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Comment by jimmar | Hacker News Reader

I watched some of the presentations at their recent quantum summit. They have smart people building sophisticated systems, but they struggled to define meaningful use cases for quantum computing.

I've noticed a "tell" for when product launches will likely fail. If the presenter cannot create a strong argument for the product, but instead tells the audience some version of, "we're excited to see what use cases you create for our product," then the product is likely to fail. IBM did this several times at the Quantum summit. It's basically saying, "Here's a thing. Hopefully you can find something useful to do with it, because we can't. But here are our hardware specs."

I'll pay more attention when IBM and other quantum players start to talk about applications more than numbers of qubits.

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LASERs had the same problem when they were first invented. It took serious cost reductions before they became practical outside of laboratory environments. Now they are everywhere. I think Quantum computing is currently in the same place. The systems just aren't practical yet, but if they survive and iterate a few more times it could be revolutionary.

Revolutionise what?

Sure, we can hand-wave away that it'll revolutionise *something*, but there's no direct candidate on the horizon. As of today, at best we can hope that more accurate quantum simulations could lead to some breakthrough tech, but that's a very indirect revolution at best.

With LASER, at least, there was a bunch of known use-cases that were blocked by the availability of the tech. There are no such equivalents here.

If you build it, they will come...

Most of the time, technology that is leading edge is only there for a short period, as its either bought out or lacks funding. With IBM, they have the funds to start revolutions, and have a larger vision at play. However, quantum computing is not a widely accepted concept due to a lot of its complexities, which is likely why its such a niche area. Given time and money, which IBM can handle, something will happen.

The Quantum Algorithm Zoo website[0] gives some examples of problems where the quantum algorithm is much faster than the classical one.

My own view is that for a lot of (but not all[1]) problems for which we have a quantum algorithm improvement, we maybe already have a heuristic that gets us 95% of the way there just as quickly. So the application of a QC on these problems is buying us a massive speedup, but only if you need to get the perfect, optimal answer. And sometimes you do!

[0]https://quantumalgorithmzoo.org/

[1]Factoring large primes is a good counter example.

An issue highlighted there is that there are very few algorithms where you have an exponential advantage vs classical hardware

I mean, even just going from n2 to n1.5 is huge.

It's nice if you can do it in software, but this necessarily requires hardware. Any algorithm can be sped up by a constant factor with specialized hardware: for example, addition is 2n if you need to perform an addition with an optional carry for each digit. With specialized hardware, 64-bit numbers can be added in one operation, which makes the algorithm n/32.

This kind of improvement is nice, but it would mean that quantum computers are no moe exciting than video cards.

Edit: This is a result of the linear speedup theorem. https://en.wikipedia.org/wiki/Linear_speedup_theorem

I don't know if this sort of analogy illuminates, seems rather it hides.

If we assume quantum computing is the same as SIMD, yeah, I wouldn't be excited either.

What I mean is that I would consider quantum computing a failure if it took us "from n2 to n1.5". That would make it the same as SIMD or parallel computing. I still think that linear-time factoring or sorting could be revolutionary, but only if enough interesting problems can be reduced to them. I'm holding out hope for a fast quantum SAT3 solver, at that point it will just be a waiting game until you can get an expansion card for solving NP-hard problems.

> no more exciting than video cards

Um... That seems like a pretty high bar to me, actually. Are you forgetting about how much they've done to speed up the training of quite a lot of deep-learning models? And really the only competition is TPUs and other dedicated chips that, at the end of the day, are also just chips optimized for matrix operations. The graphics in state of the art video games? Were I fan of crypto, mining rigs? That's some pretty impactful tech right there.

Yes, but... we already have that. If quantum computing only provides a linear speedup, that can already be done with a conventional ASIC. I don't think it's a high bar for quantum computing to provide something that is not already commonly available. The real boon comes from at least quadratic runtime improvements.

To be clear, I'm not saying that linear speedup is an innovation comparable to the video card, I'm saying it _is_ a glorified video card or ASIC.

I think there's a formatting miscommunication here. The person you're replying to meant going from n^2 -> n^1.5, which isn't just a linear speedup.

