qml.tape.OperationRecorder¶

class OperationRecorder[source]¶

Bases: pennylane.tape.tape.QuantumTape

A template and quantum function inspector, allowing easy introspection of operators that have been applied without requiring a QNode.

Example:

The OperationRecorder is a context manager. Executing templates or quantum functions stores applied operators in the recorder, which can then be printed.

>>> weights = qml.init.strong_ent_layers_normal(n_layers=1, n_wires=2)
>>>
>>> with OperationRecorder() as rec:
>>>    qml.templates.layers.StronglyEntanglingLayers(weights, wires=[0, 1])

Alternatively, the queue attribute can be used to directly access the applied Operation and Observable objects.

Attributes

`data`	Alias to `get_parameters()` and `set_parameters()` for backwards compatibilities with operations.
`diagonalizing_gates`	Returns the gates that diagonalize the measured wires such that they are in the eigenbasis of the circuit observables.
`graph`	Returns a directed acyclic graph representation of the recorded quantum circuit:
`hash`	returns an integer hash uniquely representing the quantum tape
`interface`	automatic differentiation interface used by the quantum tape (if any)
`measurements`	Returns the measurements on the quantum tape.
`num_params`	Returns the number of trainable parameters on the quantum tape.
`observables`	Returns the observables on the quantum tape.
`operations`	Returns the operations on the quantum tape.
`output_dim`	The (inferred) output dimension of the quantum tape.
`queue`	Returns a list of objects in the annotated queue
`specs`	Resource information about a quantum circuit.
`trainable_params`	Store or return a set containing the indices of parameters that support differentiability.

data¶: Alias to get_parameters() and set_parameters() for backwards compatibilities with operations.

diagonalizing_gates¶

Returns the gates that diagonalize the measured wires such that they are in the eigenbasis of the circuit observables.

Returns: the operations that diagonalize the observables
Return type: List[Operation]

graph¶

Returns a directed acyclic graph representation of the recorded quantum circuit:

>>> tape.graph
<pennylane.circuit_graph.CircuitGraph object at 0x7fcc0433a690>

Note that the circuit graph is only constructed once, on first call to this property, and cached for future use.

Returns: the circuit graph object
Return type: CircuitGraph

hash¶

returns an integer hash uniquely representing the quantum tape

Type: int

interface¶

automatic differentiation interface used by the quantum tape (if any)

Type: str, None

measurements¶

Returns the measurements on the quantum tape.

Returns: list of recorded measurement processess
Return type: list[MeasurementProcess]

Example

with JacobianTape() as tape:
    qml.RX(0.432, wires=0)
    qml.RY(0.543, wires=0)
    qml.CNOT(wires=[0, 'a'])
    qml.RX(0.133, wires='a')
    qml.expval(qml.PauliZ(wires=[0]))

>>> tape.measurements
[expval(PauliZ(wires=[0]))]

num_params¶: Returns the number of trainable parameters on the quantum tape.

observables¶

Returns the observables on the quantum tape.

Returns: list of recorded quantum operations
Return type: list[Observable]

Example

with JacobianTape() as tape:
    qml.RX(0.432, wires=0)
    qml.RY(0.543, wires=0)
    qml.CNOT(wires=[0, 'a'])
    qml.RX(0.133, wires='a')
    qml.expval(qml.PauliZ(wires=[0]))

>>> tape.observables
[expval(PauliZ(wires=[0]))]

operations¶

Returns the operations on the quantum tape.

Returns: recorded quantum operations
Return type: list[Operation]

Example

with JacobianTape() as tape:
    qml.RX(0.432, wires=0)
    qml.RY(0.543, wires=0)
    qml.CNOT(wires=[0, 'a'])
    qml.RX(0.133, wires='a')
    qml.expval(qml.PauliZ(wires=[0]))

>>> tape.operations
[RX(0.432, wires=[0]), RY(0.543, wires=[0]), CNOT(wires=[0, 'a']), RX(0.133, wires=['a'])]

output_dim¶: The (inferred) output dimension of the quantum tape.

queue¶

specs¶

Resource information about a quantum circuit.

