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qml.drawer.draw

draw(qnode, wire_order=None, show_all_wires=False, decimals=2, max_length=100, show_matrices=True, expansion_strategy=None)[source]

Create a function that draws the given qnode or quantum function.

Parameters

  • qnode (QNode or Callable) – the input QNode or quantum function that is to be drawn.

  • wire_order (Sequence[Any]) – the order (from top to bottom) to print the wires of the circuit. If not provided, the wire order defaults to the device wires. If device wires are not available, the circuit wires are sorted if possible.

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

  • decimals (int) – How many decimal points to include when formatting operation parameters. None will omit parameters from operation labels.

  • max_length (int) – Maximum string width (columns) when printing the circuit

  • show_matrices=False (bool) – show matrix valued parameters below all circuit diagrams

  • expansion_strategy (str) –

    The strategy to use when circuit expansions or decompositions are required. Note that this is ignored if the input is not a QNode.

    • gradient: The QNode will attempt to decompose the internal circuit such that all circuit operations are supported by the gradient method.

    • device: The QNode will attempt to decompose the internal circuit such that all circuit operations are natively supported by the device.

Returns

A function that has the same argument signature as qnode. When called, the function will draw the QNode/qfunc.

Example

@qml.qnode(qml.device('lightning.qubit', wires=2))
def circuit(a, w):
    qml.Hadamard(0)
    qml.CRX(a, wires=[0, 1])
    qml.Rot(*w, wires=[1], id="arbitrary")
    qml.CRX(-a, wires=[0, 1])
    return qml.expval(qml.Z(0) @ qml.Z(1))

>>> print(qml.draw(circuit)(a=2.3, w=[1.2, 3.2, 0.7]))
0: ──H─╭●─────────────────────────────────────────╭●─────────┤ ╭<Z@Z>
1: ────╰RX(2.30)──Rot(1.20,3.20,0.70,"arbitrary")─╰RX(-2.30)─┤ ╰<Z@Z>

By specifying the decimals keyword, parameters are displayed to the specified precision.

>>> print(qml.draw(circuit, decimals=4)(a=2.3, w=[1.2, 3.2, 0.7]))
0: ──H─╭●─────────────────────────────────────────────────╭●───────────┤ ╭<Z@Z>
1: ────╰RX(2.3000)──Rot(1.2000,3.2000,0.7000,"arbitrary")─╰RX(-2.3000)─┤ ╰<Z@Z>

Parameters can be omitted by requesting decimals=None:

>>> print(qml.draw(circuit, decimals=None)(a=2.3, w=[1.2, 3.2, 0.7]))
0: ──H─╭●────────────────────╭●──┤ ╭<Z@Z>
1: ────╰RX──Rot("arbitrary")─╰RX─┤ ╰<Z@Z>

If the parameters are not acted upon by classical processing like -a, then qml.draw can handle string-valued parameters as well:

>>> @qml.qnode(qml.device('lightning.qubit', wires=1))
... def circuit2(x):
...     qml.RX(x, wires=0)
...     return qml.expval(qml.Z(0))
>>> print(qml.draw(circuit2)("x"))
0: ──RX(x)─┤  <Z>

When requested with show_matrices=True (the default), matrix valued parameters are printed below the circuit. For show_matrices=False, they are not printed:

>>> @qml.qnode(qml.device('default.qubit', wires=2))
... def circuit3():
...     qml.QubitUnitary(np.eye(2), wires=0)
...     qml.QubitUnitary(-np.eye(4), wires=(0,1))
...     return qml.expval(qml.Hermitian(np.eye(2), wires=1))
>>> print(qml.draw(circuit3)())
0: ──U(M0)─╭U(M1)─┤
1: ────────╰U(M1)─┤  <𝓗(M0)>
M0 =
[[1. 0.]
[0. 1.]]
M1 =
[[-1. -0. -0. -0.]
[-0. -1. -0. -0.]
[-0. -0. -1. -0.]
[-0. -0. -0. -1.]]
>>> print(qml.draw(circuit3, show_matrices=False)())
0: ──U(M0)─╭U(M1)─┤
1: ────────╰U(M1)─┤  <𝓗(M0)>

The max_length keyword warps long circuits:

rng = np.random.default_rng(seed=42)
shape = qml.StronglyEntanglingLayers.shape(n_wires=3, n_layers=3)
params = rng.random(shape)

@qml.qnode(qml.device('lightning.qubit', wires=3))
def longer_circuit(params):
    qml.StronglyEntanglingLayers(params, wires=range(3))
    return [qml.expval(qml.Z(i)) for i in range(3)]

print(qml.draw(longer_circuit, max_length=60)(params))

0: ──Rot(0.77,0.44,0.86)─╭●────╭X──Rot(0.45,0.37,0.93)─╭●─╭X
1: ──Rot(0.70,0.09,0.98)─╰X─╭●─│───Rot(0.64,0.82,0.44)─│──╰●
2: ──Rot(0.76,0.79,0.13)────╰X─╰●──Rot(0.23,0.55,0.06)─╰X───

───Rot(0.83,0.63,0.76)──────────────────────╭●────╭X─┤  <Z>
──╭X────────────────────Rot(0.35,0.97,0.89)─╰X─╭●─│──┤  <Z>
──╰●────────────────────Rot(0.78,0.19,0.47)────╰X─╰●─┤  <Z>

The wire_order keyword specifies the order of the wires from top to bottom:

>>> print(qml.draw(circuit, wire_order=[1,0])(a=2.3, w=[1.2, 3.2, 0.7]))
1: ────╭RX(2.30)──Rot(1.20,3.20,0.70)─╭RX(-2.30)─┤ ╭<Z@Z>
0: ──H─╰●─────────────────────────────╰●─────────┤ ╰<Z@Z>

If the device or wire_order has wires not used by operations, those wires are omitted unless requested with show_all_wires=True

>>> empty_qfunc = lambda : qml.expval(qml.Z(0))
>>> empty_circuit = qml.QNode(empty_qfunc, qml.device('lightning.qubit', wires=3))
>>> print(qml.draw(empty_circuit, show_all_wires=True)())
0: ───┤  <Z>
1: ───┤
2: ───┤

Drawing also works on batch transformed circuits:

from functools import partial

@partial(qml.gradients.param_shift, shifts=[(0.1,)])
@qml.qnode(qml.device('default.qubit', wires=1))
def transformed_circuit(x):
    qml.RX(x, wires=0)
    return qml.expval(qml.Z(0))

print(qml.draw(transformed_circuit)(np.array(1.0, requires_grad=True)))

0: ──RX(1.10)─┤  <Z>

0: ──RX(0.90)─┤  <Z>

The function also accepts quantum functions rather than QNodes. This can be especially helpful if you want to visualize only a part of a circuit that may not be convertible into a QNode, such as a sub-function that does not return any measurements.

>>> def qfunc(x):
...     qml.RX(x, wires=[0])
...     qml.CNOT(wires=[0, 1])
>>> print(qml.draw(qfunc)(1.1))
0: ──RX(1.10)─╭●─┤
1: ───────────╰X─┤
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