qml.labs.resource_estimation.ResourceAdjoint

class ResourceAdjoint(base_op, wires=None)

Bases: ResourceOperator

Resource class for the symbolic Adjoint operation.

A symbolic class used to represent the adjoint of some base operation.

Parameters:

• base_op (ResourceOperator) – The operator that we want the adjoint of.

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

Resources:

This symbolic operation represents the adjoint of some base operation. The resources are determined as follows. If the base operation implements the .default_adjoint_resource_decomp() method, then the resources are obtained from this.

Otherwise, the adjoint resources are given as the adjoint of each operation in the base operation’s resources.

See also

AdjointOperation

Example

The adjoint operation can be constructed like this:

>>> qft = plre.ResourceQFT(num_wires=3)
>>> adj_qft = plre.ResourceAdjoint(qft)

We can see how the resources differ by choosing a suitable gateset and estimating resources:

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

Attributes

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

num_wires = 0

resource_keys = {'base_cmpr_op'}

resource_params

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

Returns:

A dictionary containing the resource parameters: * base_cmpr_op (~.pennylane.labs.resource_estimation.ResourceOperator): The operator that we want the adjoint of.

Return type:

dict

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(base_cmpr_op)	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(base_cmpr_op, **kwargs)	Returns a list representing the resources 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(*args, **kwargs)	Returns a list of actions that define the resources of the operator.
resource_rep(base_cmpr_op)	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
set_resources(new_func[, override_type])	Set a custom function to override the default resource decomposition.
tracking_name(base_cmpr_op)	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 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(base_cmpr_op)

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

Parameters:

base_cmpr_op (CompressedResourceOp) – A compressed resource representation for the operator we want the adjoint of.

Resources:

The adjoint of an adjointed operation is just the original operation. The resources are given as one instance of the base operation.

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]

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

classmethod default_resource_decomp(base_cmpr_op, **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 the adjoint of.

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

Resources:

This symbolic operation represents the adjoint of some base operation. The resources are determined as follows. If the base operation implements the .default_adjoint_resource_decomp() method, then the resources are obtained from this.

Otherwise, the adjoint resources are given as the adjoint of each operation in the base operation’s resources.

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

AdjointOperation

Example

The adjoint operation can be constructed like this:

>>> qft = plre.ResourceQFT(num_wires=3)
>>> adj_qft = plre.ResourceAdjoint(qft)

We can see how the resources differ by choosing a suitable gateset and estimating resources:

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

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(*args, **kwargs)

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

classmethod resource_rep(base_cmpr_op)

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

Parameters:

base_cmpr_op (ResourceOperator) – The operator that we want the adjoint of.

Returns:

the operator in a compressed representation

Return type:

CompressedResourceOp

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.

static tracking_name(base_cmpr_op)

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

tracking_name_from_op()

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

code/api/api/pennylane.labs.resource_estimation.ResourceAdjoint

 

Download Python script

 

Download Notebook

 

View on GitHub