[BUG] Drawing a ControlledQubitUnitary returns TypeError

[DSGuala]

Issue description

Trying to use the circuit drawer with qml.ControlledQubitUnitary raises the error TypeError: unsupported format string passed to numpy.ndarray.__format_

import pennylane as qml
from pennylane import numpy as np

dev = qml.device("default.qubit", wires=3)

U = np.array([[ 0.94877869,  0.31594146], [-0.31594146,  0.94877869]])

@qml.qnode(dev)
def circuit():
    qml.ControlledQubitUnitary(U, control_wires=[0, 1], wires=2)
    return qml.probs(range(3))

circuit()
circuit.draw()

• Expected behavior:
  circuit.draw() returns a string representing the circuit

• Actual behavior:
  circuit.draw() raises the error TypeError: unsupported format string passed to numpy.ndarray.__format_

• Reproduces how often:
  Always

• System information:

Name: PennyLane
Version: 0.15.0.dev0
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Author: None
Author-email: None
License: Apache License 2.0
Requires: appdirs, autograd, networkx, numpy, scipy, semantic-version, toml
Required-by: 
Platform info:           Linux-5.8.0-48-generic-x86_64-with-glibc2.29
Python version:          3.8.5
Numpy version:           1.19.5
Scipy version:           1.4.1
Installed devices:
- default.gaussian (PennyLane-0.15.0.dev0)
- default.mixed (PennyLane-0.15.0.dev0)
- default.qubit (PennyLane-0.15.0.dev0)
- default.qubit.autograd (PennyLane-0.15.0.dev0)
- default.qubit.jax (PennyLane-0.15.0.dev0)
- default.qubit.tf (PennyLane-0.15.0.dev0)
- default.tensor (PennyLane-0.15.0.dev0)
- default.tensor.tf (PennyLane-0.15.0.dev0)

Tracebacks

<ipython-input-6-2b516653526b> in <module>
     12 
     13 circuit()
---> 14 circuit.draw()

~pennylane/pennylane/qnode.py in draw(self, charset, wire_order, show_all_wires)
    685             )
    686 
--> 687         return self.qtape.draw(
    688             charset=charset, wire_order=wire_order, show_all_wires=show_all_wires
    689         )

~pennylane/pennylane/tape/tape.py in draw(self, charset, wire_order, show_all_wires)
   1003             str: the circuit representation of the tape
   1004         """
-> 1005         return self.graph.draw(
   1006             charset=charset,
   1007             wire_order=wire_order,

~pennylane/pennylane/circuit_graph.py in draw(self, charset, wire_order, show_all_wires)
    572             )
    573 
--> 574         drawer = CircuitDrawer(
    575             grid,
    576             obs,

~pennylane/pennylane/circuit_drawer/circuit_drawer.py in __init__(self, raw_operation_grid, raw_observable_grid, wires, charset, show_all_wires)
     79 
     80         # Resolve operator names
---> 81         self.resolve_representation(self.operation_grid, self.operation_representation_grid)
     82         self.resolve_representation(self.observable_grid, self.observable_representation_grid)
     83 

~pennylane/pennylane/circuit_drawer/circuit_drawer.py in resolve_representation(self, grid, representation_grid)
    160                 representation_layer[
    161                     wire_indices
--> 162                 ] = self.representation_resolver.element_representation(operator, wire)
    163 
    164             representation_grid.append_layer(representation_layer)

~pennylane/pennylane/circuit_drawer/representation_resolver.py in element_representation(self, element, wire)
    455             return self.output_representation(element, wire)
    456 
--> 457         return self.operator_representation(element, wire)

~pennylane/pennylane/circuit_drawer/representation_resolver.py in operator_representation(self, op, wire)
    394         else:
    395             representation = "{}({})".format(
--> 396                 name, ", ".join([self.single_parameter_representation(par) for par in op.data])
    397             )
    398 

~pennylane/pennylane/circuit_drawer/representation_resolver.py in <listcomp>(.0)
    394         else:
    395             representation = "{}({})".format(
--> 396                 name, ", ".join([self.single_parameter_representation(par) for par in op.data])
    397             )
    398 

~pennylane/pennylane/circuit_drawer/representation_resolver.py in single_parameter_representation(par)
    116             return par
    117 
--> 118         return f"{1.0 * par:.3g}"
    119 
    120     @staticmethod

[antalszava]

Thanks @DSGuala for catching this! Will post a fix shortly. :)