[BUG] `qml.draw` fails for templates without a custom `adjoint` method (`expansion_strategy=None`)

Expected behavior

dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def qpe_circuit():
    qml.adjoint(qml.QuantumPhaseEstimation)(
        qml.PauliX.matrix,
        target_wires=[0],
        estimation_wires=[1],
    )
    return qml.state()

print(qml.draw(qpe_circuit)())

Executes correctly.

Actual behavior

An error is raised:

~/xanadu/pennylane/pennylane/circuit_drawer/representation_resolver.py in element_representation(self, element, wire)
    487             return self.output_representation(element, wire)
    488 
--> 489         return self.operator_representation(element, wire)

~/xanadu/pennylane/pennylane/circuit_drawer/representation_resolver.py in operator_representation(self, op, wire)
    420         elif len(qml.math.shape(op.data[0])) != 0:
    421             print(name)
--> 422             representation = name + RepresentationResolver._format_matrix_arguments(
    423                 op.data, "M", self.matrix_cache
    424             )

TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'

Additional information

The issue seems to arise with any template that doesn’t define the adjoint method. The specific line in the traceback errors out because a QuantumTape object is being processed and it doesn’t have a name attribute defined.

When expansion_strategy=device is set, drawing concludes successfully and draws the circuit as expected.

Source code

No response

Tracebacks

No response

System information

Name: PennyLane
Version: 0.20.0.dev0
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: /home/antal/xanadu/pennylane
Requires: numpy, scipy, networkx, autograd, toml, appdirs, semantic_version, autoray, cachetools, pennylane-lightning
Required-by: PennyLane-Cirq, PennyLane-Orquestra, PennyLane-SF, pennylane-qulacs, PennyLane-IonQ, amazon-braket-pennylane-plugin, PennyLane-Honeywell, PennyLane-qiskit, PennyLane-AQT, PennyLane-Qchem, PennyLane-PQ, PennyLane-Forest, PennyLane-Lightning, PennyLane-qsharp
Platform info:           Linux-5.11.0-40-generic-x86_64-with-glibc2.10
Python version:          3.8.5
Numpy version:           1.21.4
Scipy version:           1.7.3
Installed devices:
- cirq.mixedsimulator (PennyLane-Cirq-0.17.1)
- cirq.pasqal (PennyLane-Cirq-0.17.1)
- cirq.qsim (PennyLane-Cirq-0.17.1)
- cirq.qsimh (PennyLane-Cirq-0.17.1)
- cirq.simulator (PennyLane-Cirq-0.17.1)
- orquestra.forest (PennyLane-Orquestra-0.15.0)
- orquestra.ibmq (PennyLane-Orquestra-0.15.0)
- orquestra.qiskit (PennyLane-Orquestra-0.15.0)
- orquestra.qulacs (PennyLane-Orquestra-0.15.0)
- strawberryfields.fock (PennyLane-SF-0.16.0.dev0)
- strawberryfields.gaussian (PennyLane-SF-0.16.0.dev0)
- strawberryfields.gbs (PennyLane-SF-0.16.0.dev0)
- strawberryfields.remote (PennyLane-SF-0.16.0.dev0)
- strawberryfields.tf (PennyLane-SF-0.16.0.dev0)
- qulacs.simulator (pennylane-qulacs-0.17.0.dev0)
- ionq.qpu (PennyLane-IonQ-0.17.0.dev0)
- ionq.simulator (PennyLane-IonQ-0.17.0.dev0)
- braket.aws.qubit (amazon-braket-pennylane-plugin-1.4.1.dev0)
- braket.local.qubit (amazon-braket-pennylane-plugin-1.4.1.dev0)
- honeywell.hqs (PennyLane-Honeywell-0.16.0.dev0)
- qiskit.aer (PennyLane-qiskit-0.18.0.dev0)
- qiskit.basicaer (PennyLane-qiskit-0.18.0.dev0)
- qiskit.ibmq (PennyLane-qiskit-0.18.0.dev0)
- aqt.noisy_sim (PennyLane-AQT-0.18.0)
- aqt.sim (PennyLane-AQT-0.18.0)
- projectq.classical (PennyLane-PQ-0.18.0.dev0)
- projectq.ibm (PennyLane-PQ-0.18.0.dev0)
- projectq.simulator (PennyLane-PQ-0.18.0.dev0)
- forest.numpy_wavefunction (PennyLane-Forest-0.18.0.dev0)
- forest.qvm (PennyLane-Forest-0.18.0.dev0)
- forest.wavefunction (PennyLane-Forest-0.18.0.dev0)
- lightning.qubit (PennyLane-Lightning-0.19.0.dev0)
- microsoft.QuantumSimulator (PennyLane-qsharp-0.19.0)
- default.gaussian (PennyLane-0.20.0.dev0)
- default.mixed (PennyLane-0.20.0.dev0)
- default.qubit (PennyLane-0.20.0.dev0)
- default.qubit.autograd (PennyLane-0.20.0.dev0)
- default.qubit.jax (PennyLane-0.20.0.dev0)
- default.qubit.tf (PennyLane-0.20.0.dev0)
- default.qubit.torch (PennyLane-0.20.0.dev0)
- default.tensor (PennyLane-0.20.0.dev0)
- default.tensor.tf (PennyLane-0.20.0.dev0)

### 

- [X] I have searched exisisting GitHub issues to make sure the issue does not already exist.

[dime10]

What if we followed case 1 and drew the tape to look as if it was its inverse? Could we get away with the user not knowing if there’s a real operation object or a tape that represents the inverse of the operation?

So like

 0: ──╭QuantumPhaseEstimation(M0)⁻¹──╭┤ State 
 1: ──╰QuantumPhaseEstimation(M0)⁻¹──╰┤ State 
M0 =
[[0 1]
 [1 0]]

The problem is that the information about the template is lost. Since the adjoint method is not defined for QuantumPhaseEstimation, the template is expanded into a tape and the adjoint is computed on the tape. All the drawer then sees is a tape with operations inside. So while in this instance the tape came from an adjointed template, it could just as well come from the manual placement of a tape in the operations list:

@qml.qnode(dev)
def tape_circuit():
    with qml.tape.QuantumTape():
        qml.Hadamard(wires=0)
        qml.CNOT(wires=[0,1])
    return qml.state()

Note that the desired behaviour you describe does happen when a template defines the adjoint method via .inv(), for example the QFT template:

@qml.qnode(dev)
def tape_circuit():
   qml.adjoint(qml.QFT)(wires=[1])
   return qml.state()

prints as:

1: ──QFT⁻¹──┤ State 

In specific, as a user, I’d expect that simply using qml.adjoint will not alter the way the circuit is being drawn. If I see QuantumPhaseEstimation, then I’d expect its inverse to pop up as a single object. If I see the decomposition of QuantumPhaseEstimation, then I’d expect a list of operations to pop up for its inverse.

So I think in addition to supporting tapes in the drawer, a fix to this bug could include defining the adjoint method for the QuantumPhaseEstimation template so that its adjoint is properly drawn.

[dime10]

#1973 removed the issue for the QuantumPhaseEstimation template.