physical axes to iterate over (default 0 for each site) Iterator over MPArray
pdims

Tuple of physical dimensions

pleg2bleg(pos)

Performs the inverse operation to bleg2pleg().

Parameters: pos – Number of the bond to perform the transformation read-only MPA with transformed bond
plegs

Tuple of number of physical legs per site

ravel()

Flatten the MPA to an MPS, shortcut for self.reshape((-1,))

reshape(newshapes)

Reshape physical legs in place.

Use self.pdims to obtain the shapes of the physical legs.

Parameters: newshapes – A single new shape or a list of new shapes. Alternatively, you can pass ‘prune’ to get rid of all physical legs of size 1. Reshaped MPA
reverse()
singularvals()

Return singular values for all bipartitions

Returns: Iterate over bipartitions with 1, 2, ... len(self) - 1 sites on the left hand side. Yields a np.ndarray containing singular values for each bipartition.

Note

May decrease the bond dimension (without changing the represented tensor).

size

Returns the number of floating point numbers used to represent the MPArray

>>> from .factory import zero
>>> zero(sites=3, ldim=4, bdim=3).dims
((1, 4, 3), (3, 4, 3), (3, 4, 1))
>>> zero(sites=3, ldim=4, bdim=3).size
60

split(pos)

Splits the MPA into two by transforming the bond legs into physical legs

Parameters: pos – Number of the bond to perform the transformation (mpa_left, mpa_right)
split_sites(sites_per_group)

Split MPA sites into several sites.

The resulting MPA has length len(self) * sites_per_group and self.plegs[i] // sites_per_group physical legs on site i. The physical legs on before splitting must be in local form.

Parameters: sites_per_group (int) – Split each site in that many sites An mpa with sites_per_group more sites and fewer plegs
sum(axes=None)

Element-wise sum over physical legs

Parameters: axes – Physical legs to sum over

axes can have the following values:

• Sequence of length zero: Sum over nothing
• Sequence of (sequences or None): axes[i] specifies the physical legs to sum over at site i; None sums over all physical legs at a site
• Sequence of integers: axes specifies the physical legs to sum over at each site
• Single integer: Sum over physical leg axes at each site
• None: Sum over all physical legs at each site

To not sum over any axes at a certain site, specify the empty sequence for that site.

to_array()

Return MPA as array in local form.

See mpnum._tools.global_to_local() for global vs. local form.

Returns: ndarray of shape sum(self.pdims, ())

Note

Full arrays can require much more memory than MPAs. (That’s why you are using MPAs, right?)

to_array_global()

Return MPA as array in global form.

See mpnum._tools.global_to_local() for global vs. local form.

Returns: ndarray of shape sum(zip(*self.pdims, ()))

See to_array() for more details.

transpose(axes=None)

Transpose physical legs

Parameters: axes – New order of the physical axes (default None = reverse the order).
>>> from .factory import random_mpa
>>> mpa = random_mpa(2, (2, 3, 4), 2)
>>> mpa.pdims
((2, 3, 4), (2, 3, 4))
>>> mpa.transpose((2, 0, 1)).pdims
((4, 2, 3), (4, 2, 3))

mpnum.mparray.dot(mpa1, mpa2, axes=(-1, 0), astype=None)

Compute the matrix product representation of a.b over the given (physical) axes. [Sch11, Sec. 4.2]

Parameters: mpa2 (mpa1,) – Factors as MPArrays axes – Tuple (ax1, ax2) where ax1 (ax2) is a single physical leg number or sequence of physical leg numbers referring to mpa1 (mpa2). The first (second, etc) entries of ax1 and ax2 will be contracted. Very similar to the axes argument for np.tensordot(), but the default value is different. astype – Return type. If None, use the type of mpa1 Dot product of the physical arrays
mpnum.mparray.inject(mpa, pos, num=None, inject_ten=None)

Interleaved outer product of an MPA and a bond dimension 1 MPA

Return the outer product between mpa and num copies of the local tensor inject_ten, but place the copies of inject_ten before site pos inside or outside mpa. You can also supply num = None and a sequence of local tensors. All legs of the local tensors are interpreted as physical legs. Placing the local tensors at the beginning or end of mpa using pos = 0 or pos = len(mpa) is also supported, but outer() is preferred for that as it is a much simpler function.

If inject_ten is omitted, use a square identity matrix of size mpa.pdims[pos][0]. If pos = len(mpa), mpa.pdims[pos - 1][0] will be used for the size of the matrix.

Parameters: mpa – An MPA. pos – Inject sites into the MPA before site pos. num – Inject num copies. Can be None; in this case inject_ten must be a sequence of values. inject_ten – Physical tensor to inject (if omitted, an identity matrix will be used; cf. above) The outer product

pos can also be a sequence of positions. In this case, num and inject_ten must be either sequences or None, where None is interpreted as len(pos) * [None]. As above, if num[i] is None, then inject_ten[i] must be a sequence of values.

mpnum.mparray.inner(mpa1, mpa2)

Compute the inner product <mpa1|mpa2>. Both have to have the same physical dimensions. If these represent a MPS, inner(...) corresponds to the canoncial Hilbert space scalar product, if these represent a MPO, inner(...) corresponds to the Frobenius scalar product (with Hermitian conjugation in the first argument)

Parameters: mpa1 – MPArray with same number of physical legs on each site mpa2 – MPArray with same physical shape as mpa1
mpnum.mparray.local_sum(mpas, embed_tensor=None, length=None, slices=None)

Embed local MPAs on a linear chain and sum as MPA.

We return the sum over embed_slice(length, slices[i], mpas[i], embed_tensor) as MPA.

If slices is omitted, we use regular_slices(length, width, offset) with offset = 1, width = len(mpas[0]) and length = len(mpas) + width - offset.

If slices is omitted or if the slices just described are given, we call _local_sum_identity(), which gives a smaller bond dimension than naive embedding and summing.

Parameters: mpas – List of local MPAs. embed_tensor – Defaults to square identity matrix (see _embed_ltens_identity() for details) length – Length of the resulting chain, ignored unless slices is given. slices – slice[i] specifies the position of mpas[i], optional. An MPA.
mpnum.mparray.louter(a, b)

Computes the tensorproduct of $$a \otimes b$$ locally, that is when a and b have the same number of sites, the new local tensors are the tensorproducts of the original ones.

Parameters: a (MPArray) – MPArray b (MPArray) – MPArray of same length as a Tensor product of a and b in terms of their local tensors
mpnum.mparray.norm(mpa)

Computes the norm (Hilbert space norm for MPS, Frobenius norm for MPO) of the matrix product operator. In contrast to mparray.inner, this can take advantage of the normalization

WARNING This also changes the MPA inplace by normalizing.

Parameters: mpa – MPArray l2-norm of that array
mpnum.mparray.normdist(mpa1, mpa2)

More efficient version of norm(mpa1 - mpa2)

Parameters: mpa1 – MPArray mpa2 – MPArray l2-norm of mpa1 - mpa2
mpnum.mparray.outer(mpas, astype=None)

Performs the tensor product of MPAs given in *args

Parameters: mpas – Iterable of MPAs same order as they should appear in the chain astype – Return type. If None, use the type of the first MPA. MPA of length len(args[0]) + ... + len(args[-1])
mpnum.mparray.partialdot(mpa1, mpa2, start_at, axes=(-1, 0))

Partial dot product of two MPAs of inequal length.

The shorter MPA will start on site start_at. Local dot products will be carried out on all sites of the shorter MPA. Other sites will remain unmodified.

mpa1 and mpa2 can also have equal length if start_at = 0. In this case, we do the same as dot().

Parameters: mpa2 (mpa1,) – Factors as MPArrays, length must be inequal. start_at – The shorter MPA will start on this site. axes – See axes argument to dot(). MPA with length of the longer MPA.
mpnum.mparray.partialtrace(mpa, axes=(0, 1), mptype=None)

Computes the trace or partial trace of an MPA.

This function is most useful for computing traces of an MPO or MPA over given physical legs. For obtaining partial traces (i.e., reduced states) of an MPO, mpnum.mpsmpo.reductions_mpo() will be more convenient.

By default (axes=(0, 1)) compute the trace and return the value as length-one MPA with zero physical legs.

For axes=(m, n) with integer m, trace over the given axes at all sites and return a length-one MPA with zero physical legs. (Use trace() to get the value directly.)

For axes=(axes1, axes2, ...) trace over axesN at site N, with axesN=(axisN_1, axisN_2) tracing the given physical legs and axesN=None leaving the site invariant. Afterwards, prune() is called to remove sites with zero physical legs from the result.

Parameters: mpa – MPArray axes – Axes for trace, (axis1, axis2) or (axes1, axes2, ...) with axesN=(axisN_1, axisN_2) or axesN=None. An MPArray (possibly one site with zero physical legs)
mpnum.mparray.prune(mpa, singletons=False)

Contract sites with zero physical legs.

