# Proper way to change operations in fully constructed qnode/tape **URL:** \ **Category:** PennyLane Help\ **Created:** [January 19, 2024, 1:14pm UTC](https://discuss.pennylane.ai/t/proper-way-to-change-operations-in-fully-constructed-qnode-tape/3928 "2024-01-19T13:14:39Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![Cap\_Cap](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/cap_cap/32/2086_2.png) [@Cap\_Cap](https://discuss.pennylane.ai/u/Cap_Cap)\ **Post date:** [January 19, 2024, 1:14pm UTC](https://discuss.pennylane.ai/t/proper-way-to-change-operations-in-fully-constructed-qnode-tape/3928/1 "2024-01-19T13:14:39Z")
Hi, I’m working on a project for quantum computer synthesis with ES and look for an efficient way to specifically exchange operation on different wires and different places. I looked through the docs and have some ideas how it could work, but they feel like I miss a crucial part to make it elegant. Is there a trivial way I missed doing such a transformation of operators, or do I have to glue something together till it works ? Also, I’m thinking about changing from generator random qnodes to instantly generating random tapes to save some unneeded code. Is there a direct answer if you can do everything with tape what would be possible in qnodes or are there clear limitations why it would be a bad idea. I can solve this answers myself, but it doesn’t hurt asking if there is a clear answer to this 🙂 Here is the current generator ```auto import pennylane as qml from pennylane import numpy as np from pennylane.ops import CNOT def random_qnode(num_wires, gate_set, min_depth, max_depth): # Create a quantum device with the specified number of wires dev = qml.device("default.qubit", wires=num_wires) # Define a quantum circuit using the PennyLane QNode decorator @qml.simplify @qml.qnode(dev) def circuit(): # Randomly choose the depth of the quantum circuit depth = np.random.randint(min_depth, max_depth + 1) # Iterate over the chosen depth for _ in range(depth): # Iterate over each wire in the quantum device for wire in range(num_wires): # Randomly choose a gate from the specified gate_set gate_class = np.random.choice(gate_set) # Special case if CNOT gate is chosen if gate_class is CNOT: # Get a list of all wires and remove the control wire possible_target_wire = list(range(num_wires)) possible_target_wire.remove(wire) # Randomly choose a target wire from the remaining wires target_wire = np.random.choice(possible_target_wire) # Apply CNOT gate to the chosen wire pair CNOT([wire, target_wire]) else: # Apply the chosen gate to a single wire gate_class(wire) # Return a list of expected values of PauliZ for each wire return qml.state() # Return the constructed quantum circuit return circuit ``` Here is the function which would change operations, still work in progress: ```auto import pennylane as qml import random from pennylane import numpy as np def mutation(m_qnode:qml.qnode, mutation_cap = 1): gates_applied = m_qnode.qtape.operations m_amount_nbr = random.choice(range(len(gates_applied)-mutation_cap)) m_rows = [] m_possible_rows = list(range(len(gates_applied))) for i in range(m_amount_nbr): rdm_possible_row = random.choice(m_possible_rows) m_possible_rows.remove(rdm_possible_row) wires = len(m_qnode.qtape.op_wires()) #TODO code to exchange differnet parts of the given circuit ``` And, finally, make sure to include the versions of your packages. Specifically, show us the output of `qml.about()`. Name: PennyLane Version: 0.33.0 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.](https://github.com/PennyLaneAI/pennylane) Author: Author-email: License: Apache License 2.0 Location: c:\users\tombi\pycharmprojects\venv\lib\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: Windows-10-10.0.22621-SP0 Python version: 3.9.13 Numpy version: 1.26.1 Scipy version: 1.11.3 Installed devices: - default.gaussian (PennyLane-0.33.0) - default.mixed (PennyLane-0.33.0) - default.qubit (PennyLane-0.33.0) - default.qubit.autograd (PennyLane-0.33.0) - default.qubit.jax (PennyLane-0.33.0) - default.qubit.legacy (PennyLane-0.33.0) - default.qubit.tf (PennyLane-0.33.0) - default.qubit.torch (PennyLane-0.33.0) - default.qutrit (PennyLane-0.33.0) - null.qubit (PennyLane-0.33.0) - lightning.qubit (PennyLane-Lightning-0.33.1) Process finished with exit code 0 ---
**Author:** ![isaacdevlugt](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/isaacdevlugt/32/1159_2.png) [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)\ **Post date:** [January 19, 2024, 10:57pm UTC](https://discuss.pennylane.ai/t/proper-way-to-change-operations-in-fully-constructed-qnode-tape/3928/2 "2024-01-19T22:57:54Z")
Hey @Cap_Cap, I’m not sure I 100% understand what you’re trying to do, but maybe check out transpilation: [qml.transforms.transpile — PennyLane 0.34.0 documentation](https://docs.pennylane.ai/en/stable/code/api/pennylane.transforms.transpile.html?highlight=transpile#pennylane.transforms.transpile). You can transform circuits according to a desired coupling map between qubits. If that’s not what you’re trying to do, let me know!