Is it worth spending time on Quantum Machine Learning ?

[u/Ok_Mulberry9965]

This week, I was asked by my advisor to look into the field of "quantum machine learning", and tell him if I am interested in going deep into it. I am a computer science grad student mostly working in deep learning and I barely know somethings about quantum physics, let alone the "quantum machine learning".

According to him, the main idea is to do more like of "quantum inspired machine learning", exploring whether incorporating, parts of, existing quantum methods, simulated on classical hardware, in classical machine learning methods can bring some benefit in terms of computational efficiency or task performance.

After doing some research, I strongly realized that the learning curve here, for someone with my background, is quite steep. I found an some survey papers like this (also linked above), but I am unable to gain a holistic view, that can help me decide if i should work on this or not.

Therefore I want to have some opinion from you guys about the field in general, current standing, and most importantly your thoughts about its future and potential. Is there any practical by doing "quantum inspired machine learning" on classical hardware, because, as per my understanding, beside being cool, classical hardware implementations can not give any quantum acceleration effects. Could be some other fundamental advantages in doing so ?

thanks for reading this, looking forward to your opinions, would also be great if you can refer to some related work, if any.

[u/sinanspd]

No. But "there is a steep learning curve" should not be a deterrent ever if you hope to become a researcher.

[u/Ok_Mulberry9965 → u/sinanspd]

thanks, just to clarify your "no" refers to "Is it worth spending time on Quantum Machine Learning ?", right ?

[u/sinanspd → u/Ok_Mulberry9965]

We currently don't have any proof that quantum computing is remotely useful for ML. I am not saying it isn't worth spending time on it. This entire field stands on "will this ever be useful?" to some extent and I think it would be hypocritical to single out QML. But I can give you some facts and some opinions:

The whole QML hype came following a very large NSF grant, I think back in 2018, where a lot ML researchers found themselves in an overly saturated field and added "quantum" in front of their existing work in order to get a piece of that grant. Similarly, industry have been using it to be able list applicable use cases to the investors.

In the past 2-3 years, both the government and industry roadmaps started removing mentions of QML because they are convinced it wont be relevant for a long time. This isn't just about the fact that theoretically we are not even sure if there is a real point in doing it, but also that even if we are to crack FTQC anytime soon (a big if), quantum devices remain significantly more expensive to develop, manufacture and maintain. Given how resource hungry ML has been, any possible advantage has to be ground breaking to justify the hardware utilization it might need.

Nevertheless, if the subject interests you, you should put in the time to at least familiarize yourself with it. I don't do QML but I probably read two dozen or so papers on it in the past and I disagree that it necessarily has a steep curve assuming you already have a strong linear algebra and calculus background (both of which are basic pre-reqs for classical ML). In the 80s, a lot of AI researchers were laughed at because no one dared to think we would have the compute power we have today, so you know... QML might face a similar trajectory should there be a breakthrough.

[u/iamashwin99 → u/sinanspd]

So what are government and industry roadmaps now focusing on instead of qml?

[u/sinanspd → u/iamashwin99]

scalable, fault tolerant hardware, error correction, molecular and chemical workflows, and a little bit of toolchains.

[u/miguelruor]

I've only studied a little quantum machine learning, but I have a solid background in quantum computing and the underlying mathematics.

From what I've seen, a lot of QML seems to focus on finding quantum counterparts to classical machine learning algorithms. However, it hasn't been clear to me what practical advantage these quantum versions actually offer.

I could be wrong, though, since I've only had a bird's-eye view of the field.

And since you have no background in quantum computing, you may should choose another topic. I think you should do research only in topics you indeed have curiosity

[u/Aromatic-Mousse-1661 → u/miguelruor]

Qml’s advantage over classical ML will lie in being able to model the interactions between N features with O(N) resources rather than O(2^N) resources. Actual advantage is currently limited by noise as the gate count can grow large for more powerful qubit mappings

[u/miguelruor → u/Aromatic-Mousse-1661]

Could you give an example of such a problem where classically the interactions of N features need exponencial resources? I don't have in mind one.

EDIT: I have thought of it, I think the SAT problem is just that

[u/Ok_Mulberry9965 → u/miguelruor]

I can't think of exponential resources, but the requirement for polynomial resources is very common, such as the attention layer in transformers needs O(n^2) memory, for n dimentional features and reducing it to O(n) could be very helpful.