Parameters: mpa (MPArray) – MPA or iterator over local tensors singletons – If True, also contract sites where all physical legs have size 1 An MPA of smaller length
mpnum.mparray.regular_slices(length, width, offset)

Iterate over regular slices on a linear chain.

Put slices on a linear chain as follows:

>>> n = 5
>>> [tuple(range(*s.indices(n))) for s in regular_slices(n, 3, 2)]
[(0, 1, 2), (2, 3, 4)]
>>> n = 7
>>> [tuple(range(*s.indices(n))) for s in regular_slices(n, 3, 2)]
[(0, 1, 2), (2, 3, 4), (4, 5, 6)]


The scheme is illustrated by the following figure:

 #### width ##### offset overlap offset ##### width ####

Todo

This table needs cell borders in the HTML output (-> CSS) and the tabularcolumns command doesn’t work.

Note that the overlap may be larger than, equal to or smaller than zero.

We enforce that the last slice coincides with the end of the chain, i.e. (length - width) / offset must be integer. We produce (length - width) / offset + 1 slices and the i-th slice is slice(offset * i, offset * i + width), with i starting at zero.

Parameters: length (int) – The length of the chain. width (int) – The width of each slice. offset (int) – Difference between starting positions of successive slices. First slice starts at 0. Iterator over slices.
mpnum.mparray.sandwich(mpo, mps, mps2=None)

Compute <mps|MPO|mps> efficiently

The runtime of this method scales with D**3 Dp + D**2 Dp**3 where D and Dp are the bond dimensions of mps and mpo. This is more efficient than inner(mps, dot(mpo, mps)), whose runtime scales with D**4 Dp**3, and also more efficient that dot(mps.conj(), dot(mpo, mps)).to_array(), whose runtime scales with D**6 Dp**3.

If mps2 is given, <mps2|MPO|mps> is computed instead.

mpnum.mparray.embed_slice(length, slice_, mpa, embed_tensor=None)

Embed a local MPA on a linear chain.

Parameters: length (int) – Length of the resulting MPA. slice (slice) – Specifies the position of mpa in the result. mpa (MPArray) – MPA of length slice_.stop - slice_.start. embed_tensor – Defaults to square identity matrix (see _embed_ltens_identity() for details) MPA of length length
mpnum.mparray.trace(mpa, axes=(0, 1))

Compute the trace of the given MPA.

By default, just compute the trace.

If you specify axes (see partialtrace() for details), you must ensure that the result has no physical legs anywhere.

Parameters: mpa – MParray axes – Axes for trace, (axis1, axis2) or (axes1, axes2, ...) with axesN=(axisN_1, axisN_2) or axesN=None. A single scalar (int/float/complex, depending on mpa)
mpnum.mparray.diag(mpa, axis=0)

Returns the diagonal elements mpa[i, i, ..., i]. If mpa has more than one physical dimension, the result is a numpy array with MPArray entries, otherwise its a numpy array with floats.

Parameters: mpa – MPArray with pdims > axis axis – The physical index to take diagonals over Array containing the diagonal elements (each diagonal element is an MPArray with the physical dimension reduced by one, note that an MPArray with physical dimension 0 is a simple number)
mpnum.mparray.sumup(mpas, weights=None)

Returns the sum of the MPArrays in mpas. Same as

but should be faster.

Parameters: mpas – Iterator over MPArrays Sum of mpas

## Random test matrix product arrays¶

Module to create random test instances of matrix product arrays

mpnum.factory.eye(sites, ldim)

Returns a MPA representing the identity matrix

Parameters: sites – Number of sites ldim – Int-like local dimension or iterable of local dimensions Representation of the identity matrix as MPA
>>> I = eye(4, 2)
>>> I.bdims, I.pdims
((1, 1, 1), ((2, 2), (2, 2), (2, 2), (2, 2)))
>>> I = eye(3, (3, 4, 5))
>>> I.pdims
((3, 3), (4, 4), (5, 5))

mpnum.factory.random_local_ham(sites, ldim=2, intlen=2, randstate=None)

Generates a random Hamiltonian on sites sites with local dimension ldim, which is a sum of local Hamiltonians with interaction length intlen.

Parameters: sites – Number of sites ldim – Local dimension intlen – Interaction length of the local Hamiltonians MPA representation of the global Hamiltonian
mpnum.factory.random_mpa(sites, ldim, bdim, randstate=None, normalized=False, force_bdim=False, dtype=<class 'numpy.float64'>)

Returns a MPA with randomly choosen local tensors

Parameters: sites – Number of sites ldim – Depending on the type passed (checked in the following order) iterable of iterable: Detailed list of physical dimensions, retured mpa will have exactly this for mpa.pdims iterable of scalar: Same physical dimension for each site scalar: Single physical leg for each site with given dimension bdim – Bond dimension randn – Function used to generate random local tensors randstate – numpy.random.RandomState instance or None normalized – Resulting mpa has mp.norm(mpa) == 1 force_bdim – If True, the bond dimension is exaclty bdim. Otherwise, it might be reduced if we reach the maximum sensible bond dimension for a bond. dtype – Whicht type the returned array should have. Currently only np.real_ and np.complex_ is implemented (default: complex) randomly choosen matrix product array
>>> mpa = random_mpa(4, 2, 10, force_bdim=True)
>>> mpa.bdims, mpa.pdims
((10, 10, 10), ((2,), (2,), (2,), (2,)))

>>> mpa = random_mpa(4, (1, 2), 10, force_bdim=True)
>>> mpa.bdims, mpa.pdims
((10, 10, 10), ((1, 2), (1, 2), (1, 2), (1, 2)))

>>> mpa = random_mpa(4, [(1, ), (2, 3), (4, 5), (1, )], 10, force_bdim=True)
>>> mpa.bdims, mpa.pdims
((10, 10, 10), ((1,), (2, 3), (4, 5), (1,)))

mpnum.factory.random_mpdo(sites, ldim, bdim, randstate=<module 'numpy.random' from '/usr/lib/python3/dist-packages/numpy/random/__init__.py'>)

Returns a randomly choosen matrix product density operator (i.e. positive semidefinite matrix product operator with trace 1).

Parameters: sites – Number of sites ldim – Local dimension bdim – Bond dimension randstate – numpy.random.RandomState instance randomly choosen classicaly correlated matrix product density op.
>>> rho = random_mpdo(4, 2, 4)
>>> rho.bdims, rho.pdims
((4, 4, 4), ((2, 2), (2, 2), (2, 2), (2, 2)))
>>> rho.normal_form
(0, 4)

mpnum.factory.random_mps(sites, ldim, bdim, randstate=None, force_bdim=False)

Returns a randomly choosen normalized matrix product state

Parameters: sites – Number of sites ldim – Local dimension bdim – Bond dimension randstate – numpy.random.RandomState instance or None force_bdim – If True, the bond dimension is exaclty bdim. Otherwise, it might be reduced if we reach the maximum sensible bond dimension for a bond. randomly choosen matrix product (pure) state
>>> mps = random_mps(4, 2, 10, force_bdim=True)
>>> mps.bdims, mps.pdims
((10, 10, 10), ((2,), (2,), (2,), (2,)))
>>> mps.normal_form
(0, 4)
>>> round(abs(1 - mp.inner(mps, mps)), 10)
0.0

mpnum.factory.random_mpo(sites, ldim, bdim, randstate=None, hermitian=False, normalized=True, force_bdim=False)

Returns an hermitian MPO with randomly choosen local tensors

Parameters: sites – Number of sites ldim – Local dimension bdim – Bond dimension randstate – numpy.random.RandomState instance or None hermitian – Is the operator supposed to be hermitian normalized – Operator should have unit norm force_bdim – If True, the bond dimension is exaclty bdim. Otherwise, it might be reduced if we reach the maximum sensible bond dimension for a bond. randomly choosen matrix product operator
>>> mpo = random_mpo(4, 2, 10, force_bdim=True)
>>> mpo.bdims, mpo.pdims
((10, 10, 10), ((2, 2), (2, 2), (2, 2), (2, 2)))
>>> mpo.normal_form
(0, 4)

mpnum.factory.zero(sites, ldim, bdim, force_bdim=False)

Returns a MPA with localtensors beeing zero (but of given shape)

Parameters: sites – Number of sites ldim – Depending on the type passed (checked in the following order) iterable of iterable: Detailed list of physical dimensions, retured mpa will have exactly this for mpa.pdims iterable of scalar: Same physical dimension for each site scalar: Single physical leg for each site with given dimension bdim – Bond dimension force_bdim – If True, the bond dimension is exaclty bdim. Otherwise, it might be reduced if we reach the maximum sensible bond dimension for a bond. Representation of the zero-array as MPA
mpnum.factory.diagonal_mpa(entries, sites)

@todo: Docstring for diagonal_mpa.

