Hello everyone! I was trying to use the qiskit plugin to get access to IBM QPUs, but I get the following error when measuring the expectation value of an operator: ValueError: The number of qubits of the circuit (127) does not match the number of qubits of the (0,)-th observable (4). According to this post the error is caused by the observable not being expressed using the Instruction Set Architecture of the QPU. How can I perform this optimisation in Pennylane, if this is the issue?
import pennylane as qml
from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService()
backend = service.least_busy(min_num_qubits=4)
print(backend.name)
device = qml.device("qiskit.remote", wires=4, backend=backend)
@qml.qnode(device)
def circuit_probs():
qml.Hadamard(wires=0)
return qml.probs()
@qml.qnode(device)
def circuit_expval():
qml.Hadamard(wires=0)
return qml.expval(qml.PauliZ(0))
print(circuit_probs())
print(circuit_expval())
If you want help with diagnosing an error, please put the full error message below:
---------------------------------------------------------------------------
ValueError Traceback (most recent caall last)
Cell In[1], line 19
16 return qml.expval(qml.PauliZ(0))
18 print(circuit_probs())
---> 19 print(circuit_expval())
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/workflow/qnode.py:1020, in QNode.__caall__(self, *args, **kwargs)
1018 if qml.capture.enabled():
1019 return qml.capture.qnode_caall(self, *args, **kwargs)
-> 1020 return self._impl_caall(*args, **kwargs)
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/workflow/qnode.py:1008, in QNode._impl_caall(self, *args, **kwargs)
1005 self._update_gradient_fn(shots=override_shots, tape=self._tape)
1007 try:
-> 1008 res = self._execution_component(args, kwargs, override_shots=override_shots)
1009 finally:
1010 if old_interface == "auto":
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/workflow/qnode.py:957, in QNode._execution_component(self, args, kwargs, override_shots)
951 warnings.filterwarnings(
952 action="ignore",
953 message=r".*argument is deprecated and will be removed in version 0.39.*",
954 category=qml.PennyLaneDeprecationWarning,
955 )
956 # pylint: disable=unexpected-keyword-arg
--> 957 res = qml.execute(
958 (self._tape,),
959 device=self.device,
960 gradient_fn=self.gradient_fn,
961 interface=self.interface,
962 transform_program=full_transform_program,
963 inner_transform=inner_transform_program,
964 config=config,
965 gradient_kwargs=self.gradient_kwargs,
966 override_shots=override_shots,
967 **execute_kwargs,
968 )
969 res = res[0]
971 # convert result to the interface in case the qfunc has no parameters
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/workflow/execution.py:771, in execute(tapes, device, gradient_fn, interface, transform_program, inner_transform, config, grad_on_execution, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform, device_vjp, mcm_config)
763 ml_boundary_execute = _get_ml_boundary_execute(
764 interface,
765 config.grad_on_execution,
766 config.use_device_jacobian_product,
767 differentiable=max_diff > 1,
768 )
770 if interface in jpc_interfaces:
--> 771 results = ml_boundary_execute(tapes, execute_fn, jpc, device=device)
772 else:
773 results = ml_boundary_execute(
774 tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n=1, max_diff=max_diff
775 )
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/workflow/interfaces/autograd.py:147, in autograd_execute(tapes, execute_fn, jpc, device)
142 # pylint misidentifies autograd.builtins as a dict
143 # pylint: disable=no-member
144 parameters = autograd.builtins.tuple(
145 [autograd.builtins.list(t.get_parameters()) for t in tapes]
146 )
--> 147 return _execute(parameters, tuple(tapes), execute_fn, jpc)
File ~/miniforge3/envs/stations/lib/python3.10/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 ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/workflow/interfaces/autograd.py:168, in _execute(parameters, tapes, execute_fn, jpc)
150 @autograd.extend.primitive
151 def _execute(
152 parameters,
(...)
155 jpc,
156 ): # pylint: disable=unused-argument
157 """Autodifferentiable wrapper around a way of executing tapes.
158
159 Args:
(...)
