Hello! I’m currently trying to use qml.qinfo.transforms.quantum_fisher to calculate the quantum Fisher information matrix (QFIM) of a certain circuit called U1_qfi. However, I’m having an issue with the calculation in Pennylane. The code I’m running to calculate the QFIM is
n_wires = 5
random_seed = 25
perceptron_qfi = QuantumPerceptron(n_wires, L=2)
@qml.qnode(dev_qfi2, interface='jax')
def U1_qfi(params, L = 1):
start_index = 0
n_params_in_layer = (2 + 2)
for i in range(L):
params_layer = params[start_index:start_index + n_params_in_layer]
qml.evolve(perceptron_qfi.H)(params_layer[:2], t)
for j in range(n_wires): # Single-qubit X rotations
qml.RX(params_layer[2], wires=j)
for k in range(n_wires): # Single-qubit Y rotations
qml.RY(params_layer[3], wires=k)
start_index += n_params_in_layer
return qml.expval(qml.sum(*[qml.PauliZ(i) for i in range(n_wires)]))
L_qfi = perceptron_qfi.L
params_qfi = perceptron_qfi.get_random_parameter_vector(random_seed)
qfim = qml.qinfo.quantum_fisher(U1_qfi)(params_qfi, L = L_qfi)
The error I’m getting is the following:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/.../.ipynb Cell 25 line 1
7 params_qfi = perceptron_qfi.get_random_parameter_vector(random_seed)
9 # qfim = calculate_QFI(U1_qfi, params_qfi, L_qfi)
---> 10 qfim = qml.qinfo.quantum_fisher(U1_qfi)(params_qfi, L = L_qfi)
11 qfim.shape
File ~/.../site-packages/pennylane/qnode.py:1027, in QNode.__call__(self, *args, **kwargs)
1022 full_transform_program._set_all_argnums(
1023 self, args, kwargs, argnums
1024 ) # pylint: disable=protected-access
1026 # pylint: disable=unexpected-keyword-arg
-> 1027 res = qml.execute(
1028 (self._tape,),
1029 device=self.device,
1030 gradient_fn=self.gradient_fn,
1031 interface=self.interface,
1032 transform_program=full_transform_program,
1033 config=config,
1034 gradient_kwargs=self.gradient_kwargs,
1035 override_shots=override_shots,
1036 **self.execute_kwargs,
1037 )
1039 res = res[0]
1041 # convert result to the interface in case the qfunc has no parameters
File ~/.../site-packages/pennylane/interfaces/execution.py:612, in execute(tapes, device, gradient_fn, interface, transform_program, config, grad_on_execution, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform)
609 return program_post_processing(program_pre_processing(results))
611 if transform_program.is_informative:
--> 612 return post_processing(tapes)
614 # Exiting early if we do not need to deal with an interface boundary
615 if no_interface_boundary_required:
File ~/.../site-packages/pennylane/transforms/core/transform_program.py:86, in _apply_postprocessing_stack(results, postprocessing_stack)
63 """Applies the postprocessing and cotransform postprocessing functions in a Last-In-First-Out LIFO manner.
64
65 Args:
(...)
83
84 """
85 for postprocessing in reversed(postprocessing_stack):
---> 86 results = postprocessing(results)
87 return results
File ~/.../site-packages/pennylane/transforms/core/transform_program.py:56, in _batch_postprocessing(results, individual_fns, slices)
30 def _batch_postprocessing(
31 results: ResultBatch, individual_fns: List[PostProcessingFn], slices: List[slice]
32 ) -> ResultBatch:
33 """Broadcast individual post processing functions onto their respective tapes.
34
35 Args:
(...)
54
55 """
---> 56 return tuple(fn(results[sl]) for fn, sl in zip(individual_fns, slices))
File ~/.../site-packages/pennylane/transforms/core/transform_program.py:56, in <genexpr>(.0)
30 def _batch_postprocessing(
31 results: ResultBatch, individual_fns: List[PostProcessingFn], slices: List[slice]
32 ) -> ResultBatch:
33 """Broadcast individual post processing functions onto their respective tapes.
34
35 Args:
(...)