E.g. processing 1000 elements with an n^2 algorithm requires a million operations, with n^1.5 only ~31.5k. Processing a million elements goes from a trillion ops to a billion, a 1000x improvement.

Ha! That explains a lot, I hadn't even considered it. Yes, a change like that would be massive, and I've just been fighting a guy I made up.

That's not true. GPUs give a speedup of O(1) (which is the smallest of Amdahl's law's maximum and the GPU number of SIMD lanes). Going from n2 to n1.5 is not a linear speedup, it's a polynomial speedup.

I agree with you, but I was misled by the formatting of your comment. I thought you were saying 2×n -> 1.5×n, but now I see you mean n^2 -> n^1.5.

Sorry for taking this unfortunate shortcut!

while BQP vs P is all nice and good, afaik, the scaling just isn't there yet on the hardware. one, the noise is too high, two the bits are too low, and apparently we don't have error correction whatsoever.

i remember that even the 'factoring of 15' on a quantum computer uses some special hacks (that needs the answer) to code it in.

Aside from the much-touted uses in cryptography, probably the biggest use will be as universal quantum simulators. A lot of quantum simulations are infeasible beyond trivial interactions, because the classical memory and number of computations required to capture and progress the state of the quantum system grows exponentially with each interacting element in the system. Simplifying exponentially increasing memory/computation time requirements to linearly increasing qubit/simulation time requirements will make a lot of chemistry and material science way easier.

I thought cryptography was the big thing here. Whoever has a big enough one of these could break tls and other encryption. Which means we enter an arms race where we need quantum to build qc-proof encryption before qc gets cheap and easy enough for the bad guys.

https://www.microsoft.com/en-us/research/project/post-quantu...

Not really - you don't need QCs to do encryption that (as far as we know right now at least) is just as infeasible to break on QCs as it is on classical computers.

For example, most symmetric key algorithms are already QC-safe; in particular, AES would still be safe even in a world where you could build a QC with as many gates as a modern chip.

Of course, there is an asterisk here, as the set of all problems for which QCs give exponential/super-polynomial speedups over known classical algorithms is not yet known (not even at the level where we have some confidence that P != NP). Everything I'm claiming is based on currently known quantum algorithms, and on belief about the properties of QCs in general (i.e. that they can only solve more efficiently problems that display certain rare characteristics).

The first laser device was demonstrated in 1960. By 1961, patents on lasers already indicated applications in communication and meteorology[1]. Subsequent patents included uses for range finding, chemical analysis of gases, drilling, and other uses.

Amateur's were constructing working lasers in their basements within a year or two after the laser was invented.

I grew up reading a monthly column in Scientific American titled The Amateur Scientist which in the 1960's described a number of laser projects that could be constructed by amateurs. 1964[2], 1965[3], 1967[4], 1969[5], 1970[6]. Naturally, projects using lasers were discussed in other books and magazines during this time as well.

To me, Quantum computing seems very different than the history of lasers.

[1] https://patents.google.com/patent/US3353115A/en

[2] Scientific American, THE AMATEUR SCIENTIST, Vol. 211, No. 3 (September 1964), pp. 227-242 (16 pages)

[3] Scientific American, THE AMATEUR SCIENTIST, Vol. 213, No. 6 (December 1965), pp. 106-113 (8 pages)

[4] Scientific American, THE AMATEUR SCIENTIST, Vol. 216, No. 2 (February 1967), pp. 122-134 (13 pages)

[5] Scientific American, THE AMATEUR SCIENTIST, Vol. 220, No. 2 (February 1969), pp. 118-125 (8 pages)

[6] Scientific American, THE AMATEUR SCIENTIST, Vol. 222, No. 2 (February 1970), pp. 116-121 (6 pages)

I'm not a laser expert, but they seem reasonably similar and dissimilar on the surface. Lasers were first theorized in 1917, which is 43 years between theory and first demonstration. Quantum computing theory seems to date to 1980, also exactly 43 years ago from today (a neat coincidence!). In the meanwhile, plenty of applications of quantum computing have been proposed.

