I have been investigating uses for quantum machine learning, and have made a few working examples (variations of variational quantum classifiers using PennyLane). However, my issue now is its relationship with classical machine learning. At the moment (in my tests, at least), QML seems to not provide any major improvement in performance (compared to a classical network) and is significantly slower when running on real hardware.

I understand that this is a young field people are still exploring, but I'm curious as to why you would not just always use a classical ML algorithm for problems. Therefore, my questions are:

  • What benefits (or predicted benefits) are there using quantum machine learning?
  • Is there little benefit now, but the potential for performance increases when hardware improves?
  • It wouldn't surprise me to learn there are examples where QML outperforms classical ML. Here, my question is why is this the case? How would moving to a quantum regime improve performance?
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    $\begingroup$ Seconded gIS, also from what I've heard QML is still an extremely nascent field with not a lot of landmark results. I think HHL (which is not even QML really) is probably one of the biggest results. We don't really have the hardware yet to do larger tests + I believe one of the difficulties was loading data into the systems (which is only avoided in very specific use cases) $\endgroup$
    – C. Kang
    Commented Aug 17, 2020 at 16:07
  • $\begingroup$ This answer to another post may be answering the same question $\endgroup$ Commented Mar 3, 2021 at 21:05

2 Answers 2


QML (as you are using the term here ${}^{(\dagger)}$) is to machine learning as quantum algorithms are to classical algorithms. I feel like you could seamlessly remove all references to ML from this question, asking only about the relation between quantum algorithms and classical algorithms, and the answers would be essentially the same.

I'll therefore refer you to some related discussions on these topics:

The last one is also essentially a duplicate of this question, in my view.

${}^{(\dagger)}$ I'd prefer using the term "quantum-enhanceed machine learning" to refer to this research direction. The reason is that the term "quantum machine learning" is also used to refer to applications of classical machine learning to problems arising from quantum mechanics/quantum information science, and this is a completely different field compared to devising quantum algorithms to tackle tasks typically considered in data science, which is what quantum-enhanced machine learning is about.


Check out these resources. It shows how QML might turn out to be in the future, see IBM Q for AI.

In case of quantum-inspired algorithms, when the dataset meets certain conditions, it might be better than the classical approaches, see Quantum-inspired algorithms in practice


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