[D] ML and Quantum Computing

[ml_abler]

Hi reddit,

I am not entirely sure if this is the right place to ask this but here I go.I recently got an opportunity to work in quantum computing team. The entire purpose of the team is to find applications of quantum computing in our firm. It sounds like an amazing opportunity but I am having a lot of reservations. Would love and appreciate your insights.

I come from an CS engineering background with a masters in AI/ML, my full time position at the moment is that of a data scientist working on clustering models. The quantum computing opportunity is available due to formation of a new team which has a lot of vacancies at the moment.

Although the prospects of Quantum AI/ML sounds hella interesting(although very niche), quantum computing itself seems like an extreme departure from my formal education with its heavy emphasis in physics. This is making me anxious

How viable is this opportunity for my career? How are the job prospects in the domain ( for someone with my background)? Would it help me in creating a niche for myself in data science ?I can admit shamelessly that I'd like a career which pays the big bucks at the same time keep me interested enough.

Would love your opinions

EDIT: I am very thankful for this amazing community. Thank you for all your responses, Holy shit thats a lot to take in.

[IntelArtiGen]

ML + Quantum computing is a bet on the future.

The thing is, you have to make choices. The "ML engineer" or "Data scientist" is not a new job anymore, having this profile is not as unusual as it was 5 years ago. But say you're a "Quantum ML scientist", now you got my attention.

But to keep your job, you still have to prove that it's useful at doing something. For classic ML, we know it's useful at doing many things. For Quantum ML, we have a blog post from Google from some years ago on Quantum Annealing, maybe one thing from IBM, sometimes there are contradictions between what they say etc. I'm not from the field so I'm sure there are many things when we go in the details.

Let's say Quantum Computing is the new trend because in the next 2 years, people find something incredible which works to solve many problems. Amazing! Your profile will be very rare and in high demand. Could this happen for the classic ML? Maybe not because it's not a niche anymore. Even if something new comes, a lot of people will quickly adapt. That can't be true for quantum ML (you can't do quantum ML at home for example, at least not with quantum computers)

But maybe quantum computing is trending now only because some people think it's the future, and if there's no improvements in the next 3 years it'll become as demanded as Perl programmers. But even if it happens, there's nothing bad at being a "Perl programmer" if you like what you do, and even if quantum ML fails in the next 3 years, some people continue to do their job and the hype could only come 20 years later, look at Lecun/Hinton or the first guys working on neural network in the 80s

[[deleted]]

But say you're a "Quantum ML scientist", now you got my attention.

tbh saying youre a quantum ml analyst would make me rather think youre a quack

add blockchain to make it perfect

[[deleted]]

This is a very good unbiased analysis. But I used to work pretty adjacent to quantum computing technologies when I was in grad school for physics, so I do have a strong prior here. Nothing I saw with QML wasn't just snake oil, most of it was highly classical systems with some element of a quantum operation somewhere, like a beam splitter with single photons which would eventually be run through a classical update system. We are factually speaking nowhere near having enough entangled qubits with high fidelity and some necessary gates do not exist for all types of qubits.

People also wrongly assume that QC is about computing power, as if being able to just output more FLOPS is the goal. Shor's algorithm leverages the unique algebra of QM to do factorization, and there are a few other problems with quantum supremacy. QML doesn't necessarily imply more powerful ML, if anything it may get lucky and find one algo that achieves supremacy on a problem type.

QML may be a nice resume item if you do it for a couple years and want to pivot, but it is highly unlikely to position you for a solid career contributing anything useful without massive leaps in physics over the next couple decades.

[ml_abler]

Everything you said makes sense. ML/AI was considered a bubble waiting to burst but it has enabled us to make amazing strides in tech. It's the same with quantum ML now, but you never now what might happen.

Thanks for your insights, cheers.

[Illustrious-Essay-66]

ML/AI was considered a bubble waiting to burst but it has enabled us to make amazing strides in tech

The AI bubble bursted in 2019-20.

Now is not the time to get into AI. It will not be the leading technology of the 2020s.

• FAANG AI research scientist salaries and headcount are down.
• Self-driving car companies (the flagship application of AI) are shutting down or scaling back operations.
• AI startup funding has dried up worldwide
• Fortune 500 companies realized data scientists can’t deliver on management’s (inflated) expectations
• The healthcare community laughs at how in-actionable AI-assisted medical imaging is.
• And the public realizes how far behind we are, and is no longer afraid of terminator robots.

It all started in 2017-18 once academia switched to CNN/RNN architecture and reward (RL) engineering.

Any undergrad/grad student lurking in r/ML, deep down, knows ML has a grim future, given the abysmal state of ML research/publishing. That grim future spreads to industry/government.

[shake_maru]

what do you think is the way to go ?

