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I know that quantum algorithms can be useful for machine learning ("ML") methods, and vice versa. For example if we use QAOA we can use for the optimization part different types of ML methods, like SGD, Bayesian etc.

Now we can hold on that the both topics can be useful for each of them but is it possible that we can convert any ML method to a quantum ML method ?

Is it possible to use QML in NISQ era?

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    $\begingroup$ It is not entirely clear what you are asking, could you be a bit more specific? $\endgroup$
    – forky40
    Feb 5 at 22:02
  • $\begingroup$ "* is it possible that we can convert any ML method to a quantum ML method ?*" this will totally depend on what exectly you define as "ML method" and what would you consider a valid "conversion". The naive answer is that sure, you can "convert" any classical (ML or otherwise) algorithm into a quantum one. That doesn't mean that this conversion would give you any sort of computational advantage though. $\endgroup$
    – glS
    Feb 7 at 14:44
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I will give you a partial answer cocerning using quantum machine learning methods in NISQ era. As any other quantum algorithm, QML algorithms can be used on current NISQ processors. However, there is a problem with noise leading to quick decoherence of qubits. Especially entangled qubits (which are crucial for quantum computing) are very sussceptible to noise. Moreover, there is a problem with low number of qubits currently available (see here).

This all means that you can use QML for small tasks only. Generally, vast mojority of quantum algorithms can be used for toy models (tasks) only.

You probably heard that some breakthroughts (quantum supremacy/advantage) have been achived by Google or some Chinese firms (sorry that I cannot remember the names), however, in these cases we are talking about quantum processors designed for a particular task. What I stated above about noise and decoherence is concerning universal quantum processors

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Quantum Machine Learning in this NISQ Era is making progress in the problems related classification, categorisation, clustering, and optimisation areas for specific type of datasets in chemistry, finance, travel and transportation domain.

For example, the Microsoft Q# Quantum Machine Learning Toolkit uses Pauli X,and Z gates to construct a Classifier Algorithms titled Half Moon and Full Moon. The Pauli gates are the Single Qubit Gates based on the better-known Pauli matrices (i.e., Pauli spin matrices) which are incredibly useful for calculating changes to the spin of a single electron. Please find the implementation in this GitHub repository.

Tensorflow Quantum by Google has a collection of interesting quantum machine learning models for optimising gradient based algorithms. NetKet is a another collection of interesting quantum machine learning algorithms such as Quantum State Tomography for Quantum State Reconstruction. Please find a reference implementation from Netket in this GitHub repository.

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