Parameters: entries – @todo @todo

## Matrix product states and operators¶

Matrix Product State (MPS) and Operator (MPO) functions

The Introduction also covers the definitions mentioned below.

### Definitions¶

We consider a linear chain of $$n$$ sites with associated Hilbert spaces mathcal H_k = C^{d_k}, $$d_k$$, $$k \in [1..n] := \{1, 2, \ldots, n\}$$. The set of linear operators $$\mathcal H_k \to \mathcal H_k$$ is denoted by $$\mathcal B_k$$. We write $$\mathcal H = \mathcal H_1 \otimes \cdots \otimes \mathcal H_n$$ and the same for $$\mathcal B$$.

We use the following three representations:

• Matrix product state (MPS): Vector $$\lvert \psi \rangle \in \mathcal H$$
• Matrix product operator (MPO): Operator $$M \in \mathcal B$$
• Locally purified matrix product state (PMPS): Positive semidefinite operator $$\rho \in \mathcal B$$

All objects are represented by $$n$$ local tensors.

#### Matrix product state (MPS)¶

Represent a vector $$\lvert \psi \rangle \in \mathcal H$$ as

$\langle i_1 \ldots i_n \vert \psi \rangle = A^{(1)}_{i_1} \cdots A^{(n)}_{i_n}, \quad A^{(k)}_{i_k} \in \mathbb C^{D_{k-1} \times D_k}, \quad D_0 = 1 = D_n.$

The $$k$$-th local tensor is $$T_{l,i,r} = (A^{(k)}_i)_{l,r}$$.

The vector $$\lvert \psi \rangle$$ can be a quantum state, with the density matrix given by $$\rho = \lvert \psi \rangle \langle \psi \rvert \in \mathcal B$$. Reference: E.g. [Sch11].

#### Matrix product operator (MPO)¶

Represent an operator $$M \in \mathcal B$$ as

$\langle i_1 \ldots i_n \vert M \vert j_1 \ldots j_n \rangle = A^{(1)}_{i_1 j_1} \cdots A^{(n)}_{i_n j_n}, \quad A^{(k)}_{i_k j_k} \in \mathbb C^{D_{k-1} \times D_k}, \quad D_0 = 1 = D_n.$

The $$k$$-th local tensor is $$T_{l,i,j,r} = (A^{(k)}_{i j})_{l,r}$$.

This representation can be used to represent a mixed quantum state $$\rho = M$$, but it is not limited to positive semidefinite $$M$$. Reference: E.g. [Sch11].

#### Locally purified matrix product state (PMPS)¶

Represent a positive semidefinite operator $$\rho \in \mathcal B$$ as follows: Let $$\mathcal H_k' = \mathbb C^{d'_k}$$ with suitable $$d'_k$$ and $$\mathcal P = \mathcal H_1 \otimes \mathcal H'_1 \otimes \cdots \otimes \mathcal H_n \otimes \mathcal H'_n$$. Find $$\vert \Phi \rangle \in \mathcal P$$ such that

$\rho = \operatorname{tr}_{\mathcal H'_1, \ldots, \mathcal H'_n} (\lvert \Phi \rangle \langle \Phi \rvert)$

and represent $$\lvert \Phi \rangle$$ as

$\langle i_1 i'_1 \ldots i_n i'_n \vert \Phi \rangle = A^{(1)}_{i_1 i'_1} \cdots A^{(n)}_{i_n i'_n}, \quad A^{(k)}_{i_k j_k} \in \mathbb C^{D_{k-1} \times D_k}, \quad D_0 = 1 = D_n.$

The $$k$$-th local tensor is $$T_{l,i,i',r} = (A^{(k)}_{i i'})_{l,r}$$.

The ancillary dimensions $$d'_i$$ are not determined by the $$d_i$$ but depend on the state. E.g. if $$\rho$$ is pure, one can set all $$d_i = 1$$. Reference: E.g. [Cue13].

Todo

Are derived classes MPO/MPS/PMPS of any help?

Todo

I am not sure the current definition of PMPS is the most elegant for our purposes...

References:

mpnum.mpsmpo.mps_to_mpo(mps)

Convert a pure MPS to a mixed state MPO.

Parameters: mps (MPArray) – An MPA with one physical leg An MPO (density matrix as MPA with two physical legs)
mpnum.mpsmpo.mps_to_pmps(mps)

Convert a pure MPS into a local purification MPS mixed state.

The ancilla legs will have dimension one, not increasing the memory required for the MPS.

Parameters: mps (MPArray) – An MPA with one physical leg An MPA with two physical legs (system and ancilla)
mpnum.mpsmpo.pmps_dm_to_array(pmps, global_=False)

Convert PMPS to full array representation of the density matrix

The runtime of this method scales with D**3 instead of D**6 where D is the bond and D**6 is the scaling of using pmps_to_mpo() and to_array(). This is useful for obtaining reduced states of a PMPS on non-consecutive sites, as normalizing before using pmps_to_mpo() may not be sufficient to reduce the bond dimension in that case.

Note

The resulting array will have dimension-1 physical legs removed.

mpnum.mpsmpo.pmps_reduction(pmps, support)

Convert a PMPS to a PMPS representation of a local reduced state

Parameters: support – Set of sites to keep Sites traced out at the beginning or end of the chain are removed using reductions_pmps() and a suitable normalization. Sites traced out in the middle of the chain are converted to sites with physical dimension 1 and larger ancilla dimension.
mpnum.mpsmpo.pmps_to_mpo(pmps)

Convert a local purification MPS to a mixed state MPO.

A mixed state on n sites is represented in local purification MPS form by a MPA with n sites and two physical legs per site. The first physical leg is a ‘system’ site, while the second physical leg is an ‘ancilla’ site.

Parameters: pmps (MPArray) – An MPA with two physical legs (system and ancilla) An MPO (density matrix as MPA with two physical legs)
mpnum.mpsmpo.pmps_to_mps(pmps)

Convert a PMPS with unit ancilla dimensions to a simple MPS

If all ancilla dimensions of the PMPS are equal to unity, they are removed. Otherwise, an AssertionError is raised.

mpnum.mpsmpo.reductions_mpo(mpa, width=None, startsites=None, stopsites=None)

Iterate over MPO partial traces of an MPO

The support of the i-th result is range(startsites[i], stopsites[i]).

Parameters: mpa (mpnum.mparray.MPArray) – An MPO startsites – Defaults to range(len(mpa) - width + 1). stopsites – Defaults to [ start + width for start in startsites ]. If specified, we require startsites to be given and width to be None. width – Number of sites in support of the results. Default None. Must be specified if one or both of startsites and stopsites are not given. Iterator over partial traces as MPO
mpnum.mpsmpo.reductions_mps_as_mpo(mps, width=None, startsites=None, stopsites=None)

Iterate over MPO mpdoreduced states of an MPS

width, startsites and stopsites: See reductions_mpo().

Parameters: mps – Pure state as MPS Iterator over reduced states as MPO
mpnum.mpsmpo.reductions_mps_as_pmps(mps, width=None, startsites=None, stopsites=None)

Iterate over PMPS reduced states of an MPS

width, startsites and stopsites: See reductions_mpo().

Parameters: mps – Pure state as MPS Iterator over reduced states as PMPS
mpnum.mpsmpo.reductions_pmps(pmps, width=None, startsites=None, stopsites=None)

Iterate over PMPS partial traces of a PMPS

width, startsites and stopsites: See reductions_mpo().

Parameters: pmps – Mixed state in locally purified MPS representation (PMPS, see Definitions) Iterator over reduced states as PMPS
mpnum.mpsmpo.reductions(state, mode, **kwargs)

Todo

## Eigenvalues and eigenvectors (DMRG)¶

Linear algebra with matrix product arrays

Currently, we support computing ground states (i.e. minimal eigenvalue and eigenvector).

mpnum.linalg.mineig(mpo, startvec=None, startvec_bonddim=None, randstate=None, max_num_sweeps=5, eigs_opts=None, minimize_sites=1)

Iterative search for smallest eigenvalue and eigenvector of an MPO.

Algorithm: [Sch11, Sec. 6.3]

Parameters: mpo (MPArray) – A matrix product operator (MPA with two physical legs) startvec – initial guess for eigenvector (default random MPS with bond startvec_bonddim) startvec_bonddim – Bond dimension of random start vector if no start vector is given. (default: Use the bond dimension of mpo) randstate – numpy.random.RandomState instance or None max_num_sweeps – Maximum number of sweeps to do (default 5) eigs_opts – kwargs for scipy.sparse.linalg.eigs(). If you supple which, you will probably not obtain the minimal eigenvalue. k different from one is not supported at the moment. minimize_sites (int) – Number of connected sites minimization should be performed on (default 1) mineigval, mineigval_eigvec_mpa

We minimize the eigenvalue by obtaining the minimal eigenvalue of an operator supported on ‘minimize_sites’ many sites. For minimize_sites=1, this is called “variational MPS ground state search” or “single-site DMRG” [Sch11, Sec. 6.3, p. 69]. For minimize_sites>1, this is called “multi-site DMRG”.

