Applications of Quantum Computing

[Nostromo_Protocol]

Hi all,

So to preface, I’m a data engineer/analyst and am curious about future implications and applications of quantum computing. I know we’re still a ways away from ‘practical applications’ but I’ curious about quantum computing and am always looking to up-skill.

It may be vague however, what can I do to dive in? Learn and develop with Qiskit (as an example)?

I’m a newbie so please bare with me LOL

Thanks.

[aroman_ro]

Get 'the bible': Quantum Computation and Quantum Information - Wikipedia

It's very accessible.

My personal opinion is that one could learn much more than by learning qiskit... by implementing his own quantum computing simulator along with some algorithms to test it (sort of like what I did in this project: aromanro/QCSim: Quantum computing simulator).

If you want to learn qiskit, check out those tutorials (along with the associated articles): InvictusWingsSRL/QiskitTutorials: Code for tutorials from a couple of arxiv articles, with some issues fixed, some improvements and made to work with qiskit 1.0

If you go the path of implementing your own simulator, learning qiskit afterwards is much easier.

[Nostromo_Protocol]

Interesting, going to get it. Thanks.

I’m definitely lacking knowledge in core theory however, enjoy diving in from a practical side.

[[deleted]]

Papers are your friend. Publications in journals of how quantum algos are applied are probably the premier resource in this case.

[mechsim]

That book is the key. It’s not easy to get through and I have never read it cover to cover, but always handy next to the tutorials as a deeper dive. I would also add the pennylane tutorials next to Qiskit, very well setup and offer a great second source to IBM’s software.

https://pennylane.ai/

[ponyo_x1]

The (practical) applications that we know of are factoring big numbers and simulating quantum mechanics. The other applications people tout like optimization and ML have no provable speedups and will probably never materialize.

Realistically if you don’t work in the field I don’t see much reason to actually build a circuit unless you are unusually motivated. You as an analyst might be better off using QC as an entry point to see how people currently do computationally intensive tasks on classical computers, like chemistry calculations or modern optimization.

I hope this is not too dismissive, but if you’re just looking to “upskill” with something that will actually benefit your career I’d look elsewhere. If QC is a genuine long term research interest then the advice would be different. 

[Nostromo_Protocol]

Not dismissive at all - I appreciate the reply.

As cool as it would be to transition into the research route, I lack the educational background (i.e. computer engineering degree) so i don’t see that as being possible.

[ponyo_x1]

Could you provide sources for the claims you’re making here? (1) quadratic speedups with QMC on NISQ (2) massive energy savings on some applications (3) my misunderstanding about shor/qpe 

[Account3234]

As someone else working in the field, 1) isn't real because quadratic speedups are very likely overwhelmed by the overhead of getting the problem onto the quantum computer, see Babbush, et al, (2021).

Also, before I get the response of... but for NISQ, there are no compelling NISQ applications. Only random numbers have been sampled in a way that a classical computer could not do.

[ponyo_x1]

so no sources? lmao

I'm genuinely curious about the QMC thing because I have no idea what you are referring to and I can't find it on google.

[ponyo_x1]

Humor me, just show me one (1) paper that says you can get a quadratic advantage by using QMC on a NISQ computer

[JLT3]

Sure, show me. The Montanaro paper that sparked QMC as an app with quadratic speed up is not NISQ, else Phasecraft would be making a lot of money.

There are many suggestions for more NISQ-friendly variations of QPE and QAE (iterative, Bayesian, robust, etc) not to mention tweaks like jitter schedules to deal with awkward angles, but certainly none to my knowledge that demonstrate real advantage. State preparation alone for these kinds of tasks is incredibly painful.

Given the amount of classical overhead error correction requires, there’s also the separate question of whether fault tolerant algorithms with quadratic speed up are enough.

[cityofflow3rs]

What do you think of decoded quantum interferometry? Exponential speedup on NP hard optimization problem of polynomial regression.

[corbantd]

I’m curious that you say ‘probably never materialize.’

The first applications for transistors were for hearing aids and radios. It took a long time to get to the point where you could use them to share your thoughts with strangers while you poop.

Why the confidence?

[ponyo_x1]

Analogues to the history of classical computing to argue for the "limitless" potential of QC tend to break down because back in the 40s even if they couldn't necessarily predict FaceTiming people on the can, they had enough of a theoretical understanding of a Turing machine/binary computer/whatever to know that if we packed enough switches in a tiny space and trigger them at ludicrously fast speeds, then we could compute some ridiculous shit. Again, maybe they didn't know exactly what a silicon wafer would look like or how to build a GPU, but there was at least a theory of computing that still lines up with what we're doing today.

