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So this is more of a soft question.

I've been trying to find some quantum machine learning algorithms can both be run with less than 8 qubits and provide a quantum advantage to classical machine learning algorithms. However, I have not been able to find any.

Could the readers of this please tell me the names of some algorithms that satisfy the two qualities that you know of, if there are any?

Thank you.

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Note that 8 qubits is not enough to show any quantum advantage. For such number of qubits, you have only $2^8=256$ basis states. Multiplying matrices and vectors of that size can be carried out in reasonable time on a classical computer.

Moreover, you are also limited by imperfections of current noisy qubits.

Overall, you can of course implement any QML algorithm currently existing but you are still constrained to toy models (proofs of principle).

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