Supervised quantum machine learning models are kernel methods

Hi @Maria_Schuld!

I am reading your paper “Supervised quantum machine learning models are kernel methods”. I have a couple of questions

  1. Did you use your results for any specific example and have a code for this?
  2. Are you currently working on it or do you know someone who is working on it (your grade student orcolleague)?

Hi @Razeen, welcome to the forum!

Maria will be unavailable for several months. You can get an idea of who is working on something similar by checking out citations of this paper. You can find them on Google Scholar or Semantic Scholar for example.

As per the code, @sophchoe created this repository where she implemented an application based on the paper. You might find it useful!

Does this answer your questions?

Hi @Razeen,

In my opinion, the paper is about Maria’s astute observation of the parallel between the higher dimensional computational space in quantum computing and the higher dimensional feature map space in the kernel method.

I think the paper is really valuable in that it recognizes the power of quantum computing as information processing in higher dimensional computational spaces from {0, 1} to C x C (without taking the projective space into account).

Any quantum algorithm, in fact, is an application of her idea already.

I believe that examination of quantum computing from the computational space (solution space) perspective will unlock more tools that have not been thought of yet.


Specific implementation of Support Vector Machine using the Kernel method on quantum computers would definitely be interesting, of which I don’t know any yet.

Hi @CatalinaAlbornoz

I came to find about NIST and NSF’s effort to build a coherent vision for quantum communications based on the fiber optics communications network infrastructure.

I think Xanadu’s photonic quantum computers fit nicely into their vision.

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