[BUG] Hamiltonians defined using operator arithmetic queuing extra gates

[alvaro-at-xanadu]

Expected behavior

When defining

dev = qml.device('default.qubit', wires = [0,1])

def H(alpha, beta):
  coeffs = [alpha, beta]
  ops = [qml.PauliX(0) @ qml.PauliX(1),qml.PauliZ(0) @ qml.PauliZ(1)]
  return qml.dot(coeffs,ops)

def H2(alpha, beta):
  return alpha * qml.PauliX(0) @ qml.PauliX(1) + beta * qml.PauliZ(0) @ qml.PauliZ(1)

@qml.qnode(dev)
def Ham_evolve(alpha, beta, time):

    qml.evolve(H(alpha,beta), coeff = time)

    return qml.probs(wires = [0,1])
  
@qml.qnode(dev)
def Ham_evolve2(alpha, beta, time):

    qml.evolve(H2(alpha,beta), coeff = time)

    return qml.probs(wires = [0,1])

I would expect Ham_evolve and Ham_evolve2 to build the same circuit.

Actual behavior

Drawing print(qml.draw(Ham_evolve, expansion_strategy="device")(0.9,1.0,0.4)) gives the correct circuit, since the Hamiltonian was defined with qml.dot.

Drawing print(qml.draw(Ham_evolve2, expansion_strategy="device")(0.9,1.0,0.4)) queues extra gates.

This can be fixed by running qml.operation.enable_new_opmath() but it does seem like an extra step users wouldn't be aware of.

Additional information

Happens all the time with version 0.35.1, as long as new_opmath is disabled.

Source code

Working examples are above.

Tracebacks

No error tracebacks since it's a queuing issue.

System information

Name: PennyLane
Version: 0.35.1
Summary: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Home-page: https://github.com/PennyLaneAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: /usr/local/lib/python3.10/dist-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane_Lightning

Platform info:           Linux-6.1.58+-x86_64-with-glibc2.35
Python version:          3.10.12
Numpy version:           1.25.2
Scipy version:           1.11.4
Installed devices:
- lightning.qubit (PennyLane_Lightning-0.35.1)
- default.clifford (PennyLane-0.35.1)
- default.gaussian (PennyLane-0.35.1)
- default.mixed (PennyLane-0.35.1)
- default.qubit (PennyLane-0.35.1)
- default.qubit.autograd (PennyLane-0.35.1)
- default.qubit.jax (PennyLane-0.35.1)
- default.qubit.legacy (PennyLane-0.35.1)
- default.qubit.tf (PennyLane-0.35.1)
- default.qubit.torch (PennyLane-0.35.1)
- default.qutrit (PennyLane-0.35.1)
- null.qubit (PennyLane-0.35.1)

Existing GitHub issues

• I have searched existing GitHub issues to make sure the issue does not already exist.