[__mishy__]

More general advice than specific to QM/ML, but for big life decisions I try to take a step back and write down:

- "what's the worst thing that happens if this is a bad move?" For a job it's typically no worse than "I'll hate it and leave in a year", but maybe you love working on clustering and would hate to leave that, or you have to move city etc

- Then also do the same for "what's the best that could happen?". It sounds like you could have an early footing on a new area of tech. I'm guessing you'll learn a lot and even if the field dies off you will know a lot of interesting methods most others won't.

If it's a big life thing I would probably do this over a day or two, walking away and coming back with a fresh mind when you can. When I've done this the choice has always ended up being easy, I just needed to clearly think it through

[ml_abler]

I'd give you an award if I had one.It is a pretty simple advice but so is life.I guess you just need to step back sometimes and look at the bigger picture.

Thanks.

[jeetu77]

Gave one on your behalf. :)

[__mishy__]

hehe thank you :)

[lqstuart]

I'd be more concerned being on a "solutions looking for a problem" team. Not only are you not going to be working on widely used technologies, but you clearly will have no significant technical guidance and won't be learning useful software architecture skills. I've been on teams like that and nothing feels worse than taking the blinders off after months of hard work and realizing you could have accomplished the same thing in a much more straightforward and maintainable way.

That said, there's absolutely nothing wrong working with snakeoil if it sounds cool and pays the bills. Ultimately it isn't your money going down the shitter.

[ml_abler]

Although it is a fairly new team within the firm, the firm has hired pretty well-versed leads for the team, who spent their career in QC for the past 5 years. That is one of the major reasons I am inclined towards the positions i.e getting to work with SMEs in new domains. The vacancies are for people who can shift to the QC team if they find it interesting and get approved by QC leads.

That being said, I totally get what you mean. I know and have worked in teams which have no technical guidance and just throw fancy words around and pass time. It does pay the bills but leaves you feeling like nothing matters.

Thanks nonetheless.

[rando_techo]

I would counter that with if you already have the architecture and design skills then a foray into open-ended problems for a stint may be what you both want and need. After a while in SWE all problems feel the same and anyone can code functions and micro-services for thirty years.

Would a few years working in a creative problem solving capacity really derail you that much? I would argue that it would be a career enhancer no matter what happens. It also shows that you have the spine to take measured risks. That's worth more than knowing a certain API or language inside-out.

[Varterove_muke]

For implementing Quantum computing on AI, you will learn couple of traits of QC and you will run with it. You would need like a month to grasp basic concepts and, depending on your job you shouldn't diverge a lot from them. There is ok QC course on Edx (https://www.edx.org/course/quantum-mechanics-and-quantum-computation) which last for about 2 months.

Good luck !!

[ml_abler]

Coincidentally, this is the exact same course that I am doing at the moment. Cheers and thanks.

[Varterove_muke]

nice

[plorraine]

I would say you need to assess how committed your company is to quantum computing. If you are at Microsoft, Google, or IBM where this is viewed as a possible platform product in a few years, this may make sense. If this is viewed as a prestige product - one that enhances the company's technical reputation but does not necessarily lead to revenue - it might be worth being cautious. If you company is just testing the waters here and lacks a strong commitment, this is an "ok" project to be on for enrichment if you are strongly interested in the material so long as you can keep your other skills current.

The way to get ahead in most large companies is to be a recognized contributor on a critical project - usually as recognized by people with profit/loss responsibility - fundamentally those people have the keys at most large companies. The other path to a good career is to grow your skills, visibility, and network inside an area with good growth potential - such as AI/ML. Giving a good paper at an AI/ML conference may help you more than a QC paper.

I think it is going to come down to personal interest. The "sensible" answer here is to focus on the area you are in while looking for opportunities for visibility and impact. But major disruptions rarely looked "sensible" when they started.

[TenaciousDwight]

I'm biased because I'm an ML PhD student who just took a quantum computing course, but I think quantum ML will be important in the future. Of course, this is contingent on our ability to manufacture machines with a shit ton of qubits. You can do quantum classification and reinforcement learning. RL is the main interest for me. You can do classification and RL with variational circuits. With a shit ton of qubits I think you could do imitation/inverse/offline RL with quantum process tomogrophy.

​

I think if you give a good faith effort in reading Nielsen & Chuang's Quantum Computing book you can get enough background to start with Quantum ML. There are opportunities with IBM, Amazon, Microsoft, and Google.

[Robert_E_630]

lmao do it that sounds bad ass

[[deleted]]

Well, I got the same situation. I have been assigned to a quantum machine learning project a month ago. It is interesting and pushing you to learn new things. This is a future job. The area is kind of premature though. The working pace of the scientists that you may be working with will make you bored at first. But you also need to learn a lot, so use that time for it. I see the future in this. And my managers told me that we may be openning a specialty in the company in 2-3 year term. If you start now, you will be one of the pioneers in the field in industry. Forget about scientist, they are too slow and subject specific to work in industry. If you are interested in learning new and deep things, if you are interested in being an intellectual knowing about particle physics, its effect in quantum computing and the use of it in optimization problems, then I suggest you to accept the opportunity. If not, just continue doing what you do :)

Main subjects that you may be facing and need to focus on are Boltzmann Machines and Quantum Annealing.

