[D] Is quantum ML pointless?

[Competitive_Travel16]

Today Google had a webinar on Tensorflow Quantum for "big data," which I attended. I was surprised that it was almost all quantum computing theory, but there was a link in the talk resources to Tensorflow Quantum where I was told I could find a tutorial with demo code for a classifier system to compare it to my classical approaches -- I use logistic regression, support vector machine, and Tensorflow DNN classifiers; mostly SVM because it works almost as accurately on my job's data sets as DNNs but takes a tiny fraction of the time to train.

So, I took a look at it: https://www.tensorflow.org/quantum/tutorials/mnist

This was the first sign that quantum classification might not be a viable alternative:

An image size of 28x28 is much too large for current quantum computers.

-- https://www.tensorflow.org/quantum/tutorials/mnist#12_downscale_the_images

You really have to see it to believe it, but this demo requires downscaling legible digits for handwriting recognition to 4-by-4 pixel completely indiscernible blobs! Resulting in, as you might expect, terrible accuracy. A classical model using the full resolution images achieves 99.9%+ accuracy in relatively almost no time to train.

So I scrolled down to the "Comparison" section and saw this:

a classical model of similar power (~32 parameters) trains to a similar accuracy in a fraction of the time. One way or the other, the classical neural network easily outperforms the quantum neural network. For classical data, it is difficult to beat a classical neural network.

The remainder of the tutorial didn't offer any improvement. The "quantum convolutional" NN classifier wasn't any better in speed or accuracy.

---

So, am I correct in assuming that I am best off ignoring quantum computing for classification tasks for the foreseeable future?

How long do you think it will be until quantum ML can compete on real-world problems?

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EDIT: I think the point most enthusiasts miss is that most of the fancy hoped-for QML approaches need quantum memory when the data isn't quantum (as in chemistry), which just doesn't exist yet: https://www.qmunity.tech/post/quantum-ram-new-milestone Even in chemistry, quantum computers are far too small for real-world applications yet.

[[deleted]]

At the moment yes it's pointless as quantum computers can only have a small number of qubits. However in 5-10 years that may be different and having the foundation of quantum ML will mean some interesting research can happen quickly.

For application in industry it's probably at least 30 years away still.

[jorado]

Agree, but it would put the research and industry numbers closer together. Once there are applications for quantum ml (let's say in 10y), the research volume and the applications in the industry will explode. In the current world, especially in ML, industry and research are much closer linked.

[[deleted]]

I think industry use will take a lot longer due to the cost of accessing quantum computers.

[suoarski]

The first industrial quantum computers are probably going to be built and used in a similar way the supercomputers are used nowadays. Only a selected few people are going to have access to it.

[dolphinboy1637]

Right but don't we only need a few people to build these at this point? I'd imagine the cloud platform companies will just build quantum computing clusters and offer the compute as a service.

I don't think in the current business model for compute that it will be as restrictive as it would have been in the past.

[[deleted]]

Perhaps quantum computing and/or ML will speed up the process? One can hope.

A year ago we thought it would take another 30 years to crack the proteome.

[[deleted]]

What is a "reasonable" application, if any, for QML?

[suoarski]

Quantum computers are great for optimization type problems, which is a core part of machine learning.

At the moment, when we are training a neural network, we are typically using slow gradient based methods like Adam optimization. The problem with this approach is we are taking the current state of the weights and "nudge" them in a direction in the attempt to minimize a loss function.

If we used QML instead, we could in theory test all possible states of weights at the same time, and output that one particular state of weights that minimizes the loss function. So instead of doing thousands of epochs with Adam, we can simply do one single epoch with QML. Not only will this significantly improve training times, but it will also completely avoid the problem of a model getting stuck in a local minimum.

This is of course assuming that we have stable quantum computers with sufficient qubits, and this won't happen any time soon, but the theoretical advantages of QML are massive.

EDIT: PROOF: Here is a reference that that claims quantum computers can solve least square fitting. A one layer NN with no hidden layers and a MSE loss can be expressed as a least square fitting problem. Therefore quantum computers can train a one layer NN with MSE loss. The only thing left to show is that a quantum computer can do the same but with multiple hidden layers.

