Qulacs error message

Hi! I tried running pennylane with qulacs plugin recently and received error messages which I did not see before. Kindly help thanks!

Test code:

import pennylane as qml
dev = qml.device('qulacs.simulator',wires=2)

def circuit(x, y, z):
    qml.RZ(z, wires=[0])
    qml.RY(y, wires=[0])
    qml.RX(x, wires=[0])
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(wires=1))
circuit(0.2, 0.1, 0.3)

If you want help with diagnosing an error, please put the full error message below:

AttributeError                            Traceback (most recent call last)
Cell In [8], line 12
     10     qml.CNOT(wires=[0, 1])
     11     return qml.expval(qml.PauliZ(wires=1))
---> 12 circuit(0.2, 0.1, 0.3)

File ~\anaconda3\lib\site-packages\pennylane\qnode.py:950, in QNode.__call__(self, *args, **kwargs)
    948     self.execute_kwargs.pop("mode")
    949 # pylint: disable=unexpected-keyword-arg
--> 950 res = qml.execute(
    951     [self.tape],
    952     device=self.device,
    953     gradient_fn=self.gradient_fn,
    954     interface=self.interface,
    955     gradient_kwargs=self.gradient_kwargs,
    956     override_shots=override_shots,
    957     **self.execute_kwargs,
    958 )
    960 res = res[0]
    962 # convert result to the interface in case the qfunc has no parameters

File ~\anaconda3\lib\site-packages\pennylane\interfaces\execution.py:642, in execute(tapes, device, gradient_fn, interface, grad_on_execution, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform)
    639     elif mapped_interface == "jax":
    640         _execute = _get_jax_execute_fn(interface, tapes)
--> 642     res = _execute(
    643         tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n=1, max_diff=max_diff
    644     )
    646 except ImportError as e:
    647     raise qml.QuantumFunctionError(
    648         f"{mapped_interface} not found. Please install the latest "
    649         f"version of {mapped_interface} to enable the '{mapped_interface}' interface."
    650     ) from e

File ~\anaconda3\lib\site-packages\pennylane\interfaces\autograd.py:319, in execute(tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n, max_diff)
    314 # pylint misidentifies autograd.builtins as a dict
    315 # pylint: disable=no-member
    316 parameters = autograd.builtins.tuple(
    317     [autograd.builtins.list(t.get_parameters()) for t in tapes]
    318 )
--> 319 return _execute(
    320     parameters,
    321     tapes=tapes,
    322     device=device,
    323     execute_fn=execute_fn,
    324     gradient_fn=gradient_fn,
    325     gradient_kwargs=gradient_kwargs,
    326     _n=_n,
    327     max_diff=max_diff,
    328 )[0]

File ~\anaconda3\lib\site-packages\autograd\tracer.py:48, in primitive.<locals>.f_wrapped(*args, **kwargs)
     46     return new_box(ans, trace, node)
     47 else:
---> 48     return f_raw(*args, **kwargs)

File ~\anaconda3\lib\site-packages\pennylane\interfaces\autograd.py:363, in _execute(parameters, tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n, max_diff)
    342 """Autodifferentiable wrapper around ``Device.batch_execute``.
    344 The signature of this function is designed to work around Autograd restrictions.
    360 understand the consequences!
    361 """
    362 unwrapped_tapes = tuple(convert_to_numpy_parameters(t) for t in tapes)
--> 363 res, jacs = execute_fn(unwrapped_tapes, **gradient_kwargs)
    365 return res, jacs

File ~\anaconda3\lib\site-packages\pennylane\interfaces\execution.py:287, in cache_execute.<locals>.wrapper(tapes, **kwargs)
    282         return (res, []) if return_tuple else res
    284 else:
    285     # execute all unique tapes that do not exist in the cache
    286     # convert to list as new device interface returns a tuple
--> 287     res = list(fn(execution_tapes.values(), **kwargs))
    289 final_res = []
    291 for i, tape in enumerate(tapes):

File ~\anaconda3\lib\site-packages\pennylane\interfaces\execution.py:210, in cache_execute.<locals>.fn(tapes, **kwargs)
    208 def fn(tapes: Sequence[QuantumTape], **kwargs):  # pylint: disable=function-redefined
    209     tapes = [expand_fn(tape) for tape in tapes]
--> 210     return original_fn(tapes, **kwargs)

