qml.sum

sum(*summands, grouping_type=None, method='lf', id=None, lazy=True)

Construct an operator which is the sum of the given operators.

Parameters

*summands (tuple[Operator]) – the operators we want to sum together.

Keyword Arguments

• id (str or None) – id for the Sum operator. Default is None.

• lazy=True (bool) – If lazy=False, a simplification will be performed such that when any of the operators is already a sum operator, its operands (summands) will be used instead.

• grouping_type (str) – The type of binary relation between Pauli words used to compute the grouping. Can be 'qwc', 'commuting', or 'anticommuting'.

• method (str) – The graph colouring heuristic to use in solving minimum clique cover for grouping. It can be 'lf' (Largest First), 'rlf' (Recursive Largest First), 'dsatur' (Degree of Saturation), or 'gis' (Greedy Independent Set). This keyword argument is ignored if grouping_type is None.

Returns

The operator representing the sum of summands.

Return type

Sum

Note

This operator supports batched operands:

>>> op = qml.sum(qml.RX(np.array([1, 2, 3]), wires=0), qml.X(1))
>>> op.matrix().shape
(3, 4, 4)

But it doesn’t support batching of operators:

>>> op = qml.sum(np.array([qml.RX(0.4, 0), qml.RZ(0.3, 0)]), qml.Z(0))
AttributeError: 'numpy.ndarray' object has no attribute 'wires'

Note

If grouping is requested, the computed groupings are stored as a list of list of indices in Sum.grouping_indices. The indices refer to the operators and coefficients returned by Sum.terms(), not Sum.operands, as these are not guaranteed to be equivalent.

See also

Sum

Example

>>> summed_op = qml.sum(qml.X(0), qml.Z(0))
>>> summed_op
X(0) + Z(0)
>>> summed_op.matrix()
array([[ 1,  1],
       [ 1, -1]])

Grouping

Grouping information can be collected during construction using the grouping_type and method keyword arguments. For example:

import pennylane as qml

a = qml.s_prod(1.0, qml.X(0))
b = qml.s_prod(2.0, qml.prod(qml.X(0), qml.X(1)))
c = qml.s_prod(3.0, qml.Z(0))

op = qml.sum(a, b, c, grouping_type="qwc")

>>> op.grouping_indices
((2,), (0, 1))

grouping_type can be "qwc" (qubit-wise commuting), "commuting", or "anticommuting", and method can be 'lf' (Largest First), 'rlf' (Recursive Largest First), 'dsatur' (Degree of Saturation), or 'gis' (Greedy Independent Set). To see more details about how these affect grouping, see Pauli Graph Colouring and compute_partition_indices().

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