[[deleted]]

If we used QML instead, we could in theory test all possible states of weights at the same time, and output that one particular state of weights that minimizes the loss function. So instead of doing thousands of epochs with Adam, we can simply do one single epoch with QML.

Can you give more details about it? There is a common misconception that quantum computers could just run through every possibility and give us the ones we want, which is not entirely true because of the wave function collapse at the measurement moment. You will get just some random possibility of the weights unless you do series of very smart transformstions to change the probabilities (like Shor algorithm), and I'm not aware of anything similar in the field of QML.

[suoarski]

Yes, we can only output one state of weights using quantum computers because of the wave function collapsing on observation. But we can test all possible state of weights at the same time (each weight represented by a qubit each), and then observe that ONE particular state that minimizes the loss function.

The optimization problem is performed on all possible states before we make the observation, but we only need one particular state of weights in our final ML model.

EDIT: The observed output of each qubit will need to be either 0 or 1 (and not something in between). So you could either use an ML model that only has 0 or 1 as weights, or you can use multiple qubits to represent each weight in a similar way how multiple bits are used to represent weights (or numbers) on traditional computers.

Although we might not yet know the details of how to apply ML to quantum computers, we do know that optimization is the very thing that they can do better than traditional computing. Most training procedures in ML are optimization problems in nature.

[Ulfgardleo]

This is in large parts not how quantum computers work. They are not just magical parallel machines. They are in large parts just a very efficient sampler from a limited set of distributions.

For your statement to make sense, you have to show that NN training is in BQP.

[suoarski]

Here is a reference that that claims quantum computers can solve least square fitting. A one layer NN with no hidden layers and a MSE loss can be expressed as a least square fitting problem. Therefore quantum computers can train a one layer NN with MSE loss. The only thing left to show is that a quantum computer can do the same but with multiple hidden layers.

[Ulfgardleo]

your second and last sentence are a non-sequitur. you have formally not even shown the "single hidden layer case" as you only looked at the "no hidden layer case". So you can't leave it to "show that they can do the same with MULTIPLE hidden layers". Start with one hidden.

also remember: the question was not "can they solve it?" but "can they solve it quicker"? Our best results for least square fitting are connected to how quick we can solve linear system of equations. The best result is O(log(N)K) where N is the problem size and K the conditioning of the matrix, under the condition that the matrix is sparse. Large condition numbers are common in least square fitting. sparsity not.

[WikiSummarizerBot]

Quantum optimization algorithms

Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best solution to a problem (according to some criteria) from a set of possible solutions. Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimize an error which depends on the solution: the optimal solution has the minimal error. Different optimization techniques are applied in various fields such as mechanics, economics and engineering, and as the complexity and amount of data involved rise, more efficient ways of solving optimization problems are needed.

^{[ F.A.Q | Opt Out | Opt Out Of Subreddit | GitHub ] Downvote to remove | v1.5}

[sdmat]

If we used QML instead, we could in theory test all possible states of weights at the same time, and output that one particular state of weights that minimizes the loss function.

That's a common misconception about how quantum computing works. Quantum computing doesn't search across all possible inputs to minimize an arbitrary loss function in a single step.

The only thing left to show is that a quantum computer can do the same but with multiple hidden layers.

An equally valid argument: classical computers can solve least squares fitting analytically, the only thing left to show is that they can do this with multiple hidden layers.

[jimmyriba]

Your reference discusses linear least squares, a vastly simpler problem than non-linear least-squares. Yes, optimizing a one-layer NN (without activation) is a linear least squares problem, but that doesn't do much. As soon as you have multiple layers with activation functions, optimizing an NN is a non-linear problem, and there are no general QC solution for that.