Comments on the implementation, for minimize_sites=1:

References are to the arXiv version of [Sch11] assuming we replace zero-based with one-based indices there.

leftvecs[i] is L_{i-1} rightvecs[i] is R_{i} | See Fig. 38 and Eq. (191) on p. 62. mpo[i] is W_{i} / eigvec[i] is M_{i} This is just the MPS matrix.

Psi^A_{i-1} and Psi^B_{i} are identity matrices because of normalization. (See Fig. 42 on p. 67 and the text; see also Figs. 14 and 15 and pages 28 and 29.)

mpnum.linalg.mineig_sum(mpas, startvec=None, startvec_bonddim=None, randstate=None, max_num_sweeps=5, eigs_opts=None, minimize_sites=1)

Iterative search for smallest eigenvalue+vector of a sum

Try to compute the ground state of the sum of the objects in mpas. MPOs are taken as-is. An MPS $$\vert\psi\rangle$$ is interpreted as $$\vert\psi\rangle \langle\psi\vert$$ in the sum.

This function executes exactly the same algorithm as mineig() applied to an uncompressed MPO sum of the elements in mpas, but it obtains the ingredients for the local optimization steps using less memory and execution time. In particular, this function does not have to convert an MPS in mpas to an MPO.

Todo

Add information on how the runtime of mineig() and mineig_sum() with the the different bond dimensions.

Parameters: mpas – A sequence of MPOs or MPSs

Remaining parameters and description: See mineig().

Algorithm: [Sch11, Sec. 6.3]

## Local POVMs¶

An informationally complete d-level POVM.

The POVM simplifies to measuring Paulis matrices in the case of qubits.

class mpnum.povm.localpovm.POVM(elements, info_complete=False, pinv=<function pinv>)

Bases: object

Represent a Positive Operator-Valued Measure (POVM).

classmethod from_vectors(vecs, info_complete=False)

Generates a POVM consisting of rank 1 projectors based on the corresponding vectors.

Parameters: vecs – Iterable of np.ndarray with ndim=1 representing the vectors for the POVM info_complete – Is the POVM informationally complete (default False)
informationally_complete
linear_inversion_map

Map that reconstructs a density matrix with linear inversion.

Linear inversion is performed by taking the Moore–Penrose pseudoinverse of self.probability_map.

probability_map

Map that takes a raveled density matrix to the POVM probabilities

The following two return the same:

probab = np.array([ np.trace(np.dot(elem, rho)) for elem in a_povm ])
probab = np.dot(a_povm.probability_map, rho.ravel())

mpnum.povm.localpovm.concat(povms, weights, info_complete=False)

Combines the POVMs given in povms according the weights given to a new POVM.

Parameters: povms – Iterable of POVM weights – Iterable of real numbers, should sum up to one info_complete – Is the resulting POVM informationally complete POVM
mpnum.povm.localpovm.pauli_parts(dim)

The POVMs used by pauli_povm() as a list

For dim > 3, x_povm() and y_povm() are returned. For dim = 2, z_povm() is included as well.

Parameters: dim – Dimension of the system Tuple of POVMs
mpnum.povm.localpovm.pauli_povm(dim)

An informationally complete d-level POVM that simplifies to measuring Pauli matrices in the case d=2.

Parameters: dim – Dimension of the system POVM with (generalized) Pauli measurments
mpnum.povm.localpovm.x_povm(dim)

The X POVM simplifies to measuring Pauli X eigenvectors for dim=2.

Parameters: dim – Dimension of the system POVM with generalized X measurments
mpnum.povm.localpovm.y_povm(dim)

The Y POVM simplifies to measuring Pauli Y eigenvectors for dim=2.

Parameters: dim – Dimension of the system POVM with generalized Y measurments
mpnum.povm.localpovm.z_povm(dim)

The Z POVM simplifies to measuring Pauli Z eigenvectors for dim=2.

Parameters: dim – Dimension of the system POVM with generalized Z measurments

## Matrix-product POVMs¶

Matrix-product representation of POVMs

This module provides the following classes:

### Linear combinations of functions of POVM outcomes¶

In order to perform the just mentioned estimation of probabilities of one POVM from samples of another POVM with possibly larger support, we provide a function which can estimate linear functions of functions of POVM outcomes: Let $$M$$ a finite index set with real elements $$y \in M \subset \mathbb R$$ such that $$\hat y$$ are the positive semidefinite POVM elements which sum to the identity, $$\sum_{y \in M} \hat y = 1$$. Given a state $$\rho$$, the probability mass function (PMF) of the probability distribution given by the POVM and the state can be expressed as $$p_y = \operatorname{tr}(\rho \hat y)$$, $$y \in M$$ or as $$p(x) = \sum_{y \in M} \delta(x - y) p_y$$. Let further $$D = (x_1, \ldots, x_m)$$, $$x_k \in M$$ a set of samples from $$p(x)$$ and let $$f \colon M \to \mathbb R$$ an arbitrary function of the POVM outcomes. The true value $$\langle f \rangle_p = \int f(y) p(y) \mathrm d y$$ can then be estimated using the sample average $$\langle f \rangle_D = \frac1m \sum_{k=1}^m f(x_k) p_{x_k}$$. In the same way, a linear combination $$f = \sum c_i f_i$$ of functions $$f_i \colon M \to \mathbb R$$ of POVM outcomes can be estimated by $$\langle f \rangle_D = \sum_i c_i \langle f_i \rangle_D$$. Such a linear combination of functions of POVM outcomes can be estimated using MPPovm.est_lfun(). More technically, the relation $$\langle \langle f \rangle_D \rangle_{p_m} = \langle f \rangle_p$$ shows that $$\langle f \rangle_D$$ is an unbiased estimator for the true expectation value $$\langle f \rangle_p$$; the probability distribution of the dataset $$D$$ is given by the sampling distribution $$p_m(D) = p(x_1) \ldots p(x_m)$$.

Estimates of the POVM probabilities $$p_y$$ can also be expressed as functions of this kind: Consider the function

$\begin{split}\theta_y(x) = \begin{cases} 1, & x = y, \\ 0, & \text{otherwise.} \end{cases}\end{split}$

The true value of this function under $$p(x)$$ is $$\langle \theta_y \rangle_p = p_y$$ and the sample average $$\langle \theta_y \rangle_D$$ provides an estimator for $$p_y$$. In order to estimate probabilities of one POVM from samples for another POVM, such a function can be used: E.g. to estimate the probability of the $$(+1, +1)$$ outcome of the POVM XX1...1, we can define a function which is equal to 1 if the outcome of the POVM XX...X on the first two sites is equal to $$(+1, +1)$$ and zero otherwise. The sample average of this function over samples for the latter POVM XX...X will estimate the desired probability. This approach is implemented in MPPovm.est_pmf_from(). If samples from more than one POVM are available for estimating a given probability, a weighted average of estimators can be used as implemented in MPPovm.est_pmf_from_mpps(); the list of MP-POVMs for which samples are available is passed as an MPPovmList instance. Finally, the function MPPovmList.est_lfun_from() allows estimation of a linear combination of probabilities from different POVMs using samples of a second list of MP-POVMs. This function also estimates the variance of the estimate. In order to perform the two estimation procedures, for each probability, we construct an estimator from a weighted average of functions of outcomes of different POVMs, as has been explained above. For more simple settings, MPPovmList.est_lfun() is also available.

True values of the functions just mentioned can be obtained from MPPovm.lfun(), MPPovmList.lfun() and MPPovmList.lfun_from(). All functions return both the true expectation value and the variance of the expectation value.

The variance of the (true) expectation value $$\langle f \rangle_p$$ of a function $$f\colon M \to \mathbb R$$ is given by $$\operatorname{var}_p(f) = \operatorname{cov}_p(f, f)$$ with $$\operatorname{cov}_p(f, g) = \langle fg \rangle_p - \langle f \rangle_p \langle g \rangle_p$$. The variance of the estimate $$\langle f \rangle_D$$ is given by $$\operatorname{var}_{p_m}(\langle f \rangle_D) = \frac1m \operatorname{var}_p(f)$$ where $$p_m(D)$$ is the sampling distribution from above. An unbiased estimator for the covariance $$\operatorname{cov}_p(f, g)$$ is given by $$\frac{m}{m-1} \operatorname{cov}_D(f, g)$$ where the sample covariance $$\operatorname{cov}_D(f, g)$$ is defined in terms of sample averages in the usual way, $$\operatorname{cov}_D(f, g) = \langle fg \rangle_D - \langle f \rangle_D \langle g \rangle_D$$. This estimator is used by MPPovm.est_lfun().

