# Circuit executing with qiskit.aer noise\_model very slow **URL:** \ **Category:** PennyLane Qiskit\ **Created:** [May 18, 2023, 8:37am UTC](https://discuss.pennylane.ai/t/circuit-executing-with-qiskit-aer-noise-model-very-slow/2946 "2023-05-18T08:37:42Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![PoJung-Lu](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/pojung-lu/32/1600_2.png) [@PoJung-Lu](https://discuss.pennylane.ai/u/PoJung-Lu)\ **Post date:** [May 18, 2023, 8:37am UTC](https://discuss.pennylane.ai/t/circuit-executing-with-qiskit-aer-noise-model-very-slow/2946/1 "2023-05-18T08:37:42Z")
Hello! I am trying to use quantum circuit to do some classification works (VQC). My circuit work pretty well with local simulater cureently. And I think the next step is to test if it is robust to noisy environment. I searched some pennylane’s tutorial, and changed my circuit into noisy version. Since I would use IBM quantum device to do final teting in the future, I think it is reasonable to plugin their noise during training. However I found that if I change my device from “default.qubit” into ‘qiskit.aer’, the traning time increases significantly. Here is a reproducable example code: ```auto import numpy as np import pennylane as qml from qiskit_ibm_provider import IBMProvider from qiskit_aer.noise import NoiseModel from jax.config import config import jax import jax.numpy as jnp config.update("jax_enable_x64", True) jax.config.update('jax_platform_name', 'cpu') n=100 provider = IBMProvider() backend = 'ibmq_belem' noise_model = NoiseModel.from_backend(provider.get_backend(backend)) #backend = provider.get_backend(backend) dev = qml.device('qiskit.aer', wires=2, noise_model=noise_model) #dev = qml.device("default.qubit", wires=2) @qml.qnode(dev, interface="jax") def circuit(p,x,y): qml.RY(p[0]*x, wires=[0]) qml.RX(p[1]*y, wires=[0]) qml.CNOT(wires=[0, 1]) return qml.expval(qml.PauliZ(wires=1)),qml.expval(qml.PauliY(wires=1)) params = jnp.array([0.1,0.2]) datax = jnp.array(np.random.randn(2, n)) def loss(p,x): rst = circuit(p, x[0], x[1]) cost=jnp.sum(sum(rst)-1)**2 return cost ``` And use `%timeit` to see the performance difference: ```auto %timeit cost, grad = jax.value_and_grad(loss)(params,datax) ``` One may see that the ‘default.qubit’ device is over hundred times faster than 'qiskit.aer’ device. I wonder what cause this difference, they should be having the similar scale of matrix multiplying. I have tried to use jax.jit function to speedup training. But this will cause one problem: A noisy circuit means there are some noise fluctuating when running, hence every time you run will get different outcome. However if I use jit function, either jit the circuit or the grad function, the noise seems to be ‘fixed’. No matter how many times i run, I always get the same results. Is there any way to speed up the training? Any suggestion would be appreciated, thanks! Here is the package imformation of `qml.about()`: ```auto Name: PennyLane Version: 0.30.0 Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc. Home-page: https://github.com/XanaduAI/pennylane Author: Author-email: License: Apache License 2.0 Location: /home/ubuntu2022/anaconda3/envs/research/lib/python3.10/site-packages Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml Required-by: PennyLane-Lightning, PennyLane-qiskit Platform info: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 Python version: 3.10.9 Numpy version: 1.23.5 Scipy version: 1.10.1 Installed devices: - qiskit.aer (PennyLane-qiskit-0.30.1) - qiskit.basicaer (PennyLane-qiskit-0.30.1) - qiskit.ibmq (PennyLane-qiskit-0.30.1) - qiskit.ibmq.circuit_runner (PennyLane-qiskit-0.30.1) - qiskit.ibmq.sampler (PennyLane-qiskit-0.30.1) - lightning.qubit (PennyLane-Lightning-0.30.0) - default.gaussian (PennyLane-0.30.0) - default.mixed (PennyLane-0.30.0) - default.qubit (PennyLane-0.30.0) - default.qubit.autograd (PennyLane-0.30.0) - default.qubit.jax (PennyLane-0.30.0) - default.qubit.tf (PennyLane-0.30.0) - default.qubit.torch (PennyLane-0.30.0) - default.qutrit (PennyLane-0.30.0) - null.qubit (PennyLane-0.30.0) ``` ---
**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:** [May 19, 2023, 1:40pm UTC](https://discuss.pennylane.ai/t/circuit-executing-with-qiskit-aer-noise-model-very-slow/2946/2 "2023-05-19T13:40:18Z")
Hey @PoJung-Lu! `default.qubit` will definitely be faster here because it’s using `diff_method="backprop"`, whereas `qiskit.aer` is using `diff_method="parameter-shift"`. On top of that, there will be a general slowdown with `qiskit.aer` because of the noise simulation. ---
**Author:** ![PoJung-Lu](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/pojung-lu/32/1600_2.png) [@PoJung-Lu](https://discuss.pennylane.ai/u/PoJung-Lu)\ **Post date:** [May 22, 2023, 2:21pm UTC](https://discuss.pennylane.ai/t/circuit-executing-with-qiskit-aer-noise-model-very-slow/2946/3 "2023-05-22T14:21:55Z")
Hi @isaacdevlugt! I see, maybe I can change the ‘diff\_method’ and see what would happen. Thank you very much! ---
**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:** [May 23, 2023, 2:42pm UTC](https://discuss.pennylane.ai/t/circuit-executing-with-qiskit-aer-noise-model-very-slow/2946/4 "2023-05-23T14:42:21Z")
Just be careful that not all differentiation methods are compatible with all devices! Check out our YouTube video on this: [Differentiation on quantum hardware | PennyLane Tutorial - YouTube](https://www.youtube.com/watch?v=yMXJ0irhPZY&list=PL_hJxz_HrXxsY23iiLZTxiPctXKYI6tNV&index=5&pp=gAQBiAQB)