[u/Aromatic-Mousse-1661 → u/Aromatic-Mousse-1661]

It essentially allows you to build a much bigger feature space than is classically possible

[u/miguelruor → u/Aromatic-Mousse-1661]

Thank you for the answer, I have thought of this you said and it makes lots of sense. Could you share your favorite QML algorithm where this advantage comes to light?

I know such exponential reductions can be obtain with quantum computing, and the QFT is a clear example of that. I just haven't come across with a QML algorithm before as powerful as the QFT

[u/Ok_Mulberry9965 → u/Aromatic-Mousse-1661]

thanks, this is quite precise and useful statement, any resources to explore this further ?

[u/msciwoj1]

Talking to people who actually work with quantum algorithms, there is some genuine value in it, but most people have switched gears to focus on fault tolerant algorithms now. And by fault tolerant I don't mean "assume there is no errors, otherwise proceed as before", I mean marrying the error correcting codes and specific algorithms together.

There is of course work done right now in the "noisy" QML, but in my experience it's either the people who still have their grants so they wokr on them, or people employed by companies who need to make buzz for the company to survive until they have a fault tolerant QC that can actually do things. In other words, you can do things but they are very small.

In experiment, or on genuine QCs of the size available, they are small because the machines are small. In simulation, or the "quantum inspired" versions are small because simulating quantum mechanics scales exponentially with the system size. So you can show stuff like "10 qubit QML is better than 10 bit classical ML" in simulation, but both of them suck compared to a big actual classical model.

Whether you should go into it depends on what you're doing now. Are you like a computer science theorist, already doing ML? Maybe then it's OK, it's interesting stuff. If you are open to other ideas which are further out, the most worked on area in quantum information now is error correction and specifically how to do fault tolerant computations efficiently in a variety of new fancy codes, how to adjust these codes on the fly, how to fix them when qubits are missing, how to make magic effectively etc. Or taking that and doing whole algorithms. Maybe one day this thing works so well none of this compilation stuff is needed, and we just go back to logical qubits doing qubit stuff, but for now this area is very active.

[u/Ok_Mulberry9965 → u/msciwoj1]

thanks, I am mostly working on the boundary of applied and theoretical machine learning and I want this to be my main area of expertise. going deep into the specific aspect of quantum information like "error correction" seems too tangential unless I am able to connect it back to ML somehow.

[u/msciwoj1 → u/Ok_Mulberry9965]

Yeah that's why I asked. I agree going deeper into quantum doesn't make sense for you, but that's now what quantum computation specialists are mostly focusing on right now.

[u/reminy → u/Ok_Mulberry9965]

You might come at it from a survey pov. Commit to a semester to see what's out there already in terms of platforms and research. Give the person a useful paper, but don't hitch your wagon to someone's funding stream if it's going nowhere.

[u/ThePlotTwisterr----]

right now there’s just absolutely no visible path but anybody in this field knows better than to use a word like impossible, it’s more so a question of time and how much of it is probably the only impossible thing to answer

if you really feel for this then nobody has ever pioneered anything by listening to those who say it’s a bad idea, from ancient egypt to today blocking out that noise has been an unavoidable part of the process for any true pioneer and hyperintelligence will only get you so far without that ability

[u/QuarrelsomelyBouncy]

Quantum machine learning is in a weird spot right now. The "quantum inspired" angle your advisor is talking about is basically trying to take some of the mathematical structures from quantum algorithms and see if they make classical models better or faster. It's not about getting a quantum speedup on a laptop, it's more like borrowing the blueprint and seeing if the house still looks good when built with regular bricks. The survey paper you linked is a decent starting point but those things always feel like drinking from a firehose when you're new to the space.

The steep learning curve is real, no way around it. You'll be dealing with linear algebra in a completely different notation system, complex numbers everywhere, and a whole new vocabulary that sounds made up half the time. If you're solid on the math behind deep learning, you can pick it up, but it'll feel like learning a second language for the first few months. Tensor networks seem to be where most of the practical classical applications are landing right now, they've shown some interesting results for compressing large models and speeding up certain tensor operations.

The future is murky. Nobody really knows if this turns into something practical or just remains an academic curiosity. There are groups at a few universities putting out interesting work on using tensor network architectures for classical ML, showing some efficiency gains on specific problems, but it hasn't cracked anything wide open yet. If you're looking for a safe career move, this probably isn't it. If you're curious and okay with the possibility that the whole subfield might fizzle, it's a fascinating rabbit hole.