Todo

Explain the details of the variance estimation, in particular the difference between the variances returned from MPPovmList.lfun() and MPPovmList.lfun_from(). Check the mean square error.

Add a good references explaining all facts mentioned above and for further reading.

Document the runtime and memory cost of the functions.

### Class and function reference¶

class mpnum.povm.mppovm.MPPovm(*args, **kwargs)

MPArray representation of multipartite POVM

There are two different ways to write down a POVM in matrix product form

1. As a list of matrix product operators, where each entry corresponds to

a single POVM element

2. As a matrix proudct array with 3 physical legs:

[POVM index, column index, row index]

that is, the first physical leg of the MPArray corresponds to the index of the POVM element. This representation is especially helpful for computing expectation values with MPSs/MPDOs.

Here, we choose the second.

Todo

This class should provide a function which returns expectation values as full array. (Even though computing expectation values using the POVM struture brings advantages, we usually need the result as full array.) This function should also replace small negative probabilities by zero and normalize the sum of all probabilities to unity (if the deviation is non-zero but small). The same checks should also be implemented in localpovm.POVM.

Todo

Right now we use this class for multi-site POVMs with elements obtained from every possible combination of the elements of single-site POVMs: The POVM index is split across all sites. Explore whether and how this concept can also be useful in other cases.

_elemsum_identity(support, given, eps)

Check whether a given subset of POVM elements sums to a multiple of the identity

Parameters: support (np.ndarray) – List of sites on which POVM elements are selected by given given (np.ndarray) – Whether a POVM element with a given index should be included (bool array)

A POVM element specified by the compound index (i_1, ..., i_n) with n = len(self) is included if given[i_(support[0]), ..., i_(support[k])] is True.

Returns: If the POVM elements sum to a fraction of the identity, return the fraction. Otherwise return None.
_fill_outcome_mpa_holes(support, outcome_mpa)

Fill holes in an MPA on some of the outcome physical legs

The dot product of outcome_mpa and self provides a sum over some or all elements of the POVM. The way sites are added to outcome_mpa implements the selection rule described in self._elemsum_identity().

Parameters: support (np.ndarray) – List of sites where outcome_mpa lives outcome_mpa (mp.MPArray) – An MPA with physical legs in agreement with self.outdims with some sites omitted An MPA with physical legs given by self.outdims
_mppl_lfun_estimator(est_coeff, est_funs, other, n_samples, coeff, eps)

Compute the estimator used by MPPovmList.estfun_from()

est_coeff[i] and est_funs[i] will specify an estimator in the format used by MPPovm.lfun() on other.mpps[i]. This function adds the coefficients and functions necessary to estimate the linear function of self probabilities specified by coeff.

Parameters: est_coeff – Output parameter, tuple of lists est_funs – Output parameter, tuple of lists other (MPPovmList) – An MP-POVM list n_samples – n_samples[i] specifies the number of samples available for other.mpps[i]. They are used for a weighted average if a given self probability can be estimated from more than one MP-POVMs in other. coeff – A linear function of self probabilities is specified by coeff n_samples: A shape self.nsoutdims array which specifies how many samples very available for each probability.

Note

Output is also added to the parameters est_coeff and est_fun.

_pmf_as_array_pmps_ltr(pmps, partial=False)

PMF-as-array fast path for PMPS

Called automatically by pmf_as_array().

This function contracts the following tensor network from top to bottom and from left to right along the chain:

+-----+  PMPS bond     +---------+     PMPS bond
|     |----------------| PMPS    |-------------------
|     |                +---------+
|     |                   |  |_______ system
|     |           ancilla |         |
|     |                   |         |
|     |  PMPS-cc bond  +---------+  |  PMPS-cc bond
|  p  |----------------| PMPS-cc |--|----------------
|     |                +---------+  |
|     |                   |         |
|     |           system' |   ______|
|     |                   |  |
|     |  POVM bond     +---------+     POVM bond
|     |----------------| MPPOVM  |-------------------
+-----+                +---------+
|                      |
| probab               | probab'

_pmf_as_array_pmps_symm(state)

PMF-as-array fast path for PMPS (uses less memory)

This function contracts the same tensor network as self._pmf_as_array_pmps_ltr(), but it starts at both ends of the chain and proceeds to a certain position in the middle of the chain. We choose the position such that the maximal size of all intermediate results is minimal. This might also minimize runtime in some cases.

_sample_cond(rng, state, mode, n_samples, n_group, out, eps)

Sample using conditional probabilities (call self.sample())

static _sample_cond_single(rng, marginal_pmf, n_group, out, eps)

Single sample from conditional probab. (call self.sample())

_sample_direct(rng, state, mode, n_samples, out, eps)

Sample from full pmfilities (call self.sample())

block(nr_sites)

Embed an MP-POVM on local blocks

The returned MPPovmList will contain self embedded at every possible position on len(self) neighbouring sites in a chain of length nr_sites. The remaining sites are not measured (self.embed()).

self must a have a uniform local Hilbert space dimension.

Parameters: nr_sites – Number of sites of the resulting MP-POVMs
block_pmfs_as_array(state, mode, asarray=False, eps=1e-10, **redarg)

Todo

elements

Returns an iterator over all POVM elements. The result is the i-th POVM element in MPO form.

It would be nice to call this method __iter__, but this breaks mp.dot(mppovm, ...). In addition, next(iter(mppovm)) would not be equal to mppovm[0].

embed(nr_sites, startsite, local_dim)

Embed MP-POVM into larger system

Applying the resulting embedded MP-POVM to a state rho gives the same result as applying the original MP-POVM self on the reduced state of sites range(startsite, startsite + len(self)) of rho.

Parameters: nr_sites – Number of sites of the resulting MP-POVM startsite – Position of the first site of self in the resulting MP-POVM local_dim – Local dimension of sites to be added MP-POVM with self on sites range(startsite, startsite + len(self)) and MPPovm.eye() elsewhere
est_lfun(coeff, funs, samples, weights=None, eps=1e-10)

Estimate a linear combination of functions of POVM outcomes

This function estimates the function with exact value given by MPPovm.lfun(); see there for description of the parameters coeff and funs.

Parameters: samples (np.ndarray) – A shape (n_samples, len(self.nsoutdims)) with samples from self weights – A length n_samples array for weighted samples. You can submit counts by passing them as weights. The number of samples used in average and variance estimation is determined by weights.sum() if weights is given. (est, var): Estimated value and estimated variance of the estimated value. For details, see Linear combinations of functions of POVM outcomes.
est_pmf(samples, normalize=True, eps=1e-10)

Estimate probability mass function from samples

Parameters: samples (np.ndarray) – (n_samples, len(self.nsoutdims)) array of samples normalize (bool) – True: Return normalized probability estimates (default). False: Return integer outcome counts. Estimated probabilities as ndarray est_pmf with shape self.nsoutdims

n_samples * est_pmf[i1, ..., ik] provides the number of occurences of outcome (i1, ..., ik) in samples.

est_pmf_from(other, samples, eps=1e-10)

Estimate PMF from samples of another MPPovm other

If other does not provide information on all elements in self, we require that the elements in self for which information is provided sum to a multiple of the identity.

Example: If we consider the MPPovm MPPovm.from_local_povm(x, n) for given local POVMs x, it is possible to obtain counts for the Pauli X part of x = pauli_povm() from samples for x = x_povm(); this is also true if the latter is supported on a larger part of the chain.

Parameters: other (MPPovm) – Another MPPovm samples (np.ndarray) – (n_samples, len(other.nsoutdims)) array of samples for other (est_pmf, n_samples_used). est_pmf: Shape self.nsoutdims ndarray of normalized probability estimates; the sum over the available probability estimates is equal to the fraction of the identity obtained by summing the corresponding POVM elements. n_samples_used: Number of samples which have contributed to the PMF estimate.
est_pmf_from_mpps(other, samples, eps=1e-10)

Estimate probability mass function from MPPovmList samples

Parameters: other (MPPovmList) – An MPPovmList instance samples – Iterable of samples (e.g. from MPPovmList.samples()) (p_est, n_samples_used), both are shape self.nsoutdims ndarrays. p_est provides estimated probabilities and n_samples_used provides the effective number of samples used for each probability.
expectations(mpa, mode='auto')

Computes the exp. values of the POVM elements with given state

Parameters: mpa – State given as MPDO, MPS, or PMPS mode – In which form mpa is given. Possible values: ‘mpdo’, ‘pmps’, ‘mps’, or ‘auto’. If ‘auto’ is passed, we choose between ‘mps’ or ‘mpdo’ depending on the number of physical legs Iterator over the expectation values, the n-th element is the expectation value correponding to the reduced state on sites [n,...,n + len(self) - 1]
classmethod eye(local_dims)

Construct MP-POVM with no output or measurement

Corresponds to taking the partial trace of the quantum state and a shorter MP-POVM.

