qml.labs.resource_estimation.ResourceOperator

class ResourceOperator(*args, wires=None, **kwargs)

Bases: ABC

Base class to represent quantum operators according to the set of information required for resource estimation.

A ResourceOperator is uniquely defined by its name (the class type) and its resource parameters (op.resource_params).

Example

This example shows how to create a custom ResourceOperator class for resource estimation. We use QFT as a well known gate for simplicity.

from pennylane.labs import resource_estimation as plre

class ResourceQFT(plre.ResourceOperator):

    resource_keys = {"num_wires"}  # the only parameter that its resources depend upon.

    def __init__(self, num_wires, wires=None):  # wire labels are optional
        self.num_wires = num_wires
        super().__init__(wires=wires)

    @property
    def resource_params(self) -> dict:        # The keys must match the `resource_keys`
        return {"num_wires": self.num_wires}  # and values obtained from the operator.

    @classmethod
    def resource_rep(cls, num_wires):             # Takes the `resource_keys` as input
        params = {"num_wires": num_wires}         #   and produces a compressed
        return plre.CompressedResourceOp(cls, params)  # representation of the operator

    @classmethod
    def default_resource_decomp(cls, num_wires, **kwargs):  # `resource_keys` are input

        # Get compressed reps for each gate in the decomposition:

        swap = plre.resource_rep(plre.ResourceSWAP)
        hadamard = plre.resource_rep(plre.ResourceHadamard)
        ctrl_phase_shift = plre.resource_rep(plre.ResourceControlledPhaseShift)

        # Figure out the associated counts for each type of gate:

        swap_counts = num_wires // 2
        hadamard_counts = num_wires
        ctrl_phase_shift_counts = num_wires*(num_wires - 1) // 2

        return [                                  # Return the decomposition
            plre.GateCount(swap, swap_counts),
            plre.GateCount(hadamard, hadamard_counts),
            plre.GateCount(ctrl_phase_shift, ctrl_phase_shift_counts),
        ]

Which can be instantiated as a normal operation, but now contains the resources:

>>> op = ResourceQFT(num_wires=3)
>>> print(plre.estimate_resources(op, gate_set={'Hadamard', 'SWAP', 'ControlledPhaseShift'}))
--- Resources: ---
Total qubits: 3
Total gates : 7
Qubit breakdown:
 clean qubits: 0, dirty qubits: 0, algorithmic qubits: 3
Gate breakdown:
 {'SWAP': 1, 'Hadamard': 3, 'ControlledPhaseShift': 3}

Attributes

num_wires
resource_keys
resource_params	A dictionary containing the minimal information needed to compute a resource estimate of the operator's decomposition.

num_wires = 0

resource_keys = {}

resource_params

A dictionary containing the minimal information needed to compute a resource estimate of the operator’s decomposition. The keys of this dictionary should match the resource_keys attribute of the operator class.

Methods

adjoint_resource_decomp(*args, **kwargs)	Returns a list of actions that define the resources of the operator.
controlled_resource_decomp(...)	Returns a list representing the resources for a controlled version of the operator.
default_adjoint_resource_decomp(*args, **kwargs)	Returns a list representing the resources for the adjoint of the operator.
default_controlled_resource_decomp(...)	Returns a list representing the resources for a controlled version of the operator.
default_pow_resource_decomp(pow_z, *args, ...)	Returns a list representing the resources for an operator raised to a power.
default_resource_decomp(*args, **kwargs)	Returns a list of actions that define the resources of the operator.
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(*args, **kwargs)	Returns a list of actions that define the resources of the operator.
resource_rep(*args, **kwargs)	Returns a compressed representation containing only the parameters of the Operator that are needed to estimate the resources.
resource_rep_from_op()	Returns a compressed representation directly from the operator
set_resources(new_func[, override_type])	Set a custom function to override the default resource decomposition.
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 of actions that define the resources 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

classmethod default_adjoint_resource_decomp(*args, **kwargs)

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

classmethod default_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

classmethod default_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

abstract classmethod default_resource_decomp(*args, **kwargs)

Returns a list of actions that define the resources of the operator.

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(*args, **kwargs)

Returns a list of actions that define the resources of the operator.

abstract classmethod resource_rep(*args, **kwargs)

Returns a compressed representation containing only the parameters of the Operator that are needed to estimate the resources.

resource_rep_from_op()

Returns a compressed representation directly from the operator

classmethod set_resources(new_func, override_type='base')

Set a custom function to override the default resource decomposition.

This method allows users to replace any of the resource_decomp, adjoint_resource_decomp, ctrl_resource_decomp, or pow_resource_decomp methods globally for every instance of the class.

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.

code/api/api/pennylane.labs.resource_estimation.ResourceOperator

 

Download Python script

 

Download Notebook

 

View on GitHub