54
55 """
---> 56 return tuple(fn(results[sl]) for fn, sl in zip(individual_fns, slices))
File ~/.../site-packages/pennylane/qinfo/transforms.py:752, in quantum_fisher.<locals>.processing_fn_multiply(r)
750 def processing_fn_multiply(r): # pylint: disable=function-redefined
751 print("Value of r:", r)
--> 752 r = qml.math.stack(r)
753 return 4 * r
File ~/.../site-packages/pennylane/math/multi_dispatch.py:151, in multi_dispatch.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
148 interface = interface or get_interface(*dispatch_args)
149 kwargs["like"] = interface
--> 151 return fn(*args, **kwargs)
File ~/.../site-packages/pennylane/math/multi_dispatch.py:488, in stack(values, axis, like)
459 """Stack a sequence of tensors along the specified axis.
460
461 .. warning::
(...)
485 [5.00e+00, 8.00e+00, 1.01e+02]], dtype=float32)>
486 """
487 values = np.coerce(values, like=like)
--> 488 return np.stack(values, axis=axis, like=like)
File ~/.../site-packages/autoray/autoray.py:80, in do(fn, like, *args, **kwargs)
31 """Do function named ``fn`` on ``(*args, **kwargs)``, peforming single
32 dispatch to retrieve ``fn`` based on whichever library defines the class of
33 the ``args[0]``, or the ``like`` keyword argument if specified.
(...)
77 <tf.Tensor: id=91, shape=(3, 3), dtype=float32>
78 """
79 backend = choose_backend(fn, *args, like=like, **kwargs)
---> 80 return get_lib_fn(backend, fn)(*args, **kwargs)
File ~/.../site-packages/numpy/core/shape_base.py:445, in stack(arrays, axis, out, dtype, casting)
443 arrays = [asanyarray(arr) for arr in arrays]
444 if not arrays:
--> 445 raise ValueError('need at least one array to stack')
447 shapes = {arr.shape for arr in arrays}
448 if len(shapes) != 1:
ValueError: need at least one array to stack
So it seems like this error pertains to some variable r which is empty and thus np.stack() doesn’t work. To make this reproducible on your machine, the perceptron_qfi.H used in the qml.evolve method in U1_qfi is a parameterized Hamiltonian for 5 wires given as
(constant(params_0, t)*((PauliZ(wires=[0]) @ PauliZ(wires=[1])) + (PauliZ(wires=[1]) @ PauliZ(wires=[2])) + (PauliZ(wires=[2]) @ PauliZ(wires=[3])) + (PauliZ(wires=[3]) @ PauliZ(wires=[4]))))
+ (constant(params_1, t)*(Identity(wires=[0]) + Identity(wires=[1]) + Identity(wires=[2]) + Identity(wires=[3]) + Identity(wires=[4])))
Also, params_qfi is just a JAX array of parameters which has a number of elements that depends on L_qfi. For the case of L_qfi=1, params_qfi is a JAX array with four elements. When L_qfi=2, params_qfi has eight elements.
This is the output of qml.about()
Name: PennyLane
Version: 0.33.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/PennyLaneAI/pennylane
Author:
Author-email:
License: Apache License 2.0
Location: /.../python3.11/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane-Lightning
Platform info: macOS-13.4.1-x86_64-i386-64bit
Python version: 3.11.5
Numpy version: 1.26.2
Scipy version: 1.11.4
Installed devices:
- default.gaussian (PennyLane-0.33.1)
- default.mixed (PennyLane-0.33.1)
- default.qubit (PennyLane-0.33.1)
- default.qubit.autograd (PennyLane-0.33.1)
- default.qubit.jax (PennyLane-0.33.1)
- default.qubit.legacy (PennyLane-0.33.1)
- default.qubit.tf (PennyLane-0.33.1)
- default.qubit.torch (PennyLane-0.33.1)
- default.qutrit (PennyLane-0.33.1)
- null.qubit (PennyLane-0.33.1)
- lightning.qubit (PennyLane-Lightning-0.33.1)
Thank you in advance for your help! 
, so we should be able to fix master in the next few days. Our next release, v0.35, will be coming out on Tuesday March 5th, so it should be fixed by then.