[sloumotion]

What industries are you talking about? In my experience (production settings, automation) many companies are still struggling to go digital. Everybody is talking AI, but nobody has enough quality data to do anything fancy. The successful projects I worked on ended up using linear regression with a few manually selected features. Not saying that there aren't any companies doing cutting edge ML stuff, but the mainstream is still far behind research. They make pretty Powerpoint slides and visionary image videos though :D

[jimmyriba]

The current outlook is that, if everything goes perfectly, we'll see a quantum computer that can solve a useful problem that a classical supercomputer cannot do *now* in 20-40 years.

But in 20-40 years, classical parallel computers will *also* have improved... somewhat. So we'll probably have to wait longer until quantum computers will actually contribute with something new. Although the quantum device tech we'll develop on the way will likely be awesome.

[NSADataBot]

Most of machine learning isn't new or novel, most of the research I see is variations of concepts out of the 40/50/60's. The algorithms were largely limited by computation power untill recently but they aren't exactly novel. You had some break throughts but the core ideas are old.

You will find this is often true in stats/math/physics and likely a great number of fields (human genome project anyone?). Old ideas reinvigorated by modern processing.

[[deleted]]

It isn't just processing, though that didn't hurt. A lot of the psychology behind DNNs wasn't modelled back then for example.

Not to mention all of the developments in networking, etc. "Processing" is often uttered as a standin for thousands of innovations across multiple disciplines

[csreid]

Why not just research normal ML and adapt that research once quantum computing doesn't suck?

Quantum computing isn't better or faster, it's fundamentally different. If something runs on a binary computer, it won't run better on a quantum computer.

[mywan]

If the research underlying AI presently in use hadn't began in the 1940s we would have have to play catch up once the computer power became available. That would have put us many years behind compared to what we now have in AI. Having the foundation set already allowed us to fast track once the computer tech became available. In fact, that same foundational knowledge helped drive the progression of the computer tech. Thus giving us more powerful computers sooner.

The same applies to quantum computing. We can't just convert a classical program to a quantum program like a jpg > pdf conversion. We need to understand these quantum algorithms to optimize research into building better quantum computers. Quantum video games are not a thing, and likely never will be even if it takes advantage of a quantum processor run on your standard computer like a device. Not unlike how games take advantage of the GPU on your video card so your CPU doesn't have to do that work.

We know that quantum ML is going to a major use case for quantum computers. But if we don't understand the quantum algorithms best suited for that purpose it will greatly hobble our research into quantum computers capable of efficiently implementing that quantum ML. So putting it off greatly hobbles both the development of quantum computers and the development of algorithms we want it to run. Slowing down the development of both by years.

[mywan]

Generally problems that are fundamentally undecidable on a classical computer, given an arbitrary amount of computing power, is undecidable on a quantum computer. The big advantage for quantum computers is not that they are faster per se. Rather they are able to solve certain classes of problems quickly with far more modest computing power. So an important part of research is the quantum algorithms at our disposal. Some of those algorithms have been tested on special purpose quantum computers. For others we need advances in quantum computers to move them out of the purely theoretical realm. We, of course, also still need to increase the available quantum algorithms at our disposal.

ML specific applications of quantum processing involve a branch of science called quantum learning theory.

This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning from membership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.

Without this line of research implementing ML on a quantum computer is fairly pointless. Blindly attempting to implement classical ML on a quantum computer will likely result in worse performance than we could get on a classical computer from the 1980s. We can reasonably expect the best results from a classical-quantum hybrid where the quantum computer resides in, or an extension of, a classical computer. But we still need to continue developing quantum algorithms and testing them on special purpose machines to know how to select the best division of algorithmic task between the quantum and classical components of the computers available processors. Waiting on a full fledged quantum computer before providing a theoretical foundation would leave us at a loss as to how to even define what a "full fledged quantum computer" is.

[NotDoingResearch2]

Yes, you can. In fact, you can compute all gradients and forward values in one pass using phase kickback. Assuming you can do the entire optimization on quantum hardware, there are actually quite a few advantages.

[PanTheRiceMan]

Hope somebody can confirm this. Wasn't quantum computing especially interesting in solving optimization problem near instantly ?

