qml.labs.resource_estimation.ResourcePow

class ResourcePow(base_op, z)

Bases: ResourceOperator

Resource class for the symbolic Pow operation.

A symbolic class used to represent some base operation raised to a power.

Parameters:

• base_op (ResourceOperator) – The operator that we want to exponentiate.

• z (float) – the exponent (default value is 1)

Resources:

The resources are determined as follows. If the power \(z = 0\), then we have the identitiy gate and we have no resources. If the base operation class base_class implements the .pow_resource_decomp() method, then the resources are obtained from this. Otherwise, the resources of the operation raised to the power \(z\) are given by extracting the base operation’s resources (via .resources()) and raising each operation to the same power.

See also

PowOperation

Example

The operation raised to a power \(z\) can be constructed like this:

>>> z = plre.ResourceZ()
>>> z_2 = plre.ResourcePow(z, 2)
>>> z_5 = plre.ResourcePow(z, 5)

We obtain the expected resources.

>>> print(plre.estimate(z_2, gate_set={"Identity", "Z"}))
--- Resources: ---
Total qubits: 1
Total gates : 1
Qubit breakdown:
clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
{'Identity': 1}
>>>
>>> print(plre.estimate(z_5, gate_set={"Identity", "Z"}))
--- Resources: ---
Total qubits: 1
Total gates : 1
Qubit breakdown:
clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
{'Z': 1}

Attributes

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

num_wires = 1

resource_keys = {'base_cmpr_op', 'z'}

resource_params

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

Returns:

A dictionary containing the resource parameters:

• base_class (Type[~.pennylane.labs.resource_estimation.ResourceOperator]): The class type of the base operator to be raised to some power.

• base_params (dict): the resource parameters required to extract the cost of the base operator

• z (int): the power that the operator is being raised to

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, base_cmpr_op, z, ...)	Returns a list representing the resources of the operator.
queue([context])	Append the operator to the Operator queue.
resource_decomp(base_cmpr_op, z, **kwargs)	Returns a list representing the resources of the operator.
resource_rep(base_cmpr_op, z)	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(base_cmpr_op, z)	Returns the tracking name built with the operator's parameters.
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, base_cmpr_op, z, **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:

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

• base_cmpr_op (CompressedResourceOp) – A compressed resource representation for the operator we want to exponentiate.

• z (float) – the exponent that the base operator is being raised to (default value is 1)

Resources:

The resources are derived by simply adding together the \(z\) exponent and the \(z_{0}\) exponent into a single instance of ResourcePow gate, raising the base operator to the power \(z + z_{0}\).

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]

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

Append the operator to the Operator queue.

classmethod resource_decomp(base_cmpr_op, z, **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:

• base_cmpr_op (CompressedResourceOp) – A compressed resource representation for the operator we want to exponentiate.

• z (float) – the exponent (default value is 1)

Resources:

The resources are determined as follows. If the power \(z = 0\), then we have the identitiy gate and we have no resources. If the base operation class base_class implements the .pow_resource_decomp() method, then the resources are obtained from this. Otherwise, the resources of the operation raised to the power \(z\) are given by extracting the base operation’s resources (via .resources()) and raising each operation to the same power.

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

PowOperation

Example

The operation raised to a power \(z\) can be constructed like this:

>>> z = plre.ResourceZ()
>>> z_2 = plre.ResourcePow(z, 2)
>>> z_5 = plre.ResourcePow(z, 5)

We obtain the expected resources.

>>> print(plre.estimate(z_2, gate_set={"Identity", "Z"}))
--- Resources: ---
Total qubits: 1
Total gates : 1
Qubit breakdown:
clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
{'Identity': 1}
>>>
>>> print(plre.estimate(z_5, gate_set={"Identity", "Z"}))
--- Resources: ---
Total qubits: 1
Total gates : 1
Qubit breakdown:
clean qubits: 0, dirty qubits: 0, algorithmic qubits: 1
Gate breakdown:
{'Z': 1}

classmethod resource_rep(base_cmpr_op, z)

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

Parameters:

• base_class (Type[ResourceOperator]) – The class type of the base operator to be raised to some power.

• base_params (dict) – the resource parameters required to extract the cost of the base operator

• z (int) – the power that the operator is being raised to

Returns:

the operator in a compressed representation

Return type:

CompressedResourceOp

resource_rep_from_op()

Returns a compressed representation directly from the operator

static tracking_name(base_cmpr_op, z)

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

tracking_name_from_op()

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

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