Parameters: local_dims – Iterable of local dimensions
classmethod from_local_povm(lelems, width)

Generates a product POVM on width sites.

Parameters: lelems – POVM elements as an iterator over all local elements (i.e. an iterator over numpy arrays representing the latter) width (int) – Number of sites the POVM lives on MPPovm which is a product POVM of the lelems
hdims

Local Hilbert space dimensions

lfun(coeff, funs, state, mode='auto', eps=1e-10)

Evaluate a linear combination of functions of POVM outcomes

Parameters: coeff (np.ndarray) – A length n_funs array with the coefficients of the linear combination. If None, return the estimated values of the individual functions and the estimated covariance matrix of the estimates. funs (np.ndarray) – A length n_funs sequence of functions. If None, the estimated function will be a linear function of the POVM probabilities.

For further information, see also Linear combinations of functions of POVM outcomes.

The parameters state and mode are passed to MPPovm.pmf().

Returns: (value, var): Expectation value and variance of the expectation value
match_elems(other, exclude_dup=(), eps=1e-10)

Find POVM elements in other which have information on self

We find all POVM sites in self which have only one possible outcome. We discard these outputs in other and afterwards check other and self for any common POVM elements.

Parameters: other – Another MPPovm exclude_dup – Sequence which can include ‘self’ or ‘other’ (or both) to assert that there are no linearly dependent pairs of elements in self or other. eps – Threshould for values which should be treated as zero (matches, prefactors)

matches[i_1, ..., i_k, j_1, ..., j_k] specifies whether outcome (i_1, ..., i_k) of self has the same POVM element as the partial outcome (j_1, ..., j_k) of other; outcomes are specified only on the sites mentioned in sites such that k = len(sites).

prefactors[i_1, ..., i_k, j_1, ..., j_k] specifies how samples from other have to be weighted to correspond to samples for self.

nsoutdims

Non-singleton outcome dimensions (dimension larger one)

nsoutpos

Sites with non-singleton outcome dimension (dimension larger one)

outdims

Outcome dimensions

pack_samples(samples, dtype=None)

Pack samples into one integer per sample

Store one sample in a single integer instead of a list of integers with length len(self.nsoutdims). Example:

>>> p = pauli_mpp(nr_sites=2, local_dim=2)
>>> p.outdims
(6, 6)
>>> p.pack_samples(np.array([[0, 1], [1, 0], [1, 2], [5, 5]]))
array([ 1,  6,  8, 35])

pmf(state, mode='auto')

Compute the POVM’s probability mass function for state

If you want to compute the probabilities for reduced states of state, you can use MPPovm.expectations() instead of this function.

Parameters: state (mp.MPArray) – A quantum state as MPA. Must have the same length as self. mode – ‘mps’, ‘mpdo’ or ‘pmps’. See MPPovm.expectations(). Probabilities as MPArray
pmf_as_array(state, mode='auto', eps=1e-10, impl='auto')

Compute the POVM’s PMF for state as full array

Parameters: See MPPovm.pmf().

Parameters: impl – ‘auto’, ‘default’, ‘pmps-symm’ or ‘pmps-ltr’. ‘auto’ will use ‘pmps-symm’ for mode ‘pmps’ and ‘default’ otherwise. PMF as shape self.nsoutdims ndarray

The resulting (real or complex) probabilities pmf are passed through check_pmf(pmf, eps, eps) before being returned.

pmfs_as_array(states, mode, asarray=False, eps=1e-10)

Todo

probability_map

Map that takes a raveled MPDO to the POVM probabilities

You can use MPPovm.expectations() or MPPovm.pmf() as convenient wrappers around this map.

If rho is a matrix product density operator (MPDO), then

mp.dot(a_povm.probability_map, rho.ravel())


produces the POVM probabilities as MPA (similar to mpnum.povm.localpovm.POVM.probability_map()).

repeat(nr_sites)

Construct a longer MP-POVM by repetition

The resulting POVM will have length nr_sites. If nr_sites is not an integer multiple of len(self), self must factorize (have bond dimension one) at the position where it will be cut. For example, consider the tensor product MP-POVM of Pauli X and Pauli Y. Calling repeat(nr_sites=5) will construct the tensor product POVM XYXYX:

>>> import mpnum as mp
>>> import mpnum.povm as mpp
>>> x, y = (mpp.MPPovm.from_local_povm(lp(3), 1) for lp in
...         (mpp.x_povm, mpp.y_povm))
>>> xy = mp.outer([x, y])
>>> xyxyx = mp.outer([x, y, x, y, x])
>>> mp.norm(xyxyx - xy.repeat(5)) <= 1e-10
True

sample(rng, state, n_samples, method='cond', n_group=1, mode='auto', pack=False, eps=1e-10)

Random sample from self on a quantum state

Parameters: state (mp.MPArray) – A quantum state as MPA (see mode) n_samples – Number of samples to create method – Sampling method (‘cond’ or ‘direct’, see below) n_group – Number of sites to sample at a time in conditional sampling. mode – Passed to MPPovm.expectations() eps – Threshold for small values to be treated as zero.

Two different sampling methods are available:

• Direct sampling (method=’direct’): Compute probabilities for all outcomes and sample from the full probability distribution. Usually faster than conditional sampling for measurements on a small number of sites. Requires memory linear in the number of possible outcomes.
• Conditional sampling (method=’cond’): Sample outcomes on all sites by sampling from conditional outcome probabilities on at most n_group sites at a time. Requires memory linear in the number of outcomes on n_group sites. Useful for measurements which act on large parts of a system (e.g. Pauli X on each spin).
Returns: ndarray samples with shape (n_samples, len(self.nsoutdims))

The i-th sample is given by samples[i, :]. samples[i, j] is the outcome for the j-th non-singleton output dimension of self.

unpack_samples(samples)

Unpack samples into several integers per sample

Inverse of MPPovm.pack_samples(). Example:

>>> p = pauli_mpp(nr_sites=2, local_dim=2)
>>> p.outdims
(6, 6)
>>> p.unpack_samples(np.array([0, 6, 7, 12]))
array([[0, 0],
[1, 0],
[1, 1],
[2, 0]], dtype=uint8)

class mpnum.povm.mppovm.MPPovmList(mppseq)

Bases: object

A list of Matrix Product POVMs

This class allows you to

• Conveniently obtain samples and estimated or exact probabilities for a list of MPPovms
• Estimate probabilities from samples for a different MPPovmList
• Estimate linear functions of probabilities of an MPPovmList from samples for a different MPPovmList
__init__(mppseq)

Construct a MPPovmList

Parameters: mppseq – An iterable of MPPovm objects

All MPPovms must have the same number of sites.

_lfun_estimator(other, coeff, n_samples, eps)

Compute the estimator used by MPPovmList.est_lfun_from()

Parameters: See MPPovmList.est_lfun_from() for other and coeff. See MPPovm._mppl_lfun_estimator() for n_samples.

Returns: (n_sam, est_coeff, est_funs): est_coeff[i] and est_funs[i] specify an estimator in the format used by MPPovm.est_lfun() on other.mpps[i]. n_sam is a shape self.nsoutdims array providing the number of samples available for each probability of self; zero indicates that a probability cannot be estimated.

This method aggregates the results from MPPovm._mppl_lfun_estimator() on each self.mpps[i].

block(nr_sites)

Embed MP-POVMs on local blocks

This function calls MPPovm.block(nr_sites)() for each MP-POVM in the list. Embedded MP-POVMs at the same position appear consecutively in the returned list:

>>> import mpnum as mp
>>> import mpnum.povm as mpp
>>> ldim = 3
>>> x, y = (mpp.MPPovm.from_local_povm(lp(ldim), 1) for lp in
...         (mpp.x_povm, mpp.y_povm))
>>> e = mpp.MPPovm.eye([ldim])
>>> xx = mp.outer([x, x])
>>> xy = mp.outer([x, y])
>>> mppl = mpp.MPPovmList((xx, xy))
>>> xxe = mp.outer([x, x, e])
>>> xye = mp.outer([x, y, e])
>>> exx = mp.outer([e, x, x])
>>> exy = mp.outer([e, x, y])
>>> expect = (xxe, xye, exx, exy)
>>> [abs(mp.norm(a - b)) <= 1e-10
...  for a, b in zip(mppl.block(3).mpps, expect)]
[True, True, True, True]

block_pmfs_as_array(state, mode, asarray=False, eps=1e-10, **redarg)

Todo

est_lfun(coeff, funs, samples, weights=None, eps=1e-10)

Estimate a linear combination of functions of POVM outcomes

Parameters: coeff – Iterable of coefficient lists funs – Iterable of function lists samples – Iterable of samples weights – Iterable of weight lists or None

The i-th item from these parameters is passed to MPPovm.est_lfun() on self.mpps[i].est_lfun.