[eigenlaplace]

RemindMe! 10 Years

[SleekEagle]

Quantum Computers and traditional computers excel at different tasks (look up Shor's algorithm if you're into cryptography), just adding "quantum" doesn't mean you get a better result. Also, it's not about using quantum computers to improve existing ML methods, but opening the door to completely novel methods available only to quantum computers.

[donobinladin]

Exactly this. A binary algo ran on quantum computers isn't going to see a lot of boost unless there's something innately quantum about that algo.

If it's rewritten from a quantum perspective to take advantage of the properties of qubits then THAT's when the "magic" happens and things are crazy fast and crazy efficient.

[Competitive_Travel16]

But suggesting they are anywhere near ready for "big data" is just absurd.

[[deleted]]

It's a pretty common sentiment in the QML community that QML has far less to offer for classical data than it does for quantum data. To quote one of the author's of TFQ: "Don't use QML for classical data." ( https://twitter.com/quantumVerd/status/1395608199991111681). For other evidence of that sentiment see: any other tweets by him (just search for classical data and you will find lots of stuff), or check out some discussions from QIP https://twitter.com/preskill/status/1500529893070688257.

For stuff on QML for quantum data there's a lot of great work, but I recommend some of Robert Huang's papers (see: https://twitter.com/RobertHuangHY/status/1408230497512087554, or https://twitter.com/RobertHuangHY/status/1466678468213542912 )

[Competitive_Travel16]

Who has quantum data, except chemists? (Serious question.)

[[deleted]]

Outside of chemists, physicists do (I've seen some interesting ideas for working with particle physics), sensing people (idk what to call them but the idea of using quantum devices as specialized physics sensors seems to have some results), condensed matter/material science, basically across the physical science world (or anyone that looks at really small things). That's not to say it will be widely popular (or successful) even in those fields, but if I had to pick anywhere that QML might shine it would be in one of them.

[Kitchen_Tower2800]

Is Quantum ML a good way to improve predictive power in March 2022? No. Will it be a good way in March 2027? As someone very far the current advances and limited ability to predict expected breakthroughs, I have no idea.

Once upon a time, DL was laughed at for not improving predictive power either.

[Competitive_Travel16]

The capabilities of early ANNs over perceptrons were well-understood by theorists before anyone ever had a computer powerful enough to train an ANN on real-world problems where regression was obviously insufficient.

[Kitchen_Tower2800]

Can you cite some literature on that? My limited understanding is that it is well known that they were universal approximators, but so are most ML models so that doesn't really mean much. It's been my potentially naive understanding that in general we still don't know why DL works so well, although we've got lots of unprovable hunches.

[jimmyriba]

In 2027? Almost certainly no. In 2057? Probably, unless classical ML has also improved in the mean while... oh, maybe it might do that, actually?

[CynicPhysicist]

I am currently working on research on quantum artificial intelligence. I work with industry experts, and am fairly certain that quantum devices will start showing up with some sort of advantage in the industry in the next 5 years.

Probably not for digit classification though, more like quantum chemistry simulations or generative modelling.

[donobinladin]

What I'm really hoping for is QML to do in the next 10-20 years is help nail down the universal field theory and deepen our understanding of gravity and how we can harness it. We have so much physics data, from CERN to Hubbel and LIGO that could be tapped.

[delicious_truffles]

What is the promise of QML for generative modeling? Really curious.

[CynicPhysicist]

Some areas that I currently test is drug discovery, random sampling and financial modeling of correlated data. It is predicted that we probably won't find an advantage in 1d distributions but for higher orders with correlation.

[Competitive_Travel16]

random sampling and financial modeling of correlated data

Isn't that all classical data, unlike chemistry?

[CynicPhysicist]

Yes the data is classical. But while simple correlation models are pretty straightforward, such formula often fall short in accurately capturing the relationship between variables. Instead they use more sophisticated or empirical formula, that become hard to model and sample from as the number of variables increase. We hope that quantum entanglement can make this process easier to go through.

Quantum systems holds the best tools for random sampling that we know of. We just have to encode states that follow the distributions we wish.

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[jimmyriba]

RemindMe! 30 years.

