KeyError Traceback (most recent call last)
Cell In[6], line 5
3 q_weights_flat = np.array([0.5,0.5,0.6,0.7],requires_grad=True)
4 pool_weights_flat = np.array([0.3,0.4,1,1],requires_grad=True)
----> 5 q_net(q_in, c_weights_flat, q_weights_flat, pool_weights_flat)
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane/qnode.py:847, in QNode.call(self, *args, **kwargs)
843 self._update_original_device()
845 return res
→ 847 res = qml.execute(
848 [self.tape],
849 device=self.device,
850 gradient_fn=self.gradient_fn,
851 interface=self.interface,
852 gradient_kwargs=self.gradient_kwargs,
853 override_shots=override_shots,
854 **self.execute_kwargs,
855 )
857 if old_interface == “auto”:
858 self.interface = “auto”
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane/interfaces/execution.py:724, in execute(tapes, device, gradient_fn, interface, mode, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform)
718 except ImportError as e:
719 raise qml.QuantumFunctionError(
720 f"{mapped_interface} not found. Please install the latest "
721 f"version of {mapped_interface} to enable the ‘{mapped_interface}’ interface."
722 ) from e
→ 724 res = _execute(
725 tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n=1, max_diff=max_diff, mode=_mode
726 )
728 return batch_fn(res)
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane/interfaces/autograd.py:81, in execute(tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n, max_diff, mode)
75 # pylint misidentifies autograd.builtins as a dict
76 # pylint: disable=no-member
77 parameters = autograd.builtins.tuple(
78 [autograd.builtins.list(t.get_parameters()) for t in tapes]
79 )
—> 81 return _execute(
82 parameters,
83 tapes=tapes,
84 device=device,
85 execute_fn=execute_fn,
86 gradient_fn=gradient_fn,
87 gradient_kwargs=gradient_kwargs,
88 _n=_n,
89 max_diff=max_diff,
90 )[0]
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/autograd/tracer.py:48, in primitive..f_wrapped(*args, **kwargs)
46 return new_box(ans, trace, node)
47 else:
—> 48 return f_raw(*args, **kwargs)
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane/interfaces/autograd.py:125, in _execute(parameters, tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n, max_diff)
104 “”“Autodifferentiable wrapper around Device.batch_execute.
105
106 The signature of this function is designed to work around Autograd restrictions.
(…)
122 understand the consequences!
123 “””
124 with qml.tape.Unwrap(*tapes):
→ 125 res, jacs = execute_fn(tapes, **gradient_kwargs)
127 for i, r in enumerate(res):
128 if any(isinstance(m, CountsMP) for m in tapes[i].measurements):
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/contextlib.py:79, in ContextDecorator.call..inner(*args, **kwds)
76 @wraps(func)
77 def inner(*args, **kwds):
78 with self._recreate_cm():
—> 79 return func(*args, **kwds)
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane/_device.py:575, in Device.execute_and_gradients(self, circuits, method, **kwargs)
570 for circuit in circuits:
571 # Evaluations and gradients are paired, so that
572 # devices can re-use the device state for the
573 # gradient computation (if applicable).
574 res.append(self.batch_execute([circuit])[0])
→ 575 jacs.append(gradient_method(circuit, **kwargs))
577 return res, jacs
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane_lightning/lightning_qubit.py:553, in LightningQubit.adjoint_jacobian(self, tape, starting_state, use_device_state)
546 raise QuantumFunctionError(
547 "This method does not support statevector return type. "
548 “Use vjp method instead for this purpose.”
549 )
551 self._check_adjdiff_supported_operations(tape.operations)
→ 553 processed_data = self._process_jacobian_tape(tape, starting_state, use_device_state)
555 if not processed_data: # training_params is empty
556 return np.array(, dtype=self._state.dtype)
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane_lightning/lightning_qubit.py:492, in LightningQubit._process_jacobian_tape(self, tape, starting_state, use_device_state)
489 ket = self._pre_rotated_state
491 obs_serialized = _serialize_observables(tape, self.wire_map, use_csingle=self.use_csingle)
→ 492 ops_serialized, use_sp = _serialize_ops(tape, self.wire_map)
494 ops_serialized = create_ops_list(*ops_serialized)
496 # We need to filter out indices in trainable_params which do not
497 # correspond to operators.
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane_lightning/_serialize.py:190, in _serialize_ops(tape, wires_map)
187 mats.append()
189 wires_list = single_op.wires.tolist()
→ 190 wires.append([wires_map[w] for w in wires_list])
192 inverses = [False] * len(names)
193 return (names, params, wires, inverses, mats), uses_stateprep
File ~/anaconda3/envs/env_pennylane_gpu/lib/python3.10/site-packages/pennylane_lightning/_serialize.py:190, in (.0)
187 mats.append()
189 wires_list = single_op.wires.tolist()
→ 190 wires.append([wires_map[w] for w in wires_list])
192 inverses = [False] * len(names)
193 return (names, params, wires, inverses, mats), uses_stateprep
KeyError: tensor(1, requires_grad=True)