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  4. qml.labs.resource_estimation.ResourceCRX

qml.labs.resource_estimation.ResourceCRX

class ResourceCRX(precision=None, wires=None)[source]

Bases: ResourceOperator

Resource class for the CRX gate.

Parameters:

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

  • precision (float, optional) – The error threshold for clifford plus T decomposition of the rotation gate. The default value is None which corresponds to using the precision stated in the config.

Resources:

The resources are taken from Figure 1b of Gheorghiu, V., Mosca, M. & Mukhopadhyay. In combination with the following identity:

\[\hat{RX} = \hat{H} \cdot \hat{RZ} \cdot \hat{H},\]

we can express the CRX gate as a CRZ gate conjugated by Hadamard gates. The expression for controlled-RZ gates is used as defined in the reference above. Specifically, the resources are defined as two ResourceCNOT gates, two ResourceHadamard gates and two ResourceRZ gates.

See also

CRX

Example

The resources for this operation are computed using:

>>> re.ResourceCRX.resource_decomp()
[(2 x CNOT), (2 x RZ), (2 x Hadamard)]

num_wires

resource_keys

resource_params

Returns a dictionary containing the minimal information needed to compute the resources.
num_wires = 2
resource_keys = {'precision'}
resource_params

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

Returns:

  • precision (Union[float, None]): error threshold for the approximation

Return type:

A dictionary containing the resource parameters

adjoint_resource_decomp([precision])

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

controlled_resource_decomp(...[, precision])

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[, precision])

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

queue([context])

Append the operator to the Operator queue.

resource_decomp([precision])

Returns a list of GateCount objects representing the resources of the operator.

resource_rep([precision])

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

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(precision=None, **kwargs)[source]

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

Resources:

The adjoint of a single qubit rotation changes the sign of the rotation angle, thus the resources of the adjoint operation result in the original 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 controlled_resource_decomp(ctrl_num_ctrl_wires, ctrl_num_ctrl_values, precision=None, **kwargs)[source]

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

  • precision (float, optional) – The error threshold for clifford plus T decomposition of the rotation gate. The default value is None which corresponds to using the precision stated in the config.

Resources:

The resources are expressed using the symbolic ResourceControlled. The resources are computed according to the controlled_resource_decomp() of the base ResourceRX class.

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]

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, precision=None, **kwargs)[source]

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

Parameters:

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

  • precision (float, optional) – The error threshold for clifford plus T decomposition of the rotation gate. The default value is None which corresponds to using the precision stated in the config.

Resources:

Taking arbitrary powers of a single qubit rotation produces a sum of rotations. The resources simplify to just one total single qubit rotation.

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(precision=None, **kwargs)[source]

Returns a list of GateCount objects representing the resources of the operator. Each GateCount object specifies a gate type and its total occurrence count.

Parameters:

precision (float, optional) – The error threshold for clifford plus T decomposition of the rotation gate. The default value is None which corresponds to using the precision stated in the config.

Resources:

The resources are taken from Figure 1b of Gheorghiu, V., Mosca, M. & Mukhopadhyay. In combination with the following identity:

\[\hat{RX} = \hat{H} \cdot \hat{RZ} \cdot \hat{H},\]

we can express the CRX gate as a CRZ gate conjugated by Hadamard gates. The expression for controlled-RZ gates is used as defined in the reference above. Specifically, the resources are defined as two ResourceCNOT gates, two ResourceHadamard gates and two ResourceRZ gates.

classmethod resource_rep(precision=None)[source]

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

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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