qml.QNode

class QNode(func, device, interface='autograd', diff_method='best', mutable=True, max_expansion=10, **diff_options)

Bases: object

Represents a quantum node in the hybrid computational graph.

A quantum node contains a quantum function (corresponding to a variational circuit) and the computational device it is executed on.

The QNode calls the quantum function to construct a QuantumTape instance representing the quantum circuit.

Parameters

• func (callable) – a quantum function

• device (Device) – a PennyLane-compatible device

• interface (str) –

  The interface that will be used for classical backpropagation. This affects the types of objects that can be passed to/returned from the QNode:

  • "autograd": Allows autograd to backpropagate through the QNode. The QNode accepts default Python types (floats, ints, lists) as well as NumPy array arguments, and returns NumPy arrays.

  • "torch": Allows PyTorch to backpropogate through the QNode. The QNode accepts and returns Torch tensors.

  • "tf": Allows TensorFlow in eager mode to backpropogate through the QNode. The QNode accepts and returns TensorFlow tf.Variable and tf.tensor objects.

  • None: The QNode accepts default Python types (floats, ints, lists) as well as NumPy array arguments, and returns NumPy arrays. It does not connect to any machine learning library automatically for backpropagation.

• diff_method (str) –

  the method of differentiation to use in the created QNode

  • "best": Best available method. Uses classical backpropagation or the device directly to compute the gradient if supported, otherwise will use the analytic parameter-shift rule where possible with finite-difference as a fallback.

  • "device": Queries the device directly for the gradient. Only allowed on devices that provide their own gradient computation.

  • "backprop": Use classical backpropagation. Only allowed on simulator devices that are classically end-to-end differentiable, for example default.tensor.tf. Note that the returned QNode can only be used with the machine-learning framework supported by the device.

  • "reversible": Uses a reversible method for computing the gradient. This method is similar to "backprop", but trades off increased runtime with significantly lower memory usage. Compared to the parameter-shift rule, the reversible method can be faster or slower, depending on the density and location of parametrized gates in a circuit. Only allowed on (simulator) devices with the “reversible” capability, for example default.qubit.

  • "adjoint": Uses an adjoint method that reverses through the circuit after a forward pass by iteratively applying the inverse (adjoint) gate. This method is similar to the reversible method, but has a lower time overhead and a similar memory overhead. Only allowed on simulator devices such as default.qubit.

  • "parameter-shift": Use the analytic parameter-shift rule for all supported quantum operation arguments, with finite-difference as a fallback.

  • "finite-diff": Uses numerical finite-differences for all quantum operation arguments.

• mutable (bool) – If True, the underlying quantum circuit is re-constructed with every evaluation. This is the recommended approach, as it allows the underlying quantum structure to depend on (potentially trainable) QNode input arguments, however may add some overhead at evaluation time. If this is set to False, the quantum structure will only be constructed on the first evaluation of the QNode, and is stored and re-used for further quantum evaluations. Only set this to False if it is known that the underlying quantum structure is independent of QNode input.

• max_expansion (int) – The number of times the internal circuit should be expanded when executed on a device. Expansion occurs when an operation or measurement is not supported, and results in a gate decomposition. If any operations in the decomposition remain unsupported by the device, another expansion occurs.

Keyword Arguments

• h=1e-7 (float) – step size for the finite difference method

• order=1 (int) – The order of the finite difference method to use. 1 corresponds to forward finite differences, 2 to centered finite differences.

• shift=pi/2 (float) – the size of the shift for two-term parameter-shift gradient computations

• adjoint_cache=True (bool) – for TensorFlow and PyTorch interfaces and adjoint differentiation, this indicates whether to save the device state after the forward pass. Doing so saves a forward execution. Device state automatically reused with autograd and JAX interfaces.

• argnum=None (int, list(int), None) – Which argument(s) to compute the Jacobian with respect to. When there are fewer parameters specified than the total number of trainable parameters, the jacobian is being estimated. Note that this option is only applicable for the following differentiation methods: "parameter-shift", "finite-diff" and "reversible".

Example

>>> def circuit(x):
...     qml.RX(x, wires=0)
...     return expval(qml.PauliZ(0))
>>> dev = qml.device("default.qubit", wires=1)
>>> qnode = qml.QNode(circuit, dev)

Attributes

INTERFACE_MAP
specs	Resource information about a quantum circuit.

INTERFACE_MAP = {'autograd': <function QNode.to_autograd>, 'jax': <function QNode.to_jax>, 'tf': <function QNode.to_tf>, 'torch': <function QNode.to_torch>}

specs

Resource information about a quantum circuit.

Returns: dict[str, Union[defaultdict,int]]: dictionaries that contain QNode specifications

Example

dev = qml.device('default.qubit', wires=2)
@qml.qnode(dev)
def circuit(x):
    qml.RX(x[0], wires=0)
    qml.RY(x[1], wires=1)
    qml.CNOT(wires=(0,1))
    return qml.probs(wires=(0,1))

x = np.array([0.1, 0.2])
res = circuit(x)

>>> circuit.specs
{'gate_sizes': defaultdict(int, {1: 2, 2: 1}),
'gate_types': defaultdict(int, {'RX': 1, 'RY': 1, 'CNOT': 1}),
'num_operations': 3,
'num_observables': 1,
'num_diagonalizing_gates': 0,
'num_used_wires': 2,
'depth': 2,
'num_device_wires': 2,
'device_name': 'default.qubit.autograd',
'diff_method': 'backprop'}

