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qml.FermiSentence

class FermiSentence(operator)[source]

Bases: dict

Immutable dictionary used to represent a Fermi sentence, a linear combination of Fermi words, with the keys as FermiWord instances and the values correspond to coefficients.

>>> w1 = qml.FermiWord({(0, 0) : '+', (1, 1) : '-'})
>>> w2 = qml.FermiWord({(0, 1) : '+', (1, 2) : '-'})
>>> s = qml.FermiSentence({w1 : 1.2, w2: 3.1})
>>> print(s)
1.2 * a⁺(0) a(1)
+ 3.1 * a⁺(1) a(2)

wires

Return wires of the FermiSentence.
wires

Return wires of the FermiSentence.

adjoint()

Return the adjoint of FermiSentence.

clear()

copy()

fromkeys([value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items()

keys()

pop(k[,d])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

simplify([tol])

Remove any FermiWords in the FermiSentence with coefficients less than the threshold tolerance.

to_mat([n_orbitals, format, buffer_size])

Return the matrix representation.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()

adjoint()[source]

Return the adjoint of FermiSentence.

clear() → None.  Remove all items from D.
copy() → a shallow copy of D
fromkeys(value=None, /)

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
pop(k[, d]) → v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

simplify(tol=1e-08)[source]

Remove any FermiWords in the FermiSentence with coefficients less than the threshold tolerance.

to_mat(n_orbitals=None, format='dense', buffer_size=None)[source]

Return the matrix representation.

Parameters:

  • n_orbitals (int or None) – Number of orbitals. If not provided, it will be inferred from the largest orbital index in the Fermi operator

  • format (str) – The format of the matrix. It is “dense” by default. Use “csr” for sparse.

  • buffer_size (int or None) – The maximum allowed memory in bytes to store intermediate results in the calculation of sparse matrices. It defaults to 2 ** 30 bytes that make 1GB of memory. In general, larger buffers allow faster computations.

Returns:

Matrix representation of the FermiSentence.

Return type:

NumpyArray

Example

>>> fw1 = qml.FermiWord({(0, 0): "+", (1, 1): "-"})
>>> fw2 = qml.FermiWord({(0, 0): "+", (1, 0): "-"})
>>> fs = qml.FermiSentence({fw1: 1.2, fw2: 3.1})
>>> fs.to_mat()
array([[0. +0.j, 0. +0.j, 0. +0.j, 0. +0.j],
       [0. +0.j, 0. +0.j, 0. +0.j, 0. +0.j],
       [0. +0.j, 1.2+0.j, 3.1+0.j, 0. +0.j],
       [0. +0.j, 0. +0.j, 0. +0.j, 3.1+0.j]])
update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
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