Returns: dictionaries that contain tape specifications
Return type: dict[str, Union[defaultdict,int]]

Example

with qml.tape.QuantumTape() as tape:
    qml.Hadamard(wires=0)
    qml.RZ(0.26, wires=1)
    qml.CNOT(wires=[1, 0])
    qml.Rot(1.8, -2.7, 0.2, wires=0)
    qml.Hadamard(wires=1)
    qml.CNOT(wires=[0, 1])
    qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

Asking for the specs produces a dictionary as shown below:

>>> tape.specs['gate_sizes']
defaultdict(int, {1: 4, 2: 2})
>>> tape.specs['gate_types']
defaultdict(int, {'Hadamard': 2, 'RZ': 1, 'CNOT': 2, 'Rot': 1})

As defaultdict objects, any key not present in the dictionary returns 0.

>>> tape.specs['gate_types']['RX']
0

trainable_params¶

Store or return a set containing the indices of parameters that support differentiability. The indices provided match the order of appearence in the quantum circuit.

Setting this property can help reduce the number of quantum evaluations needed to compute the Jacobian; parameters not marked as trainable will be automatically excluded from the Jacobian computation.

The number of trainable parameters determines the number of parameters passed to set_parameters(), execute(), and jacobian(), and changes the default output size of methods jacobian() and get_parameters().

Note

Since the jacobian() method is not called for devices that support native backpropagation (such as default.qubit.tf and default.qubit.autograd), this property contains no relevant information when using backpropagation to compute gradients.

Example

with JacobianTape() as tape:
    qml.RX(0.432, wires=0)
    qml.RY(0.543, wires=0)
    qml.CNOT(wires=[0, 'a'])
    qml.RX(0.133, wires='a')
    qml.expval(qml.PauliZ(wires=[0]))

>>> tape.trainable_params
{0, 1, 2}
>>> tape.trainable_params = {0} # set only the first parameter as free
>>> tape.get_parameters()
[0.432]

Parameters: param_indices (set[int]) – parameter indices

Methods

`active_context`()	Returns the currently active queuing context.
`adjoint`()	Create a tape that is the adjoint of this one.
`append`(obj, **kwargs)	Append an object to this QueuingContext instance.
`copy`([copy_operations, tape_cls])	Returns a shallow copy of the quantum tape.
`draw`([charset, wire_order, show_all_wires])	Draw the quantum tape as a circuit diagram.
`execute`(device[, params])	Execute the tape on a quantum device.
`execute_device`(params, device)	Execute the tape on a quantum device.
`expand`([depth, stop_at, expand_measurements])	Expand all operations in the processed queue to a specific depth.
`get_info`(obj)	Retrieves information of an object in the queue instance.
`get_operation`(idx)	Returns the trainable operation, and the corresponding operation argument index, for a specified trainable parameter index.
`get_parameters`([trainable_only])	Return the parameters incident on the tape operations.
`inv`()	Inverts the processed operations.
`recording`()	Whether a queuing context is active and recording operations
`remove`(obj)	Remove an object from this QueuingContext instance.
`set_parameters`(params[, trainable_only])	Set the parameters incident on the tape operations.
`stop_recording`()	Context manager to temporarily stop recording operations onto the tape.
`to_openqasm`([wires, rotations, measure_all])	Serialize the circuit as an OpenQASM 2.0 program.
`unwrap`()	A context manager that unwraps a tape with tensor-like parameters to NumPy arrays.
`update_info`(obj, **kwargs)	Updates information of an object in the queue instance.

classmethod active_context()¶: Returns the currently active queuing context.

adjoint()¶

Create a tape that is the adjoint of this one.

Adjointed tapes are the conjugated and transposed version of the original tapes. Adjointed ops are equivalent to the inverted operation for unitary gates.