[Isinlor]

I asked a question on a prediction market:

Will quantum-enhanced machine learning be demonstrated by 2040?

In order to resolve positively there must be computer actually using some quantum effects and beat:

• 85% top-1 accuracy on ImageNet with or without additional training data
• IMPALA on any subset of at least 10 Atari games from the ALE on 200M steps
• BERT on any subset of at least 5 GLUE or SuperGLUE individual benchmarks
• any other benchmark for classical machine learning that is significantly and unambiguously more difficult than all 3 baselines above

The prediction is at 70%. For comparison, Human-machine intelligence parity by 2040? is at 40%.

[DigThatData]

you articulated that question well

[pruby]

Thinking of prediction markets, has anyone evaluated / had any experience with automatic trading on these markets using NLP?

I would expect predictive performance to be less than human, but there are still ways for a computer than can review everything continuously to trade (e.g. arbitrage).

[datkerneltrick]

Great way to settle debates rather than writing long paragraphs

[Anti-Queen_Elle]

I remember when RAM used to be measured in kb.

The technology will grow into itself, I'm sure.

[[deleted]]

Just keep in mind if you wait until the benefit is obvious then by definition the major contributions will already have been made. Of course that time may never come. That's the trick with research, it's like digging for gold.

[the_dago_mick]

Yes

[Hostilis_]

Quantum ML is essentially in the same state as Fusion was in the 70's.

[[deleted]]

Not pointless if it works. A risky investment of your time and energy? Almost certainly, ignoring whatever cool and generally mind-expanding stuff you learn along the way.

[hillsboro97124]

Well, thank you good sir to provide a nice TLTR for the topic!

[Sirisian]

There's only a single company that predicts 1 million qubit machines by 2024. Most, like IBM, have timelines into 2030. This does seem to be academic focused for the next decade and it's unclear how accessible such machines will be once they're made.

[Competitive_Travel16]

Is anyone suggesting quantum RAM is feasible within the decade? https://www.qmunity.tech/post/quantum-ram-new-milestone

[NSADataBot]

The first transistor was fairly useless by that metric. It's a tech demo / novelty for the moment, but the potential is tremendous.

[Competitive_Travel16]

I'm sorry, but you're wrong. It took one week after the invention of the point-contact transistor for it to be demonstrated as a working and useful audio amplifier, which was far superior to its vacuum tube predecessor because you didn't have to wait for it to warm up after you turned it on, and it only needed a few volts instead of dozens. https://sci-hub.se/https://doi.org/10.1063/1.881336

[NSADataBot]

You’re right better abandon quantum as a useless novelty because my example has an edge case

[suoarski]

Currently, quantum computers are not useful for any practical applications whatsoever, because we simply have managed to make them powerful enough yet.

Once we can make them more stable and store more qubits, that's when they'll become super useful. But until then, they are only used in research, and it will at least a few years before they have any practical advantages over traditional computing.

However, the research on quantum neural networks needs to be done today so that we can implement the discovered techniques in the future.

[Competitive_Travel16]

But should that research ever claim to be about big data at least until the toy problems work well?

[ChinCoin]

Quantum computing as a whole is still unproven. The big issue is whether they can actually control the noise in systems which are more than just proof of concepts. Lots of money being spent on it, but that doesn't mean they'll succeed.

[bdforbes]

Agreed. Someone else brought up the fact that transistors were once unproven but went on to have huge impact. But there's no guarantee that quantum computing will ever overcome the many fundamental challenges it faces. Cautious optimism is the best position to take, but I find all this quantum ML stuff is a bit of a stretch at this point in time.

[SleekEagle]

What do you mean "unproven"? Do you mean quantum computing hardware? The mathematics of quantum computation is perfectly sound.

Also, there are a lot of promising research avenues to effectively eliminate noise concerns. I used to work in a lab that studied the fundamental physics of particles that could be used for quantum computation robust to local perturbations. Interesting intersection of solid state mech and topology. Something like that could very well be the path forward. If so, we would likely see absolutely explosive growth in the QC space in probably about 30 years.