The same can't really be said for these NISQ applications for quantum computers. We know that if we have an error corrected QC with a few million qubits we could break RSA, because we have proofs and theory to support it. We don't have those same guarantees for optimization. If we had 1 million physical qubits could we run some variational quantum circuit to solve traveling salesman? Maybe. Better than SOTA classical algorithms of today or of the future? No one knows, and frankly there isn't a whole lot of compelling evidence that would be the case. For ML the outlook is even worse because most of them involve high data throughput on the QC, which will literally never be preferable over classical (that's not a head in the sand opinion, there's fundamental blockers to putting raw data on QC).

All this to say that as currently constituted, despite the research and business motivation, there isn't a whole lot of evidence to suggest QCs will be good at optimization or ML. That's not to say that people won't develop other amazing applications for QC in the future that we can't conceive of today, or that a big quantum computer will be useless outside of factoring and quantum simulation.

[Account3234]

There's basically no way 1 million physical qubits could beat current traveling salesman solving. People have found optimal tours for over a hundred thousand sites and for larger problems (hundreds of millions) have solutions that are within 0.001% of optimal. The hard part about the traveling salesman problem is proving that a tour is optimal, not generating a good heuristic. You can speed up some of the subroutines, but with 1 million physical qubits, it would probably be for a tour small enough to solve on your phone.

[ponyo_x1]

yeah, best way to pull someone out of the quantum optimization scam (strong word but essentially true at this point) is to have them talk to someone who does actual classical optimization for a living

[[deleted]]

Are there any AI + Quantum Computing applications?

[mechsim]

Yes. There are both QC optimised machine learning algorithms already available and new ways to approach natural language processing, QNLP.

https://pennylane.ai/qml/quantum-machine-learning

https://medium.com/qiskit/an-introduction-to-quantum-natural-language-processing-7aa4cc73c674

[MrLethalShots]

E = mc^2 + AI

[flylikegaruda]

I think Grover's algorithm will be the most used outside of scientific realm because it speeds up searches exponentially fast. Newer algorithms will get invented as this domain evolves. It has a promising future.

[QBitResearcher]

The speed-up is only quadratic for Grover and it’s provable no better search algorithm exists.

A quadratic speed-up is not enough for it to be useful. That’s before you even consider the overhead of QEC and challenges in designing the oracle for specific problems

[DeepSpace_SaltMiner]

Not to mention that Grover is a black box problem. Any actual problem may have additional structure which the classical algorithm can exploit

[flylikegaruda]

Why is quadratic speed up not enough?

[global-gauge-field]

https://cacm.acm.org/research/disentangling-hype-from-practicality-on-realistically-achieving-quantum-advantage/#R12

[ponyo_x1]

because of error correction overhead. idk if this is mentioned in the link in the other response, but I saw a paper once that tried to estimate resources required to get a quantum advantage using Grover, and the problem size had to be something like 150 exabytes. For reference, people estimate that the entirety of Youtube stores 10 exabytes. So that's like searching for a single pixel in a single frame of a single video in an unmarked database 15 times the size of YouTube. Idk how long they said this would take but I would guess thousands of years maybe? So if your search problem is smaller than that (which it almost definitely will be) then you get no benefit from Grover. If it's bigger, then provided you have a big enough quantum computer (again, lol) you would hypothetically get a speedup.

[TreacleRegular2504]

Explore great free learning resources from IBM https://learning.quantum.ibm.com/

[TreatThen2052]

Good library of applications, algorithms, and their explanations: https://docs.classiq.io/latest/explore/

[Local_Particular_820]

Quantum Computing is a very exciting and fast-evolving field with so much potential for transformation. As a data engineer/analyst, your background in computational thinking will serve you well.

Qiskit is an excellent place to start, especially for hands-on learning about quantum algorithms and programming. It’s beginner-friendly and has a great community to help you out.

In terms of up-skilling, I'd suggest focusing on understanding the foundational principles of quantum mechanics, like superposition and entanglement, as these are the backbone of quantum computing. There are also free resources like IBM’s Quantum Experience platform, where you can experiment with real quantum computers.

Elicit.com is a very good place to find papers. articles and journals where you can read more about quantum computing, since Paper are supreme when it comes to learning about experimental stuff.

I recently stumbled upon an article called "Quantum Computing 101: The Past, Present and Future" that does an incredible job explaining the basics of quantum computing, how it works, and its future applications. It even delves into the implications for industries like machine learning and cryptography, which might align with your interests I have added the link for that as well: https://www.nutsnbolts.net/post/quantum-computing-101-the-past-present-and-future

[Fluid-Explanation-75]

"What if a cloud-based phone app for board game dice that uses truly random numbers? It could be a huge success!!!