Returns: the adjointed tape
Return type: QuantumTape

append(obj, **kwargs)¶

Append an object to this QueuingContext instance.

Parameters: obj – The object to be appended

copy(copy_operations=False, tape_cls=None)¶

Returns a shallow copy of the quantum tape.

Parameters

copy_operations (bool) – If True, the tape operations are also shallow copied. Otherwise, if False, the copied tape operations will simply be references to the original tape operations; changing the parameters of one tape will likewise change the parameters of all copies.
tape_cls (QuantumTape) – Cast the copied tape to a specific quantum tape subclass. If not provided, the same subclass is used as the original tape.

Returns

a shallow copy of the tape

Return type

QuantumTape

draw(charset='unicode', wire_order=None, show_all_wires=False)¶

Draw the quantum tape as a circuit diagram.

Consider the following circuit as an example:

with QuantumTape() as tape:
    qml.Hadamard(0)
    qml.CRX(2.3, wires=[0, 1])
    qml.Rot(1.2, 3.2, 0.7, wires=[1])
    qml.CRX(-2.3, wires=[0, 1])
    qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

We can draw the tape after construction:

>>> print(tape.draw())
0: ──H──╭C────────────────────────────╭C─────────╭┤ ⟨Z ⊗ Z⟩
1: ─────╰RX(2.3)──Rot(1.2, 3.2, 0.7)──╰RX(-2.3)──╰┤ ⟨Z ⊗ Z⟩
>>> print(tape.draw(charset="ascii"))
0: --H--+C----------------------------+C---------+| <Z @ Z>
1: -----+RX(2.3)--Rot(1.2, 3.2, 0.7)--+RX(-2.3)--+| <Z @ Z>

Parameters

charset (str, optional) – The charset that should be used. Currently, “unicode” and “ascii” are supported.
wire_order (Sequence[Any]) – the order (from top to bottom) to print the wires of the circuit
show_all_wires (bool) – If True, all wires, including empty wires, are printed.

Raises

ValueError – if the given charset is not supported

Returns

the circuit representation of the tape

Return type

str

execute(device, params=None)¶

Execute the tape on a quantum device.

Parameters

device (Device) – a PennyLane device that can execute quantum operations and return measurement statistics
params (list[Any]) – The quantum tape operation parameters. If not provided, the current tape parameters are used (via get_parameters()).

Example

with JacobianTape() as tape:
    qml.RX(0.432, wires=0)
    qml.RY(0.543, wires=0)
    qml.CNOT(wires=[0, 'a'])
    qml.RX(0.133, wires='a')
    qml.probs(wires=[0, 'a'])

If parameters are not provided, the existing tape parameters are used:

>>> dev = qml.device("default.qubit", wires=[0, 'a'])
>>> tape.execute(dev)
array([[8.84828969e-01, 3.92449987e-03, 4.91235209e-04, 1.10755296e-01]])

Parameters can be optionally passed during execution:

>>> tape.execute(dev, params=[1.0, 0.0, 1.0])
array([[0.5931328 , 0.17701835, 0.05283049, 0.17701835]])

Parameters provided for execution are temporary, and do not affect the tapes’ parameters in-place:

>>> tape.get_parameters()
[0.432, 0.543, 0.133]

execute_device(params, device)¶

Execute the tape on a quantum device.

This is a low-level method, intended to be called by an interface, and does not support autodifferentiation.

For more details on differentiable tape execution, see execute().

Parameters

device (Device) – a PennyLane device that can execute quantum operations and return measurement statistics
params (list[Any]) – The quantum tape operation parameters. If not provided, the current tape parameter values are used (via get_parameters()).

expand(depth=1, stop_at=None, expand_measurements=False)¶

Expand all operations in the processed queue to a specific depth.

Parameters

depth (int) – the depth the tape should be expanded
stop_at (Callable) – A function which accepts a queue object, and returns True if this object should not be expanded. If not provided, all objects that support expansion will be expanded.
expand_measurements (bool) – If True, measurements will be expanded to basis rotations and computational basis measurements.

