qml.labs.resource_estimation.ResourceProd

class ResourceProd(res_ops, wires=None)

Bases: ResourceOperator

Resource class for the symbolic Prod operation.

A symbolic class used to represent a product of some base operations.

Parameters:

• res_ops (tuple[ResourceOperator]) – A tuple of resource operators or a nested tuple of resource operators and counts.

• wires (Sequence[int], optional) – the wires the operation acts on

Resources:

This symbolic class represents a product of operations. The resources are defined trivially as the counts for each operation in the product.

See also

Prod

Example

The product of operations can be constructed from a list of operations or a nested tuple where each operator is accompanied with the number of counts. Note, each operation in the product must be a valid ResourceOperator

We can construct a product operator as follows:

>>> factors = [plre.ResourceX(), plre.ResourceY(), plre.ResourceZ()]
>>> prod_xyz = plre.ResourceProd(factors)
>>>
>>> print(plre.estimate(prod_xyz))
--- Resources: ---
Total qubits: 1
Total gates : 3
Qubit breakdown:
 clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
 {'X': 1, 'Y': 1, 'Z': 1}

We can also specify the factors as a tuple with

>>> factors = [(plre.ResourceX(), 2), (plre.ResourceZ(), 3)]
>>> prod_x2z3 = plre.ResourceProd(factors)
>>>
>>> print(plre.estimate(prod_x2z3))
--- Resources: ---
Total qubits: 1
Total gates : 5
Qubit breakdown:
 clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
 {'X': 2, 'Z': 3}

Attributes

num_wires
resource_keys
resource_params	Returns a dictionary containing the minimal information needed to compute the resources.

num_wires = 1

resource_keys = {'cmpr_factors_and_counts', 'num_wires'}

resource_params

Returns a dictionary containing the minimal information needed to compute the resources.

Returns:

A dictionary containing the resource parameters:

• num_wires (int): the number of wires this operator acts upon

• cmpr_factors_and_counts (Tuple[Tuple[~.labs.resource_estimation.CompressedResourceOp, int]]): A sequence of tuples containing the operations, in the compressed representation, and a count for how many times they are repeated corresponding to the factors in the product.

Return type:

dict

Methods

adjoint_resource_decomp(*args, **kwargs)	Returns a list representing the resources for the adjoint of the operator.
controlled_resource_decomp(...)	Returns a list representing the resources for a controlled version of the operator.
dequeue(op_to_remove[, context])	Remove the given resource operator(s) from the Operator queue.
pow_resource_decomp(pow_z, *args, **kwargs)	Returns a list representing the resources for an operator raised to a power.
queue([context])	Append the operator to the Operator queue.
resource_decomp(cmpr_factors_and_counts, ...)	Returns a list representing the resources of the operator.
resource_rep(cmpr_factors_and_counts[, ...])	Returns a compressed representation containing only the parameters of the Operator that are needed to compute a resource estimation.
resource_rep_from_op()	Returns a compressed representation directly from the operator
tracking_name(*args, **kwargs)	Returns a name used to track the operator during resource estimation.
tracking_name_from_op()	Returns the tracking name built with the operator's parameters.

classmethod adjoint_resource_decomp(*args, **kwargs)

Returns a list representing the resources for the adjoint of the operator.

classmethod controlled_resource_decomp(ctrl_num_ctrl_wires, ctrl_num_ctrl_values, *args, **kwargs)

Returns a list representing the resources for a controlled version of the operator.

Parameters:

• ctrl_num_ctrl_wires (int) – the number of qubits the operation is controlled on

• ctrl_num_ctrl_values (int) – the number of control qubits, that are controlled when in the \(|0\rangle\) state

static dequeue(op_to_remove, context=<class 'pennylane.queuing.QueuingManager'>)

Remove the given resource operator(s) from the Operator queue.

classmethod pow_resource_decomp(pow_z, *args, **kwargs)

Returns a list representing the resources for an operator raised to a power.

Parameters:

pow_z (int) – exponent that the operator is being raised to

queue(context=<class 'pennylane.queuing.QueuingManager'>)

Append the operator to the Operator queue.

classmethod resource_decomp(cmpr_factors_and_counts, num_wires, **kwargs)

Returns a list representing the resources of the operator. Each object represents a quantum gate and the number of times it occurs in the decomposition.

Parameters:

• cmpr_factors_and_counts (Tuple[Tuple[CompressedResourceOp, int]]) – A sequence of tuples containing the operations, in the compressed representation, and a count for how many times they are repeated corresponding to the factors in the product.

• num_wires (int) – the number of wires this operator acts upon

Resources:

This symbolic class represents a product of operations. The resources are defined trivially as the counts for each operation in the product.

Returns:

A list of GateCount objects, where each object represents a specific quantum gate and the number of times it appears in the decomposition.

Return type:

list[GateCount]

See also

Prod

Example

The product of operations can be constructed as follows. Note, each operation in the product must be a valid ResourceOperator

>>> factors = [plre.ResourceX(), plre.ResourceY(), plre.ResourceZ()]
>>> prod_xyz = plre.ResourceProd(factors)
>>>
>>> print(plre.estimate(prod_xyz))
--- Resources: ---
Total qubits: 1
Total gates : 3
Qubit breakdown:
clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
{'X': 1, 'Y': 1, 'Z': 1}

We can also specify the factors as a tuple with

>>> factors = [(plre.ResourceX(), 2), (plre.ResourceZ(), 3)]
>>> prod_x2z3 = plre.ResourceProd(factors)
>>>
>>> print(plre.estimate(prod_x2z3))
--- Resources: ---
Total qubits: 1
Total gates : 5
Qubit breakdown:
clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
{'X': 2, 'Z': 3}

classmethod resource_rep(cmpr_factors_and_counts, num_wires=None)

Returns a compressed representation containing only the parameters of the Operator that are needed to compute a resource estimation.

Parameters:

• cmpr_factors_and_counts (Tuple[Tuple[CompressedResourceOp, int]]) – A sequence of tuples containing the operations, in the compressed representation, and a count for how many times they are repeated corresponding to the factors in the product.

• num_wires (int) – an optional integer representing the number of wires this operator acts upon

Returns:

the operator in a compressed representation

Return type:

CompressedResourceOp

resource_rep_from_op()

Returns a compressed representation directly from the operator

classmethod tracking_name(*args, **kwargs)

Returns a name used to track the operator during resource estimation.

tracking_name_from_op()

Returns the tracking name built with the operator’s parameters.

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