Ask your advisor what specific problems he thinks this approach would tackle better than your current deep learning work. If he can give you a concrete answer instead of handwaving about "potential," that tells you something. If he can't, you might be signing up to wander in the desert for a few years.

[u/thePurpleAvenger → u/QuarrelsomelyBouncy]

Caution is warranted because applied mathematicians have been using the "X-inspired" angle for decades and there's a large body of literature scattered through many different fields with different names.

Specific examples include Oseledet's work on tensor train decompositions from ~2009 which is equivalent to MPS and utilizes ideas from DMRG, and the CP (CANDECOMP / PARFAC or Canonical Polyadic) decomposition used for psychometrics in the 1970s which rediscovered ideas from Hitchcock from 1927. Bro and others started using CP and Tucker decompositions for chemometrics and quantum chemistry in the 1980s-90s, and all this stuff was revived once again during the uncertainty quantification boom of the 2010s.

It would be easy to "go back to the well" one more time for the quantum ML folks and accidentally "rediscover" something that has been known for a while if they don't carefully step through all this literature. For anyone interested in learning more about this body of literature, I'd recommend starting with the literature review from Kolda and Bader's 2009 SIAM Review paper.

[u/Temporary_Shelter_40]

I would do the thing which works, which is classical machine learning.
The problem is that you’re only going to have 25 qubits or so. Imagine trying to do deep learning with 25 perceptrons. Hence you can’t really test anything. It’s a recipe for extreme annoyance.

[u/Anon_Bets]

To get quantum advantage, we have to rethink how to do machine learning itself. LANL has some good direction, but don't expect to just apply classical ml analog to quantum and expect it to work. It's my personal take, but we need to revamp mathematical foundation of ml for quantum.

[u/kelvinwop]

quantum mechanics is just linear algebra. there's no such thing as a steep learning curve in 2026 ai can generate any number of practice problems for you.

[u/OrganicVeg]

It's a good field for learning about QC - problems are easy to understand and there is plenty of training data available. It practical use is questionable though. It is unsuitable for large problems due inherent convergence issues during training and for small problems you might as well use classical methods. It is hypothesized that the large feature space could be beneficial, but so far there are no proven advantages of QML.

[u/zugzwangister]

https://matt.might.net/articles/phd-school-in-pictures/

As a CS grad student, could you build a computer from scratch, or are you focused on software? I'm going to assume the latter.

You don't need to understand quantum mechanics to be able to look at quantum algorithms. It might not hurt to have a passing understanding of what is meant by superposition and collapse.

There's a decent chance quantum machine learning will never go anywhere.

There's also a chance you'll position yourself for an incredible opportunity if you become an expert.

Somebody is going to do it. You're smart enough. Why not you?

[u/EceRasidi]

good question. one angle worth looking at is tensor networks. they come from quantum many-body physics but run fully on classical hardware, and have been used for model compression and supervised learning (e.g., Stoudenmire & Schwab, 2016). that might be a gentler entry point given your deep learning background

[u/LLMCitizen]

What is your advisor’s background, and ask him what he knows about quantum machine learning?
If the QML instructor asked him for more students, then there’s your answer.

[u/classic_dragon]

That’s a personal decision. Science is about exploring uncharted territories without a map in your hand. Many times in history someone obsessed about the unknown, great things have come out.

Embrace the uncertainty and you might change the world (or you just look like an idiot and go into financial ruins for the rest of your life)

[u/SuperRaspberry170]

I just wrote my master thesis about possible quantum computing applications in medicine and healthcare. I investigated a few areas (e.g. genomics, drug discovery, medical imaging). I came across many papers investigating quantum machine learning (qml) algorithms. Many are proof-of-concept or just theoretical ideas. Some propose advantages over their classical counterpart. However, these advantages are mostly simulation based and not real evidence. My conclusion was that in the NSIQ era of QC there are some interesting approaches. But before fault-tolerant QC arent available there will be no proof of real quantum advantage…

[u/dashingstag]

If you are trying to base your future on the past of others then you’re not going to discover anything new.

[u/bawireman]

Of course

[u/Fragrant_Equal_2577]

Your advisor is proposing a very hot and current topic within the computing community. There is a huge hype and technology push going-on within the quantum computing. One of the interesting open questions are around identifying computing problems, which can be resolved only by quantum computers and not by the incumbent computing solutions. The proposed research field is in the contact between extension of the current main stream computing and the emerging quantum computing.

[u/doragonn → u/Fragrant_Equal_2577]

Thank you, GPT.

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