File ~\anaconda3\lib\contextlib.py:79, in ContextDecorator.__call__.<locals>.inner(*args, **kwds)
     76 @wraps(func)
     77 def inner(*args, **kwds):
     78     with self._recreate_cm():
---> 79         return func(*args, **kwds)

File ~\anaconda3\lib\site-packages\pennylane\_qubit_device.py:603, in QubitDevice.batch_execute(self, circuits)
    598 for circuit in circuits:
    599     # we need to reset the device here, else it will
    600     # not start the next computation in the zero state
    601     self.reset()
--> 603     res = self.execute(circuit)
    604     results.append(res)
    606 if self.tracker.active:

File ~\anaconda3\lib\site-packages\pennylane\_qubit_device.py:320, in QubitDevice.execute(self, circuit, **kwargs)
    317 self.check_validity(circuit.operations, circuit.observables)
    319 # apply all circuit operations
--> 320 self.apply(circuit.operations, rotations=self._get_diagonalizing_gates(circuit), **kwargs)
    322 # generate computational basis samples
    323 if self.shots is not None or circuit.is_sampled:

File ~\anaconda3\lib\site-packages\pennylane_qulacs\qulacs_device.py:147, in QulacsDevice.apply(self, operations, **kwargs)
    144 def apply(self, operations, **kwargs):
    145     rotations = kwargs.get("rotations", [])
--> 147     self.apply_operations(operations)
    148     self._pre_rotated_state = self._state.copy()
    150     # Rotating the state for measurement in the computational basis

File ~\anaconda3\lib\site-packages\pennylane_qulacs\qulacs_device.py:179, in QulacsDevice.apply_operations(self, operations)
    177     self._apply_matrix(op)
    178 else:
--> 179     self._apply_gate(op)

File ~\anaconda3\lib\site-packages\pennylane_qulacs\qulacs_device.py:287, in QulacsDevice._apply_gate(self, op)
    284 par = op.parameters
    286 mapped_operation = self._operation_map[op.name]
--> 287 if op.inverse:
    288     mapped_operation = self._get_inverse_operation(mapped_operation, device_wires, par)
    290 # Negating the parameters such that it adheres to qulacs

AttributeError: 'RZ' object has no attribute 'inverse'

And, finally, make sure to include the versions of your packages. Specifically, show us the output of qml.about().
Name: PennyLane
Version: 0.31.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: GitHub - PennyLaneAI/pennylane: PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
License: Apache License 2.0
Location: c:\users\sorac\anaconda3\lib\site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane-Lightning, PennyLane-Orquestra, pennylane-qulacs

Platform info: Windows-10-10.0.19045-SP0
Python version: 3.9.13
Numpy version: 1.21.5
Scipy version: 1.9.3
Installed devices:

  • default.gaussian (PennyLane-0.31.1)
  • default.mixed (PennyLane-0.31.1)
  • default.qubit (PennyLane-0.31.1)
  • default.qubit.autograd (PennyLane-0.31.1)
  • default.qubit.jax (PennyLane-0.31.1)
  • default.qubit.tf (PennyLane-0.31.1)
  • default.qubit.torch (PennyLane-0.31.1)
  • default.qutrit (PennyLane-0.31.1)
  • null.qubit (PennyLane-0.31.1)
  • lightning.qubit (PennyLane-Lightning-0.31.0)
  • qulacs.simulator (pennylane-qulacs-0.24.0)

Hello @leongfy, I ran your circuit after !pip install qulacs and received:

Hello @leongfy , thank you for your question and thank you @kevinkawchak for your input in
helping out.

From your output for qml.about(), I can see that you are working with an older
version of PennyLane and the pennylane-qulacs plugin. I was able to replicate your error
using the versions of PennyLane and the pennylane-qulacs plugin that you are using.

When I use the most up to date versions of pennylane (0.32.0) and the pennylane-qulacs (0.32.0), I was able to run your code without getting an error. I would recommend upgrading your PennyLane and pennylane-qulacs to their most recent versions. If you are using pip, you can do this by typing

pip install pennylane pennylane-qulacs --upgrade

Please let me know if that works for you.

Also, we have a new PennyLane survey that you may be interested in.

We are interested in collecting feedback from users like you @leongfy on how we can keep improving PennyLane. If you would like to a moment to fill out this short survey, we would really appreciate it.

That works! Thank you very much.

1 Like