Returns: (est, var): Estimated value est and estimated variance var of the estimate est
est_lfun_from(other, coeff, samples, eps=1e-10)

Estimate a linear function from samples for another MPPovmList

The function to estimate is a linear function of the probabilities of self and it is specified by coeff. Its true expectation value and variance are returned by MPPovmList.lfun_from(). First, an estimator is constructed using MPPovmList._lfun_estimator() and this estimator is passed to MPPovm.est_lfun() to obtain the estimate. See Linear combinations of functions of POVM outcomes for more details.

Parameters: other (MPPovmList) – Another MP-POVM list coeff – A sequence of shape self.mpps[i].nsoutdims coefficients which specify the function to estimate samples – A sequence of samples for other (est, var): Estimated value and estimated variance of the estimated value. Return (np.nan, np.nan) if other is not sufficient to estimate the function.
est_pmf(samples, normalized=True, eps=1e-10)

Estimate PMF from samples

Returns an iterator over results from MPPovm.est_pmf() (see there).

est_pmf_from(other, samples, eps=1e-10)

Estimate PMF from samples of another MPPovmList

Parameters: other (MPPovmList) – A different MPPovmList samples – Samples from other Iterator over (p_est, n_samples_used) from MPPovm.est_pmf_from_mpps().
lfun(coeff, funs, state, mode='auto', eps=1e-10)

Evaluate a linear combination of functions of POVM outcomes

coeff[i] and funs[i] are passed to MPPovm.lfun() on self.mpps[i]. funs = None is treated as [None] * len(self.mpps). state and mode are passed to MPPovm.pmf().

Returns: (value, var): Expectation value and variance of the expectation value
lfun_from(other, coeff, state, mode='auto', other_weights=None, eps=1e-10)

Evaluate a linear combination of POVM probabilities

This function computes the same expectation value as MPPovmList.lfun() if supplied with funs = None, but it computes the variance for a different estimation procedure: It uses weighted averages of POVM probabilities from other to obtain the necessary POVM probabilities for self (the same is done in MPPovmList.est_lfun_from()).

The parameter coeff is explained in MPPovmList.est_lfun_from(). state and mode are passed to MPPovm.pmf().

You can supply the array other_weights to determine the weighted average used when a probability in a POVM in self can be estimated from probabilities in multiple different POVMs in other.

Returns: (value, var): Expectation value and variance of the expectation value. Return (np.nan, np.nan) if other is not sufficient to estimate the function.
pack_samples(samples)

Pack samples into one integer per sample

Returns: Iterator over output from MPPovm.pack_samples()
pmf(state, mode='auto')

Compute the probability mass functions of all MP-POVMs

Parameters: state – A quantum state as MPA mode – Passed to MPPovm.expectations() Iterator over probabilities as MPArrays
pmf_as_array(state, mode='auto', eps=1e-10)

Compute the PMF of all MP-POVMs as full arrays

Parameters: See MPPovmList.pmf(). Sanity checks: See MPPovm.pmf_as_array().

Returns: Iterator over probabilities as ndarrays
pmfs_as_array(states, mode, asarray=False, eps=1e-10)

Todo

repeat(nr_sites)

Construct longer MP-POVMs by repeating each MP-POVM

This function calls MPPovm.repeat(nr_sites) for each MP-POVM in the list.

For example, pauli_mpps() for local_dim > 3 (i.e. without Z) and two sites returns POVMs for the four tensor product observables XX, XY, YX and YY:

>>> import mpnum as mp
>>> import mpnum.povm as mpp
>>> block_sites = 2
>>> ldim = 3
>>> x, y = (mpp.MPPovm.from_local_povm(lp(ldim), 1) for lp in
...         (mpp.x_povm, mpp.y_povm))
>>> pauli = mpp.pauli_mpps(block_sites, ldim)
>>> expect = (
...     mp.outer((x, x)),
...     mp.outer((x, y)),
...     mp.outer((y, x)),
...     mp.outer((y, y)),
... )
>>> [abs(mp.norm(a - b)) <= 1e-10 for a, b in zip(pauli.mpps, expect)]
[True, True, True, True]


Calling repeat(5) then returns the following MPPovmList:

>>> expect = (
...     mp.outer((x, x, x, x, x)),
...     mp.outer((x, y, x, y, x)),
...     mp.outer((y, x, y, x, y)),
...     mp.outer((y, y, y, y, y)),
... )
>>> [abs(mp.norm(a - b)) <= 1e-10
...  for a, b in zip(pauli.repeat(5).mpps, expect)]
[True, True, True, True]

sample(rng, state, n_samples, method, n_group=1, mode='auto', pack=False, eps=1e-10)

Random sample from all MP-POVMs on a quantum state

Parameters: See MPPovm.sample().

Return value: Iterable of return values from MPPovm.sample().

unpack_samples(samples)

Unpack samples into several integers per sample

Returns: Iterator over output from MPPovm.unpack_samples()
mpnum.povm.mppovm.pauli_mpp(nr_sites, local_dim)

Pauli POVM tensor product as MP-POVM

The resulting MP-POVM will contain all tensor products of the elements of the local Pauli POVM from mpp.pauli_povm().

Parameters: nr_sites (int) – Number of sites of the returned MP-POVM local_dim (int) – Local dimension MPPovm

For example, for two qubits the (1, 3) measurement outcome is minus X on the first and minus Y on the second qubit:

>>> nr_sites = 2
>>> local_dim = 2
>>> pauli = pauli_mpp(nr_sites, local_dim)
>>> xy = np.kron([1, -1], [1, -1j]) / 2
>>> xyproj = np.outer(xy, xy.conj())
>>> proj = pauli.get_phys([1, 3], astype=mp.MPArray) \
...             .to_array_global().reshape((4, 4))
>>> abs(proj - xyproj / 3**nr_sites).max() <= 1e-10
True


The prefactor 1 / 3**nr_sites arises because X, Y and Z are in a single POVM.

mpnum.povm.mppovm.pauli_mpps(nr_sites, local_dim)

Pauli POVM tensor product as MP-POVM list

The returned MPPovmList contains all tensor products of the single-site X, Y (and Z if local_dim == 2) POVMs:

>>> import mpnum as mp
>>> import mpnum.povm as mpp
>>> block_sites = 2
>>> ldim = 3
>>> x, y = (mpp.MPPovm.from_local_povm(lp(ldim), 1) for lp in
...         (mpp.x_povm, mpp.y_povm))
>>> pauli = mpp.pauli_mpps(block_sites, ldim)
>>> expect = (
...     mp.outer((x, x)),
...     mp.outer((x, y)),
...     mp.outer((y, x)),
...     mp.outer((y, y)),
... )
>>> [abs(mp.norm(a - b)) <= 1e-10 for a, b in zip(pauli.mpps, expect)]
[True, True, True, True]

Parameters: nr_sites (int) – Number of sites of the returned MP-POVMs local_dim (int) – Local dimension MPPovmList

## Internal utility functions¶

### General tools¶

General helper functions for dealing with arrays (esp. for quantum mechanics)

mpnum._tools.block_diag(summands, axes=(0, 1))

Block-diagonal sum for n-dimensional arrays.

Perform something like a block diagonal sum (if len(axes) == 2) along the specified axes. All other axes must have identical sizes.

Parameters: axes – Along these axes, perform a block-diagonal sum. Can be negative.
>>> a = np.arange(8).reshape((2, 2, 2))
>>> b = np.arange(8, 16).reshape((2, 2, 2))
>>> a
array([[[0, 1],
[2, 3]],

[[4, 5],
[6, 7]]])
>>> b
array([[[ 8,  9],
[10, 11]],

[[12, 13],
[14, 15]]])
>>> block_diag((a, b), axes=(1, -1))
array([[[ 0,  1,  0,  0],
[ 2,  3,  0,  0],
[ 0,  0,  8,  9],
[ 0,  0, 10, 11]],

[[ 4,  5,  0,  0],
[ 6,  7,  0,  0],
[ 0,  0, 12, 13],
[ 0,  0, 14, 15]]])

mpnum._tools.check_nonneg_trunc(values, imag_eps=1e-10, real_eps=1e-10, real_trunc=0.0)

Check that values are real and non-negative

Parameters: values (np.ndarray) – An ndarray of complex or real values (or a single value). values is modified in-place unless values is complex. A single value is also accepted. imag_eps (float) – Raise an error if imaginary parts with modulus larger than imag_eps are present. real_eps (float) – Raise an error if real parts smaller than -real_eps are present. Replace all remaining negative values by zero. real_trunc (float) – Replace positive real values smaller than or equal to real_trunc by zero. An ndarray of real values (or a single real value).