Example

Consider the following nested tape:

with JacobianTape() as tape:
    qml.BasisState(np.array([1, 1]), wires=[0, 'a'])

    with JacobianTape() as tape2:
        qml.Rot(0.543, 0.1, 0.4, wires=0)

    qml.CNOT(wires=[0, 'a'])
    qml.RY(0.2, wires='a')
    qml.probs(wires=0), qml.probs(wires='a')

The nested structure is preserved:

>>> tape.operations
[BasisState(array([1, 1]), wires=[0, 'a']),
 <JacobianTape: wires=[0], params=3>,
 CNOT(wires=[0, 'a']),
 RY(0.2, wires=['a'])]

Calling .expand will return a tape with all nested tapes expanded, resulting in a single tape of quantum operations:

>>> new_tape = tape.expand(depth=2)
>>> new_tape.operations
[PauliX(wires=[0]),
PauliX(wires=['a']),
RZ(0.543, wires=[0]),
RY(0.1, wires=[0]),
RZ(0.4, wires=[0]),
CNOT(wires=[0, 'a']),
RY(0.2, wires=['a'])]

get_info(obj)¶: Retrieves information of an object in the queue instance.

get_operation(idx)¶

Returns the trainable operation, and the corresponding operation argument index, for a specified trainable parameter index.

Parameters: idx (int) – the trainable parameter index
Returns: tuple containing the corresponding operation, and an integer representing the argument index, for the provided trainable parameter.
Return type: tuple[Operation, int]

get_parameters(trainable_only=True, **kwargs)¶

Return the parameters incident on the tape operations.

The returned parameters are provided in order of appearance on the tape.

Parameters: trainable_only (bool) – if True, returns only trainable parameters

Example

with JacobianTape() as tape:
    qml.RX(0.432, wires=0)
    qml.RY(0.543, wires=0)
    qml.CNOT(wires=[0, 'a'])
    qml.RX(0.133, wires='a')
    qml.expval(qml.PauliZ(wires=[0]))

By default, all parameters are trainable and will be returned:

>>> tape.get_parameters()
[0.432, 0.543, 0.133]

Setting the trainable parameter indices will result in only the specified parameters being returned:

>>> tape.trainable_params = {1} # set the second parameter as free
>>> tape.get_parameters()
[0.543]

The trainable_only argument can be set to False to instead return all parameters:

>>> tape.get_parameters(trainable_only=False)
[0.432, 0.543, 0.133]

inv()¶

Inverts the processed operations.

Inversion is performed in-place.

Note

This method only inverts the quantum operations/unitary recorded by the quantum tape; state preparations and measurements are left unchanged.

Example

with JacobianTape() as tape:
    qml.BasisState(np.array([1, 1]), wires=[0, 'a'])
    qml.RX(0.432, wires=0)
    qml.Rot(0.543, 0.1, 0.4, wires=0).inv()
    qml.CNOT(wires=[0, 'a'])
    qml.probs(wires=0), qml.probs(wires='a')

This tape has the following properties:

>>> tape.operations
[BasisState(array([1, 1]), wires=[0, 'a']),
 RX(0.432, wires=[0]),
 Rot.inv(0.543, 0.1, 0.4, wires=[0]),
 CNOT(wires=[0, 'a'])]
>>> tape.get_parameters()
[array([1, 1]), 0.432, 0.543, 0.1, 0.4]

Here, let’s set some trainable parameters:

>>> tape.trainable_params = {1, 2}
>>> tape.get_parameters()
[0.432, 0.543]

Inverting the tape:

>>> tape.inv()
>>> tape.operations
[BasisState(array([1, 1]), wires=[0, 'a']),
 CNOT.inv(wires=[0, 'a']),
 Rot(0.543, 0.1, 0.4, wires=[0]),
 RX.inv(0.432, wires=[0])]

Tape inversion also modifies the order of tape parameters:

>>> tape.get_parameters(trainable_only=False)
[array([1, 1]), 0.543, 0.1, 0.4, 0.432]
>>> tape.get_parameters(trainable_only=True)
[0.543, 0.432]
>>> tape.trainable_params
{1, 4}

classmethod recording()¶: Whether a queuing context is active and recording operations

remove(obj)¶

Remove an object from this QueuingContext instance.

