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In this plot:

enter image description here

taken from here, IonQ is claiming to have a potential application in machine learning by 2023. What applications could they have in mind?

From what I understand, modern error correction prevents obtaining speedup from any quadratic algorithms, so the only realistic speedup could be done with algorithms faster than polynomial. From this chart: enter image description here

It seems like there aren't any ML applications that are faster than quadratic without "the fine-print" that they might not be do-able in a real-life situation.

Is there something I'm missing here?

glS
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Steven Sagona
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1 Answers1

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"IonQ is claiming to have a potential application in machine learning by 2023. What applications could they have in mind?"

None.

  • The plot you showed has no units on the y-axis. It doesn't even have numbers.
  • The choice of 2023, 2025, and 2027 for "inflection points" (which they didn't define, and based on their graph has nothing to do with the standard calculus definition of the term) was arbitrary.
  • The choice of topics listed (Machine Learning, Materials, Chemistry) is arbitrary, and the order is even more arbitrary. In fact it would make more sense to switch chemistry and materials, since materials involve far more atoms than molecules, and are often significantly harder to model using the techniques for which quantum computers provide an advantage (QCs are very unlikely to speed up DFT, but could possibly speed up FCI, but FCI for materials is objectively orders of magnitude harder than molecules).
  • This last point is funny: they predicted "faster optimization" in 2025, but "better optimization" will have to wait until 2027 (just hilarious!).

If you are just wondering what applications of machine learning are possible in the future, please see this question: Is there any potential application of quantum computers in machine learning or AI?.

If you are wondering what applications IonQ has in mind for the year 2023, the answer is most certainly zero. I wish I could also underline "most certainly zero" to put more emphasis on it. Let me say it again: quantum computing will not be outperforming classical computers for any machine learning applications in 2023.

You may also find useful a similar answer about applications of quantum computes in drug design.

user1271772 No more free time
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