The main difference seems to be the gap between a demonstration prototype and a useful prototype, where a useful laser followed soon after a first demonstration, whereas a useful quantum computer requires many more qubits than a simple demonstration device.

I think there is also a bit of an unfair uphill battle that quantum computers have. They have to outcompete existing computers, which is a technology that perhaps has had more investment and refinement than any other human technology, so the bar to clear is high and may take longer. Or, maybe quantum computers will never reach that bar!

I think the battle to promote the use of quantum computers is rendered even more difficult by the fact that their raison d'être of "being faster than classical computers" requires one to further develop the existing field of complexity analysis for classical computers. Indeed, it's difficult to fairly compare the gate/oracle-based complexity of quantum computers to the iterations/multiplications/etc-based complexity of classical computers, especially if we don't know their complexity in the first place [1]!

[1] - Recently, there has been interest in the complexity analysis of deep learning NNs: https://arxiv.org/abs/1811.03962

Further Reading:

* Church-Turing Thesis for quantum computers - https://quantumcomputing.stackexchange.com/questions/6088/wh...

* Quantum Turing Machine - https://en.wikipedia.org/wiki/Quantum_Turing_machine

I recently had the opportunity to talk to the head of a company which is trying to build quantum computers. It's very obviously a spin off from a university to provide a way to keep their better Ph. D students employed and researching but not on University payroll (i.e. with the various requirements that entails).

Basically her opinion was they were 10 years at least away from meaningfully getting beyond 4 practical qubits with their technology, and weren't worried about it because everyone reporting more are using hundreds to thousands of qubits to error correct out to get the 60 or so they report using in calculations (their technology value add was that they could do with a lot less error correction).

The overall take away I got was quantum computers aren't "imminent" - they're 30 years away at minimum, probably more. The company exists because it's important basic research, but it's definitely not a "commercial products soon" endeavor - it was basically a way to fund focused research on a particular method.

There are plenty of examples that already run on simulators. One example would be QAOA that can be used to solve combinational problems like MQO.

Then there is also research that compares the performance of classical ML algorithms to Quantum Based variants, not only simulated but on real, IBM quantum hardware. Not only does the quantum equivalent outperform it (in pure terms of accuracy, as time to train cannot yet be compared), but also in complexity - iirc ~67k parameters for a NN and 16 for a QNN.

This is not to say that it is all set in stone - its still highly unclear which approach and design is the best for what when it comes to quantum circuits.

There is also a problem that noise suppression and error correction need even bigger quantum computers, so we‘re still a way off.

In the words of a Roche ML/Research presentation, who also already tried QC: They tried it and it didnt offer the expected results due to hardware limitations. But they are still actively pursuing it, because in terms of QC you have to be prepared, as otherwise you might be playing an endless catching up with those who were ready.

Couldn't you say the same thing about faster processors? Compute power is generally useful, but it's utility is all about where you have the most computation for the least amount of money, and until you hit that point, it's not very useful. Once they hit that point, it's insanely useful for a plethora of circumstances.

My favorite are the press releases from companies that say they need a quantum computer to do logistics and routing. I'm still waiting to see one of the solutions the QC came up with that couldn't have been produced by a modern constraint solver or heuristic router for 1/100th the cost and time.

I would imagine that any logistical problem looking to use so-called "ant routing" might benefit substantially from QC. Similarly for Monte-Carlo type problems.

There is a slight of hand that goes on. The QC solutions are touted in comparison to the challenge of generating an optimal/exact solution. In reality there are many heuristic algorithms that generate solutions that are with 1% of the optimum 99% of the time (choose your numbers - could be 0.1/99.9) It's also important to realise that the process of reading the solution out of a QC is probabilistic, so although the solution might be exact, the noise in the bits you are reading from means that you might have some error there...

It's exciting, people have a job doing it, they are trying to do science... marketing people try and market it as valuable and no one wants to rock the boat as they will be pushed out and the science will stop.

> "we're excited to see what use cases you create for our product,"

The killer feature of the iPhone is making phone calls.

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