If values is an array with complex type, a new array is returned. If values is an array with real type, it is modified in-place and returned.

mpnum._tools.check_pmf(values, imag_eps=1e-10, real_eps=1e-10, real_trunc=0.0)

Check that values are real probabilities

See check_nonneg_trunc() for parameters and return value. In addition, we check that abs(values.sum() - 1.0) is smaller than or equal to real_eps and divide values by values.sum() afterwards.

mpnum._tools.compression_svd(array, bdim, direction='right', retproj=False)

Re-implement MPArray.compress(‘svd’) but on the level of the full array representation, i.e. it truncates the Schmidt-decompostion on each bipartition sequentially.

Parameters: mpa – Array to compress bdim – Compress to this bond dimension direction – ‘right’ means sweep from left to right, ‘left’ vice versa retproj – Besides the compressed array, also return the projectors on the appropriate eigenspaces Result as numpy.ndarray
mpnum._tools.global_to_local(array, sites, left_skip=0, right_skip=0)

Converts a general sites-local array with fixed number p of physical legs per site from the global form

A[i_1,..., i_N, j_1,..., j_N, ...]

(i.e. grouped by physical legs) to the local form

A[i_1, j_1, ..., i_2, j_2, ...]

(i.e. grouped by site).

Parameters: array (np.ndarray) – Array with ndim, such that ndim % sites = 0 sites (int) – Number of distinct sites left_skip (int) – Ignore that many axes on the left right_skip (int) – Ignore that many axes on the right Array with same ndim as array, but reshaped
>>> global_to_local(np.zeros((1, 2, 3, 4, 5, 6)), 3).shape
(1, 4, 2, 5, 3, 6)
>>> global_to_local(np.zeros((1, 2, 3, 4, 5, 6)), 2).shape
(1, 3, 5, 2, 4, 6)

mpnum._tools.local_to_global(array, sites, left_skip=0, right_skip=0)

Inverse of local_to_global

Parameters: array (np.ndarray) – Array with ndim, such that ndim % sites = 0 sites (int) – Number of distinct sites left_skip (int) – Ignore that many axes on the left right_skip (int) – Ignore that many axes on the right Array with same ndim as array, but reshaped
>>> ltg, gtl = local_to_global, global_to_local
>>> ltg(gtl(np.zeros((1, 2, 3, 4, 5, 6)), 3), 3).shape
(1, 2, 3, 4, 5, 6)
>>> ltg(gtl(np.zeros((1, 2, 3, 4, 5, 6)), 2), 2).shape
(1, 2, 3, 4, 5, 6)


Transform all or only the inner axes:

>>> ltg = local_to_global
>>> ltg(np.zeros((1, 2, 3, 4, 5, 6)), 3).shape
(1, 3, 5, 2, 4, 6)
>>> ltg(np.zeros((1, 2, 3, 4, 5, 6)), 2, left_skip=1, right_skip=1).shape
(1, 2, 4, 3, 5, 6)

mpnum._tools.matdot(A, B, axes=((-1, ), (0, )))

np.tensordot with sane defaults for matrix multiplication

mpnum._tools.mkron(*args)

np.kron() with an arbitrary number of n >= 1 arguments

mpnum._tools.partial_trace(array, traceout)

Return the partial trace of an array over the sites given in traceout.

Parameters: array (np.ndarray) – Array in global form (see global_to_local() above) with exactly 2 legs per site traceout – List of sites to trace out, must be in _ascending_ order Partial trace over input array
mpnum._tools.truncated_svd(A, k)

Compute the truncated SVD of the matrix A i.e. the k largest singular values as well as the corresponding singular vectors. It might return less singular values/vectors, if one dimension of A is smaller than k.

In the background it performs a full SVD. Therefore, it might be inefficient when k is much smaller than the dimensions of A.

Parameters: A – A real or complex matrix k – Number of singular values/vectors to compute u, s, v, where u: left-singular vectors s: singular values v: right-singular vectors
mpnum._tools.verify_real_nonnegative(values, zero_tol=1e-06, zero_cutoff=None)

Deprecated; use check_nonneg_trunc() instead

### NumPy ndarray with named axes¶

A numpy.ndarray with axis names

Access as mpnum.named_ndarray.

class mpnum._named_ndarray.named_ndarray(array, axisnames)

Bases: object

Associate names to the axes of a ndarray.

Property axisnames:
The names of the axes.

All methods which return arrays return named_ndarray instances.

Method axispos(axisname):
Return the position of the named axis
Method rename(translate):
Rename axes
Method conj():Return the complex conjugate array
Method to_array(name_order):
Return a ndarray with axis order specified by name_order.
Method tensordot(other, axes):
numpy.tensordot() with axis names instead of axis indices
axisnames

The names of the array

axispos(axisname)

Return the position of an axis.

conj()

Complex conjugate as named_ndarray.

rename(translate)

Rename axes.

An error will be raised if the resulting list of names contains duplicates.

Parameters: translate – List of (old_name, new_name) axis name pairs.
tensordot(other, axes)

Compute tensor dot product along named axes.

An error will be raised if the remaining axes of self and other contain duplicate names.

Parameters: other – Another named_ndarray instance axes – List of axis name pairs (self_name, other_name) to be contracted Result as named_ndarray
to_array(name_order)

Convert to a normal ndarray with given axes ordering.

Parameters: name_order – Order of axes in the array

### Unit test utilities¶

Auxiliary functions useful for writing tests

mpnum._testing._assert_lcanonical(ltens, msg='')
mpnum._testing._assert_rcanonical(ltens, msg='')
mpnum._testing.assert_correct_normalization(lt, lnormal_target=None, rnormal_target=None)

Verify that normalization info in lt is correct

We check that lt is at least as normalized as specified by the information. lt being “more normalized” than the information specifies is admissible and not treated as an error.

If [lr]normal_target are not None, verify that normalization info is exactly equal to the given values.

mpnum._testing.assert_mpa_almost_equal(mpa1, mpa2, full=False, **kwargs)

Verify that two MPAs are almost equal

mpnum._testing.assert_mpa_identical(mpa1, mpa2, decimal=inf)

Verify that two MPAs are complety identical

mpnum._testing.mpo_to_global(mpo)

Convert mpo to dense global array

Todo

### Todo list (autogenerated)¶

Todo

As it is now, e.g. MPArray.__imul__() modifies items from self._ltens. This requires e.g. outer() to take copies of the local tensors. The data model seems to be that an MPArray instance owns its local tensors and everyone else, including each new MPArray instance, must take copies. Is this correct?

(The original entry is located in docstring of mpnum.mparray.MPArray, line 18.)

Todo

If we enable all special members (e.g. __len__) to be shown, we get things like __dict__ with very long contents. Therefore, special members are hidden at the moment, but we should show the interesting one.

(The original entry is located in docstring of mpnum.mparray.MPArray, line 26.)

Todo

This table needs cell borders in the HTML output (-> CSS) and the tabularcolumns command doesn’t work.

(The original entry is located in docstring of mpnum.mparray.regular_slices, line 24.)

Todo

Are derived classes MPO/MPS/PMPS of any help?

(The original entry is located in docstring of mpnum.mpsmpo, line 99.)

Todo

I am not sure the current definition of PMPS is the most elegant for our purposes...

(The original entry is located in docstring of mpnum.mpsmpo, line 101.)

Todo

(The original entry is located in docstring of mpnum.mpsmpo.reductions, line 1.)

Todo

Add information on how the runtime of mineig() and mineig_sum() with the the different bond dimensions.

(The original entry is located in docstring of mpnum.linalg.mineig_sum, line 15.)

Todo

Explain the details of the variance estimation, in particular the difference between the variances returned from MPPovmList.lfun() and MPPovmList.lfun_from(). Check the mean square error.

Add a good references explaining all facts mentioned above and for further reading.

Document the runtime and memory cost of the functions.

(The original entry is located in docstring of mpnum.povm.mppovm, line 116.)

Todo

This class should provide a function which returns expectation values as full array. (Even though computing expectation values using the POVM struture brings advantages, we usually need the result as full array.) This function should also replace small negative probabilities by zero and normalize the sum of all probabilities to unity (if the deviation is non-zero but small). The same checks should also be implemented in localpovm.POVM.

(The original entry is located in docstring of mpnum.povm.mppovm.MPPovm, line 19.)

Todo

Right now we use this class for multi-site POVMs with elements obtained from every possible combination of the elements of single-site POVMs: The POVM index is split across all sites. Explore whether and how this concept can also be useful in other cases.

(The original entry is located in docstring of mpnum.povm.mppovm.MPPovm, line 28.)

Todo

(The original entry is located in docstring of mpnum.povm.mppovm.MPPovm.block_pmfs_as_array, line 1.)

Todo

(The original entry is located in docstring of mpnum.povm.mppovm.MPPovm.pmfs_as_array, line 1.)

Todo

(The original entry is located in docstring of mpnum.povm.mppovm.MPPovmList.block_pmfs_as_array, line 1.)

Todo

make livehtml` (based on sphinx-autobuild) does not rebuild this list.