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If I run the code

backend = FakeLima()
nm = NoiseModel.from_backend(backend)
print(nm.__dict__.keys())

I get the following output:

dict_keys(['_basis_gates', '_noise_instructions', '_noise_qubits', '_default_quantum_errors', '_local_quantum_errors', '_nonlocal_quantum_errors', '_default_readout_error', '_local_readout_errors', '_custom_noise_passes'])

My question is: between these quantum errors, which ones are the most reasonable to manually decrease and still have a device similar to a quantum computer?

For example, maybe there are some techniques to decrease the error due to single qubit gates in an "easy" way (from the hardware point of view or with error mitigation techniques). Therefore use a fake backend with slightly lower single qubit gates error is not that utopian now or in the near future.

This question is similar to this one, but the answer of that question is about how you can change the probability errors. Instead, I would like to understand how you can do that in a realistic way.

I have this problem because all the fake devices I found for an high number of qubits (greater than 10) have an extreme amount of noise. If you know other ways (or more accurate fake backends) to solve the problem, they would be a good alternative.

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  • $\begingroup$ Just to clarify the question for me, instead of changing the Noise Model or specifying errors you want another 'realistic' way to impact the results. I mean I don't know if this is possible (probably yes in some way). But you could just take the NoiseModel of a fake device with a lower amount of qubits and use this as noisemodel for a device with higher amount of qubits. You could start with just the Simulator and maybe add a Noisemodel of smaller devices to it and apply it to a device with more qubits. Other than that I think this is already an interesting research in itself. $\endgroup$
    – Qubii
    Nov 23, 2023 at 12:51

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