166
167 """
--> 168 return execute_fn(tapes)
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/workflow/execution.py:212, in _make_inner_execute.<locals>.inner_execute(tapes, **_)
209 transformed_tapes, transform_post_processing = transform_program(tapes)
211 if transformed_tapes:
--> 212 results = device.execute(transformed_tapes, execution_config=execution_config)
213 else:
214 results = ()
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane_qiskit/qiskit_device.py:66, in custom_simulator_tracking.<locals>.execute(self, circuits, execution_config)
64 @wraps(tracked_execute)
65 def execute(self, circuits, execution_config=DefaultExecutionConfig):
---> 66 results = tracked_execute(self, circuits, execution_config)
67 if self.tracker.active:
68 res = []
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane/devices/modifiers/simulator_tracking.py:30, in _track_execute.<locals>.execute(self, circuits, execution_config)
28 @wraps(untracked_execute)
29 def execute(self, circuits, execution_config=DefaultExecutionConfig):
---> 30 results = untracked_execute(self, circuits, execution_config)
31 if isinstance(circuits, QuantumScript):
32 batch = (circuits,)
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane_qiskit/qiskit_device.py:590, in QiskitDevice.execute(self, circuits, execution_config)
587 finally:
588 session.close()
--> 590 with execute_circuits(session) as results:
591 return results
File ~/miniforge3/envs/stations/lib/python3.10/contextlib.py:135, in _GeneratorContextManager.__enter__(self)
133 del self.args, self.kwds, self.func
134 try:
--> 135 return next(self.gen)
136 except StopIteration:
137 raise RuntimeError("generator didn't yield") from None
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane_qiskit/qiskit_device.py:585, in QiskitDevice.execute.<locals>.execute_circuits(session)
583 else:
584 execute_fn = self._execute_sampler
--> 585 results.append(execute_fn(circ, session))
586 yield results
587 finally:
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/pennylane_qiskit/qiskit_device.py:656, in QiskitDevice._execute_estimator(self, circuit, session)
651 # split into one caall per measurement
652 # could technically be more efficient if there are some observables where we ask
653 # for expectation value and variance on the same observable, but spending time on
654 # that right now feels excessive
655 circ_and_obs = [(compiled_circuits[0], pauli_observables)]
--> 656 result = estimator.run(
657 circ_and_obs,
658 precision=np.sqrt(1 / circuit.shots.total_shots) if circuit.shots else None,
659 ).result()
660 self._current_job = result
661 result = self._process_estimator_job(circuit.measurements, result)
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/qiskit_ibm_runtime/estimator.py:191, in EstimatorV2.run(self, pubs, precision)
189 if precision <= 0:
190 raise ValueError("The precision value must be strictly greater than 0.")
--> 191 coerced_pubs = [EstimatorPub.coerce(pub, precision) for pub in pubs]
192 validate_estimator_pubs(coerced_pubs)
193 return self._run(coerced_pubs)
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/qiskit_ibm_runtime/estimator.py:191, in <listcomp>(.0)
189 if precision <= 0:
190 raise ValueError("The precision value must be strictly greater than 0.")
--> 191 coerced_pubs = [EstimatorPub.coerce(pub, precision) for pub in pubs]
192 validate_estimator_pubs(coerced_pubs)
193 return self._run(coerced_pubs)
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/qiskit/primitives/containers/estimator_pub.py:162, in EstimatorPub.coerce(cls, pub, precision)
159 if len(pub) > 3 and pub[3] is not None:
160 precision = pub[3]
--> 162 return cls(
163 circuit=circuit,
164 observables=observables,
165 parameter_values=parameter_values,
166 precision=precision,
167 validate=True,
168 )
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/qiskit/primitives/containers/estimator_pub.py:85, in EstimatorPub.__init__(self, circuit, observables, parameter_values, precision, validate)
79 raise ValueError(
80 f"The observables shape {self.observables.shape} and the "
81 f"parameter values shape {self.parameter_values.shape} are not broadcastable."
82 ) from ex
84 if validate:
---> 85 self.validate()
File ~/miniforge3/envs/stations/lib/python3.10/site-packages/qiskit/primitives/containers/estimator_pub.py:188, in EstimatorPub.validate(self)
186 num_qubits = len(next(iter(observable)))
187 if self.circuit.num_qubits != num_qubits:
--> 188 raise ValueError(
189 f"The number of qubits of the circuit ({self.circuit.num_qubits}) does "
190 f"not match the number of qubits of the {i}-th observable ({num_qubits})."
191 )
193 # Cross validate circuits and parameter_values
194 num_parameters = self.parameter_values.num_parameters
ValueError: The number of qubits of the circuit (127) does not match the number of qubits of the (0,)-th observable (4).
And, finally, make sure to include the versions of your packages. Specifically, show us the output of qml.about().
Name: PennyLane
Version: 0.38.0
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: /Users/manu/miniforge3/envs/stations/lib/python3.10/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, packaging, pennylane-lightning, requests, rustworkx, scipy, toml, typing-extensions
Required-by: PennyLane-qiskit, PennyLane_Lightning
Platform info: macOS-14.7-x86_64-i386-64bit
Python version: 3.10.13
Numpy version: 1.26.4
Scipy version: 1.12.0
Installed devices:
- qiskit.aer (PennyLane-qiskit-0.38.0)
- qiskit.basicaer (PennyLane-qiskit-0.38.0)
- qiskit.basicsim (PennyLane-qiskit-0.38.0)
- qiskit.remote (PennyLane-qiskit-0.38.0)
- lightning.qubit (PennyLane_Lightning-0.38.0)
- default.clifford (PennyLane-0.38.0)
- default.gaussian (PennyLane-0.38.0)
- default.mixed (PennyLane-0.38.0)
- default.qubit (PennyLane-0.38.0)
- default.qubit.autograd (PennyLane-0.38.0)
- default.qubit.jax (PennyLane-0.38.0)
- default.qubit.legacy (PennyLane-0.38.0)
- default.qubit.tf (PennyLane-0.38.0)
- default.qubit.torch (PennyLane-0.38.0)
- default.qutrit (PennyLane-0.38.0)
- default.qutrit.mixed (PennyLane-0.38.0)
- default.tensor (PennyLane-0.38.0)
- null.qubit (PennyLane-0.38.0)