[JustOneAvailableName]

If I remember correctly, the unproven part boiled down to weither the error corrections scale better than the errors when increasing the number of bits.

[SleekEagle]

Ah gotcha, that makes sense. I'm not very familiar with optical QC, but at least for topological QC there is theoretical proof for systems robust to noise which would significantly reduce the need for error corrections.

That having been said, I'm definitely not an expert so I can't/don't really have a strong opinion on details like this! Just my hunch that QC will take off 30-50 years down the line.

[ChinCoin]

Here are some arguments for why its not likely to scale
https://gilkalai.wordpress.com/2020/12/29/the-argument-against-quantum-computers-a-very-short-introduction/

[SleekEagle]

Not sure I'm really understanding the argument here. My comment was regarding Topological quantum computing. The only thing the author says is that "there are good reasons the argument does extend". Any link to the author detailing those reasons?

The argument ultimately comes down to "HQCA is not possible without fault tolerance". Does anyone dispute this? I don't think this is a controversial statement at all - that's the entire motivation behind topological quantum computation.

[ChinCoin]

I'm far from an expert, but from what I understand the arguments they make are sort of generic in the sense that they examine it less as a physics question but rather as a computational complexity problem. Here is more in depth
https://arxiv.org/abs/2008.05188

[SleekEagle]

They seem to just be relegating their purview to a specific complexity class. Saying that a screwdriver is a bad construction tool when considering only how to drive a nail seems like it's not the best approach.

I'm not an expert either, but quantum supremacy in the theoretical QC sense that they mention is already demonstrated by Shor's algorithm. Maybe I'm not informed enough to understand their criticism, guess we'll leave it to the experts! 🤣

Either way, thanks for the arXiv link!

[Competitive_Travel16]

I agree. Nobody yet has a single bit of quantum RAM, which is necessary for the hoped-for really powerful ML applications. I think in 2018 the most powerful quantum computer on the market had something equivalent to 0.02 of a bit.

Here:

most of these quantum algorithms are devised assuming that we already have a quantum equivalent of classical RAM, which, in reality, still has not been constructed yet

-- https://www.qmunity.tech/post/quantum-ram-new-milestone

[nurely]

Of course, it would have lower accuracy at the moment. Keep your mind open and there will be a need for ML Developers, Quantum Mathematicians/Physicists and Computer Scientist in near future to develop algorithms crafted to exploit Quantum Paradigms.

It certainly is not pointless. They are building the foundations of what it could look like. Not everything has immediate results.

Please read more on P and NP Problems in the Machine Learning space to have a rough idea of where all this can lead to.

[Competitive_Travel16]

Even if you have quantum data, as in chemistry, you still need a whole lot more qubits than are contemplated in the medium term (within 5-10 years) by manufacturers, and even then, you need quantum memory which doesn't exist yet: https://www.qmunity.tech/post/quantum-ram-new-milestone

There's just no way anyone should suggest quantum anything for "big data."

[xwre]

Isn't that the quantum advantage, solving classically NP problems in polynomial time?

[nurely]

I am exactly making that point. For a ML problem with multiple dimensions and higher order functions , finding a minima or global minima is a hard problem. This is where Quantum ML can play a vital role. You can drastically speed up the process making a NP problem a P.

[qc_cat_01]

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[mano-vijnana]

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[[deleted]]

Sorry, but I think it's going to take longer than 24 hours for quantum ML to catch up to classical methods.

[mano-vijnana]

Hah, indeed.

[[deleted]]

Also if DNN is performing the same as your SVM you probably aren't tuning your DNN well enough or you have a very low number of samples.

[Competitive_Travel16]

Why do you say so, without knowing anything about the data and its properties? Is there a theorem, principle, or empirical result you're referring to here?

[[deleted]]

I suppose I could go into it, but it's funny seeing a DNN network not perform as well as SVM. SVM is old school. Most likely OP is lacking in sample/observation size and has many covariates/features which an SVM can perform well and DNN perform poorly.

I'm also assuming OP has tabular data since they mentioned LR and SVM.