Methods

__call__(*args, **kwargs)	Call self as a function.
construct(args, kwargs)	Call the quantum function with a tape context, ensuring the operations get queued.
draw([charset, wire_order, show_all_wires])	Draw the quantum tape as a circuit diagram.
get_best_method(device, interface)	Returns the ‘best’ JacobianTape and differentiation method for a particular device and interface combination.
get_tape(device, interface[, diff_method])	Determine the best JacobianTape, differentiation method, interface, and device for a requested device, interface, and diff method.
metric_tensor(*args[, diag_approx, …])	Evaluate the value of the metric tensor.
to_autograd()	Apply the Autograd interface to the internal quantum tape.
to_jax()	Apply the JAX interface to the internal quantum tape.
to_tf([dtype])	Apply the TensorFlow interface to the internal quantum tape.
to_torch([dtype])	Apply the Torch interface to the internal quantum tape.

__call__(*args, **kwargs)

Call self as a function.

construct(args, kwargs)

Call the quantum function with a tape context, ensuring the operations get queued.

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

Draw the quantum tape as a circuit diagram.

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. If not provided, this defaults to the wire order of the device.

• show_all_wires (bool) – If True, all wires, including empty wires, are printed.

Raises

• ValueError – if the given charset is not supported

• QuantumFunctionError – drawing is impossible because the underlying quantum tape has not yet been constructed

Returns

the circuit representation of the tape

Return type

str

Example

Consider the following circuit as an example:

@qml.qnode(dev)
def circuit(a, w):
    qml.Hadamard(0)
    qml.CRX(a, wires=[0, 1])
    qml.Rot(*w, wires=[1])
    qml.CRX(-a, wires=[0, 1])
    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

We can draw the QNode after execution:

>>> result = circuit(2.3, [1.2, 3.2, 0.7])
>>> print(circuit.draw())
0: ──H──╭C────────────────────────────╭C─────────╭┤ ⟨Z ⊗ Z⟩
1: ─────╰RX(2.3)──Rot(1.2, 3.2, 0.7)──╰RX(-2.3)──╰┤ ⟨Z ⊗ Z⟩
>>> print(circuit.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>

Circuit drawing works with devices with custom wire labels:

dev = qml.device('default.qubit', wires=["a", -1, "q2"])

@qml.qnode(dev)
def circuit():
    qml.Hadamard(wires=-1)
    qml.CNOT(wires=["a", "q2"])
    qml.RX(0.2, wires="a")
    return qml.expval(qml.PauliX(wires="q2"))

When printed, the wire order matches the order defined on the device:

>>> print(circuit.draw())
  a: ─────╭C──RX(0.2)──┤
 -1: ──H──│────────────┤
 q2: ─────╰X───────────┤ ⟨X⟩

We can use the wire_order argument to change the wire order:

>>> print(circuit.draw(wire_order=["q2", "a", -1]))
 q2: ──╭X───────────┤ ⟨X⟩
  a: ──╰C──RX(0.2)──┤
 -1: ───H───────────┤

static get_best_method(device, interface)

Returns the ‘best’ JacobianTape and differentiation method for a particular device and interface combination.

This method attempts to determine support for differentiation methods using the following order:

• "device"

• "backprop"

• "parameter-shift"

• "finite-diff"

The first differentiation method that is supported (going from top to bottom) will be returned.

Parameters

• device (Device) – PennyLane device

• interface (str) – name of the requested interface

Returns

Tuple containing the compatible JacobianTape, the interface to apply, the device to use, and the method argument to pass to the JacobianTape.jacobian method.

Return type

tuple[JacobianTape, str, Device, dict[str, str]]

static get_tape(device, interface, diff_method='best')

Determine the best JacobianTape, differentiation method, interface, and device for a requested device, interface, and diff method.

Parameters

• device (Device) – PennyLane device

• interface (str) – name of the requested interface

• diff_method (str) – The requested method of differentiation. One of "best", "backprop", "reversible", "adjoint", "device", "parameter-shift", or "finite-diff".

Returns

Tuple containing the compatible JacobianTape, the interface to apply, the device to use, and the method argument to pass to the JacobianTape.jacobian method.

Return type

tuple[JacobianTape, str, Device, dict[str, str]]

metric_tensor(*args, diag_approx=False, only_construct=False, **kwargs)

Evaluate the value of the metric tensor.

Parameters

• args (tuple[Any]) – positional arguments

• kwargs (dict[str, Any]) – auxiliary arguments

• diag_approx (bool) – iff True, use the diagonal approximation

• only_construct (bool) – Iff True, construct the circuits used for computing the metric tensor but do not execute them, and return the tapes.

Returns

metric tensor

Return type

array[float]

to_autograd()

Apply the Autograd interface to the internal quantum tape.

to_jax()

Apply the JAX interface to the internal quantum tape.

Parameters

dtype (tf.dtype) – The dtype that the JAX QNode should output. If not provided, the default is jnp.float64.

Raises

QuantumFunctionError – if TensorFlow >= 2.1 is not installed

to_tf(dtype=None)

Apply the TensorFlow interface to the internal quantum tape.

Parameters

dtype (tf.dtype) – The dtype that the TensorFlow QNode should output. If not provided, the default is tf.float64.

Raises

QuantumFunctionError – if TensorFlow >= 2.1 is not installed

to_torch(dtype=None)

Apply the Torch interface to the internal quantum tape.

Parameters

dtype (tf.dtype) – The dtype that the Torch QNode should output. If not provided, the default is torch.float64.

Raises

QuantumFunctionError – if PyTorch >= 1.3 is not installed