Parameters: obj – the object to be removed

set_parameters(params, trainable_only=True)¶

Set the parameters incident on the tape operations.

Parameters

params (list[float]) – A list of real numbers representing the parameters of the quantum operations. The parameters should be provided in order of appearance in the quantum tape.
trainable_only (bool) – if True, set only trainable parameters

Example

with JacobianTape() as tape:
    qml.RX(0.432, wires=0)
    qml.RY(0.543, wires=0)
    qml.CNOT(wires=[0, 'a'])
    qml.RX(0.133, wires='a')
    qml.expval(qml.PauliZ(wires=[0]))

By default, all parameters are trainable and can be modified:

>>> tape.set_parameters([0.1, 0.2, 0.3])
>>> tape.get_parameters()
[0.1, 0.2, 0.3]

Setting the trainable parameter indices will result in only the specified parameters being modifiable. Note that this only modifies the number of parameters that must be passed.

>>> tape.trainable_params = {0, 2} # set the first and third parameter as free
>>> tape.set_parameters([-0.1, 0.5])
>>> tape.get_parameters(trainable_only=False)
[-0.1, 0.2, 0.5]

The trainable_only argument can be set to False to instead set all parameters:

>>> tape.set_parameters([4, 1, 6], trainable_only=False)
>>> tape.get_parameters(trainable_only=False)
[4, 1, 6]

stop_recording()¶

Context manager to temporarily stop recording operations onto the tape. This is useful is scratch space is needed.

Example

>>> with qml.tape.QuantumTape() as tape:
...     qml.RX(0, wires=0)
...     with tape.stop_recording():
...         qml.RY(1.0, wires=1)
...     qml.RZ(2, wires=1)
>>> tape.operations
[RX(0, wires=[0]), RZ(2, wires=[1])]

to_openqasm(wires=None, rotations=True, measure_all=True)¶

Serialize the circuit as an OpenQASM 2.0 program.

Measurements are assumed to be performed on all qubits in the computational basis. An optional rotations argument can be provided so that output of the OpenQASM circuit is diagonal in the eigenbasis of the tape’s observables. The measurement outputs can be restricted to only those specified in the tape by setting measure_all=False.

Note

The serialized OpenQASM program assumes that gate definitions in qelib1.inc are available.

Parameters

wires (Wires or None) – the wires to use when serializing the circuit
rotations (bool) – in addition to serializing user-specified operations, also include the gates that diagonalize the measured wires such that they are in the eigenbasis of the circuit observables.
measure_all (bool) – whether to perform a computational basis measurement on all qubits or just those specified in the tape

Returns

OpenQASM serialization of the circuit

Return type

str

unwrap()¶

A context manager that unwraps a tape with tensor-like parameters to NumPy arrays.

Parameters: tape (QuantumTape) – the quantum tape to unwrap
Returns: the unwrapped quantum tape
Return type: QuantumTape

Example

>>> with tf.GradientTape():
...     with qml.tape.QuantumTape() as tape:
...         qml.RX(tf.Variable(0.1), wires=0)
...         qml.RY(tf.constant(0.2), wires=0)
...         qml.RZ(tf.Variable(0.3), wires=0)
...     with tape.unwrap():
...         print("Trainable params:", tape.trainable_params)
...         print("Unwrapped params:", tape.get_parameters())
Trainable params: {0, 2}
Unwrapped params: [0.1, 0.3]
>>> print("Original parameters:", tape.get_parameters())
Original parameters: [<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=0.1>,
  <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=0.3>]

update_info(obj, **kwargs)¶: Updates information of an object in the queue instance.

qml.tape.OperationRecorder¶

Attributes

Methods

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