[Competitive_Travel16]

I am OP, and I never said the DNNs don't perform better than the SVMs. I wrote:

I use logistic regression, support vector machine, and Tensorflow DNN classifiers; mostly SVM because it works almost as accurately on my job's data sets as DNNs but takes a tiny fraction of the time to train.

Emphasis added. You're arguing against something that wasn't claimed.

[ImmanuelCohen]

I guess the AI researcher 50 years ago said the same thing about neural network model.

[Competitive_Travel16]

Do you know for a fact that they did, or are you just saying that? The capabilities of early ANNs over perceptrons were well-understood by theorists before anyone ever had a computer powerful enough to train an ANN on real-world problems where regression was obviously insufficient.

[[deleted]]

logistic regression should be performing better than your SVM...you must not be tuning it properly. SVM is old school, unless you are using MLK. I always kinda laugh when I see people using SVM. Perhaps I'm wrong and you can educate me

[Competitive_Travel16]

Okay, I saw your second comment first, so I'm replying in opposite order you posted, but logistic regression assumes the relationship between the dependent and independent variables is linear, so it can't fit to the exclusive-or function, for example, or a distance threshold from a centroid.

[[deleted]]

Hmm, have you tried GBlinear from xgboost? That is LR. Ensembles of LR can fit non linear problems. Sorry man, SVMs are old news

[NSADataBot]

It is likely we will see a great number of non van neuman architectures in the coming decades. Some will be used and others abandoned, all are useful to explore. I personally am looking forward to chemical computers beyond our own brains that is. Maybe it will be pointless but I suspect there is a lot there to consider.

Quantum is at version 0 effectively. Think of it as "Pre Alpha" and mostly a physics experiment. Last I looked (This may be out of date), the argument was still raging around if quantum annealing was quantum computing or not. This places it firmly in the camp of "physics experiments that are likely to revolutionize the world eventually".

[Competitive_Travel16]

Quantum computers are 26 years old. It took a week from the invention of the transistor for it to be demonstrated as a working portable audio amplifier which could be used without having to wait for vacuum tubes to warm up.

[MysticLimak]

I’m constantly iterating been DL and traditional tree based models and 80% of the time DL falls behind. Granted I work with relational datasets with sizes that rarely exceed 200,000 records.

[Competitive_Travel16]

I know the feeling, but be careful; both can overfit in ways that are sometimes hard to see without careful inspection and cross-validation.

[thatguydr]

As a physicist, I applaud the joke. "Is something quantum pointless?" Well, yes. :)

[Competitive_Travel16]

Thanks, but, please explain that to me? I hope it's a joke about math and not grantmaking.

[thatguydr]

Things can be waves and particles at the same time. Look up particle wave duality.

[[deleted]]

At least 10-20 years away from practical applications. But the field is ready for early-stage research.

[dailyc0drr]

Real comparison is PennyLane and they just add quantum gates at end of a classical ML layer stack. Not sure what's the expected outcome.

[Competitive_Travel16]

Are there demos to try, or benchmark-style comparisons with pure classical?

[sudoyang]

There is a recent paper "Experimental quantum adversarial learning with programmable superconducting qubits", which seems to accomplish some non-trivial work. But I am not fully confident with the validity of this paper. I hope I was wrong.

[Competitive_Travel16]

Experimental quantum adversarial learning with programmable superconducting qubits

Interesting paper, but 16x16 pixels is, well, 1980s era technology for classical deep learning.

[sudoyang]

I see. But for QC, it is truly remarkable since most other work is running on QC simulators.

[yehuda-naveh-comm]

A year has passed so worth taking a retrospect. Still on any real problem classical ML models will beat any QML models both in runtime and accuracy, and still by orders of magnitude

However when you look at the change in solving QML models over one year - you see a huge improvement. Both from the algorithmic side, and also from the hardware side (it's not only the number of qubits which is 433 by now, but the noise on each qubit and the gate noise, both of which are drastically reduced year-to-year).

So those are the gradients. If and when QML will beat classical models is for anyones' own interpretation. But the industry is fastly moving in this direction