# How do I operate between to different measurement processes for my loss function? **URL:** \ **Category:** PennyLane Help\ **Created:** [August 13, 2024, 9:56pm UTC](https://discuss.pennylane.ai/t/how-do-i-operate-between-to-different-measurement-processes-for-my-loss-function/5041 "2024-08-13T21:56:41Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![NickGut0711](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/nickgut0711/32/2137_2.png) [@NickGut0711](https://discuss.pennylane.ai/u/NickGut0711)\ **Post date:** [August 13, 2024, 9:56pm UTC](https://discuss.pennylane.ai/t/how-do-i-operate-between-to-different-measurement-processes-for-my-loss-function/5041/1 "2024-08-13T21:56:41Z")
Hello! I’m trying to calculate a certain quantity for my loss function. This quantity involves operating between two different measurement processes. For example, my loss function is ```auto S_x = (1/2)*sum([qml.PauliX(i) for i in range(qnn_layer.n_qubits)]) S_z = (1/2)*sum([qml.PauliZ(i) for i in range(qnn_layer.n_qubits)]) def get_loss_function(qnn_layer, ts, S_x, S_z, dev): @jax.jit @qml.qnode(dev, interface="jax") def loss(param_vector): # hamitlonian_params = dictionary_to_hamiltonian_parameters(params_dict) param_list = qnn_layer.vector_to_hamiltonian_parameters(param_vector) qml.evolve(qnn_layer.H)(param_list, ts) return (-1) * qnn_layer.n_qubits * (qml.var(S_z) / qml.expval(S_x)) return loss # Getting the loss_function loss = get_loss_function(qnn_layer, ts, S_x, S_z, dev) # Initializing parameters param_vector = qnn_layer.get_random_parameter_vector(seed) print(f'Initial parameters: {param_vector}') print(f'Initial loss: {loss(param_vector)}') ``` However, I get an error that tells me that I cannot operate (in this case, divide) between two different measurement processes, namely ‘VarianceMP’ and ‘ExpectationMP’. ```auto --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[17], line 9 5 param_vector = qnn_layer.get_random_parameter_vector(seed) 7 print(f'Initial parameters: {param_vector}') ----> 9 print(f'Initial loss: {loss(param_vector)}') [... skipping hidden 11 frame] File ~/.../pennylane/workflow/qnode.py:1164, in QNode. __call__ (self, *args, **kwargs) 1162 if qml.capture.enabled(): 1163 return qml.capture.qnode_call(self, *args, **kwargs) -> 1164 return self._impl_call(*args, **kwargs) File ~/.../pennylane/workflow/qnode.py:1144, in QNode._impl_call(self, *args, **kwargs) 1141 override_shots = kwargs["shots"] 1143 # construct the tape -> 1144 self.construct(args, kwargs) 1146 original_grad_fn = [self.gradient_fn, self.gradient_kwargs, self.device] 1147 self._update_gradient_fn(shots=override_shots, tape=self._tape) File ~/.../pennylane/logging/decorators.py:61, in log_string_debug_func..wrapper_entry(*args, **kwargs) 54 s_caller = "::L".join( 55 [str(i) for i in inspect.getouterframes(inspect.currentframe(), 2)[1][1:3]] 56 ) 57 lgr.debug( 58 f"Calling {f_string} from {s_caller}", 59 **_debug_log_kwargs, 60 ) ---> 61 return func(*args, **kwargs) File ~/.../pennylane/workflow/qnode.py:966, in QNode.construct(self, args, kwargs) 964 with pldb_device_manager(self.device): 965 with qml.queuing.AnnotatedQueue() as q: --> 966 self._qfunc_output = self.func(*args, **kwargs) 968 self._tape = QuantumScript.from_queue(q, shots) 970 params = self.tape.get_parameters(trainable_only=False) Cell In[14], line 13 9 param_list = qnn_layer.vector_to_hamiltonian_parameters(param_vector) 11 qml.evolve(qnn_layer.H)(param_list, ts) --> 13 return (-1) * qnn_layer.n_qubits * (qml.var(S_z) / qml.expval(S_x)) TypeError: unsupported operand type(s) for /: 'VarianceMP' and 'ExpectationMP' ``` How do I make this sort of quantity of interest feasible in my loss function? Thanks! My `qml.about()` is as follows: ```auto Version: 0.37.0 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: /.../python3.11/site-packages Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, packaging, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions Required-by: PennyLane_Lightning Platform info: macOS-14.5-x86_64-i386-64bit Python version: 3.11.7 Numpy version: 1.26.4 Scipy version: 1.14.0 Installed devices: - lightning.qubit (PennyLane_Lightning-0.37.0) - default.clifford (PennyLane-0.37.0) - default.gaussian (PennyLane-0.37.0) - default.mixed (PennyLane-0.37.0) - default.qubit (PennyLane-0.37.0) - default.qubit.autograd (PennyLane-0.37.0) - default.qubit.jax (PennyLane-0.37.0) - default.qubit.legacy (PennyLane-0.37.0) - default.qubit.tf (PennyLane-0.37.0) - default.qubit.torch (PennyLane-0.37.0) - default.qutrit (PennyLane-0.37.0) - default.qutrit.mixed (PennyLane-0.37.0) - default.tensor (PennyLane-0.37.0) - null.qubit (PennyLane-0.37.0) ``` ---
**Author:** ![Guillermo\_Alonso](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/guillermo_alonso/32/816_2.png) [@Guillermo\_Alonso](https://discuss.pennylane.ai/u/Guillermo_Alonso)\ **Post date:** [August 14, 2024, 7:53am UTC](https://discuss.pennylane.ai/t/how-do-i-operate-between-to-different-measurement-processes-for-my-loss-function/5041/2 "2024-08-14T07:53:54Z")
Hi @NickGut0711 ! 😄 That would be a great PL feature but for now this would be an alternative: ```python def get_loss_function(): @qml.qnode(qml.device("default.qubit")) def circuit(param): qml.RX(param, wires = 0) return qml.var(qml.X(0)), qml.expval(qml.Z(0)) def loss(param): var, exp = circuit(param) return var / exp return loss ``` I hope that helps! ---
**Author:** ![NickGut0711](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/nickgut0711/32/2137_2.png) [@NickGut0711](https://discuss.pennylane.ai/u/NickGut0711)\ **Post date:** [August 14, 2024, 2:34pm UTC](https://discuss.pennylane.ai/t/how-do-i-operate-between-to-different-measurement-processes-for-my-loss-function/5041/3 "2024-08-14T14:34:43Z")
Hi @Guillermo_Alonso! Thank you so much for the prompt, and helpful, reply! I’ll try that now 🙂