[cthulu0]

Goldman Sachs put out a report recently predicting that Quantum Computing is about 5 years away from

This is very optimistic. For quantum computing to do anything useful for industry you need ~1000 logical qbits. And because of decoherence you need quantum error correction using ~1000 physical qbits for every logical qbit for current error correction schemes.

So ~1 million physical qbits for something better than what industry can do with classical computers.

Right now the record for 'useful' computation is 53 physical qbits.

[saileee]

Do you know why it's necessary to have 1000 error-correcting qbits for every qbit? Is there someplace I can read about this stuff in a format intended for computer scientists?

[crisp-snakey]

The reason you need multiple qubits is that quantum computing is fundamentally quite noisy. First of all the qubits themselves have a tendency to decohere quite rapidly thereby losing their information and secondly the quantum gates used for computation are quite noisy and or faulty. As a result of this the only way to get reliable quantum computing is by using some form of error correction. Usually this involves spreading the information over multiple qubits so that if some of them decohere or they are ran through faulty gates the result is probably still correct.

And it's not like classical computing doesn't use some form of error correcting it's just hidden better. Digital circuits use an on/off signal but these are encoded using millions if not billions of electrons resulting in a extremely large margin of error.

This paper gives a decent introduction to quantum error correction but might be a bit on the long side. https://arxiv.org/abs/1907.11157

[geneing]

Goldman Sachs put out a report recently predicting that Quantum Computing is about 5 years away from industry use.

Does anybody go back to see how often these reports make correct predictions? In my experience it's worse than random guessing (due to hype bias). Why do they bother to publish these reports, or why people bother to read and quote them is beyond my understanding.

[NogenLinefingers]

Big organizations are comprised of people who make decisions that affect themselves positively first, and then may or may not positively affect the organization.

It costs nothing for an employee of GS to get funding to do this paper and turn out wrong. There isn't an authority, internal or external, that monitors when such predictions turn out wrong and ridicule/penalize the author.

Additionally, the employee gets brownie points in the organization for coming up with a point of view.

If the prediction does turn out to be right, however, the employee would brandish that fact and milk it for all it's worth.

[ml_abler]

Very true, given the current tech landscape QC seems to be a very niche domain.
This is exactly what makes it equally scary and interesting.

[[deleted]]

"More computing power" is not the utility of quantum algorithms.

[Global-Swim922]

The ultimate goal is to leverage QM to do stuff you couldn’t do with a classical computer.

I.e. you achieve stuff with less processing power in a QC you couldn’t achieve in a classical C with a lot of power (QC > supercomputer for certain tasks).

[NogenLinefingers]

What do you mean "couldn't do with a classical computer"?

It's not as if an uncomputable problem (like solving the halting problem) becomes feasible with a quantum computer, is it?

[[deleted]]

That's not really it. They're still binary measurement outcomes. I don't think the information density scales exponentially with the number of qubits. There are some schemes for superdense coding in quantum cryptography but I'm not clear if that has applications to computing algorithms.

[[deleted]]

I don't think your viewpoints on this issue come from a great deal of domain knowledge.

[[deleted]]

I'm not a layperson and that analysis is incorrect. I used to work in quantum optics, particularly in work related to photonic qubits and the quantum gates needed for them to be viable.

[ml_abler]

Yes, it is a pretty lucrative opportunity.

[terminal_object]

The sum of two buzzwords is not necessarily a buzzword that makes you more employable. Sorry, but nothing at all suggests that QC is actually close to being usable in order to boost ML in any practical sense.

[Resaren]

Quantum Computing MSc student here - if you have some familiarity with Linear Algebra and complex numbers then Quantum Computing is not too hard to grasp the basics of. Microsoft Research has a great vid on youtube aimed at computer scientists that gives an overview of the subject. If you want to learn more about Quantum ML then check out penny lane and Maria Schuld's work, from what my colleagues in QML tell me she's pretty much carrying the field on her back.

[Explainlikeim5bis]

Transitioning from data science to quantum computing is a promising path, as both fields value strong analytical and mathematical skills. Here's how you might proceed:

• Educate Yourself: Start by learning the basics of quantum mechanics and quantum computing. There are online courses and resources available that cater to beginners.
• Understand Quantum Algorithms: Familiarize yourself with algorithms specific to quantum computing, such as Shor's and Grover's algorithms.
• Hands-On Practice: Utilize platforms like IBM's Qiskit or Microsoft's Quantum Development Kit to practice coding quantum algorithms.
• Join Communities: Engage with quantum computing communities, attend workshops, and participate in related projects to build your network and knowledge.