The fact this prize exists is admitting that no one has figured out a use for quantum computers.
I have heard this mentioned several times in the last decade or so : "The only thing a quantum computer definitively does better than a classical computer is simulating a quantum computer."
Whether this capability is useful is up in the air.
Note that in practice, classical computers are going to be better at factoring numbers for the foreseeable future.
> There have been XPRIZE competitions for vehicle efficiency, oil spill technology, more efficient rockets, health sensors, AI systems, genomics, etc.
All of which are based on existing technologies that have been delivering for decades if not an entire century (vehicle efficiency). Even something as nebulous as "AI systems" has been around for twenty years in the form of Google's original semantic search capabilities.
This "Quantum AI" prize, however, is a solution in search of a problem.
Ther are plenty of well documented uses for quantum computers, the hardware is just too nascent to fully accommodate them. The most powerful quantum computers today still only have just over 1,000 qbits.
If you have significantly better quantum computers, you can solve realistic problems, yes.
But what's not being spelled out here is that as far as we know classical computers will still totally smoke them unless you allow a large probability of inaccurate results.
And if you are fine with inaccurate results, classical randomized algorithms make it a much more difficult deadline to beat.
Assuming what the public knows about is the state of the art, of course, which I doubt is a good assumption to make. I'm sure major governments have been funneling billions for years into secret projects to be the first to be able to break the (non-post-quantum) communications of everyone else.
The theory of relativity does not in any way enable GPS. GPS is subject to (some) relativistic effects, but that is merely a source of bias, which could be corrected for with just an experience-based correction factor even if we did not understand relativity. If relativity did not exist as a physical concept, GPS would be easier, not harder or impossible. (I guess this misconception comes from xkcd in some form?)
A perhaps more relevant example: Einstein did not have the cell phone camera in mind when developing his theory of the photoelectric effect.
"[N]ature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical, and by golly it's a wonderful problem, because it doesn't look so easy."
>and if you want to make a simulation of nature, you'd better make it quantum mechanical
That is an absurd claim, if taken by itself. Most of nature relevant to us behaves classically. If you want to do simulations of a building or a car or an earthquake or the climate, modeling it as a quantum system would be absurd.
> I have heard this mentioned several times in the last decade or so : "The only thing a quantum computer definitively does better than a classical computer is simulating a quantum computer."
And, hopefully, other quantum systems in general!
I can see that helping with material science. That can have huge multiplier effects on the rest of the economy.
But I agree with you, that other serious applications of quantum computers seem to be thin on the ground.
I thought one of the main advantages of QC was that it could (theoretically) solve existing problems that have exponential time complexity with more efficiency. Isn’t the idea that it could make everything faster? Or did I fall for the marketing.
It's a "solution looking for a problem." Nothing wrong with that. Lasers were theorized in 1917 by Einstein and invented at Bell Labs in 1958, but it took another 20 years before anyone had any idea what the heck to do with them. Now they are the backbone of the internet, among thousands of other applications. Patience, grasshopper.
Wouldn't the D-Wave kinda-but-not-really quantum computer that came out a decade ago be ideal for AI? Annealing sounds like exactly the kind of problem that needs to be solved for ML training?
I can't find mention of it online, but back in 2013 Lockheed Martin purchased a D-Wave machine because they wanted to use it for "AI", which it turned out meant software verification (of fighter jets?) I believe by searching for the possibility of some kind of invalid program state in a large program, which IIRC they couldn't manage to solve with standard solvers. But in that case the number of qubits in a D-Wave machine appears to me far too few for that to be possible, although I don't know the task exactly.
If by "AI" you include operations research (as opposed to statistical machine learning), yes, adiabatic quantum annealing makes sense for certain optimisation problems which you can manage to naturally formulate as a QUBO problem. By 'naturally' I mean it won't blow up the number of variables/qubits, as otherwise a far simpler algorithm on classical computer would be more efficient. I know someone who published QUBO algorithms and ran them on a NASA D-Wave machine, while I myself was using a lot of annealing for optimisation, I didn't want to get involved in that field.
But if you want to do machine learning on a large amount of data using quantum annealing, no, that's terribly badly matched, because the number of physical qubits needed is proportional to the amount of data you want to feed in.
"better". There currently isn't a practical way to brute force factor a 4k rsa key with classical computing. doesn't shor's algorithm on a quantum computer make that feasible? that would be a big driver for quantum computing though maybe my understanding is off.
Quantum computers SHOULD destroy regular computers at knapsack style problems and other combinatorial explosions. The problem there is that decomposing real world problems into 128bit combinatorial selection is really hard.
There's no reason to think that quantum computers will have any fundamental advantage at knapsack problems; the √n advantage from grovers is not substantial when classical computers are going to be many orders of magnitude bigger than quantum computers for the foreseeable future.
If a problem can be reduced to efficiently sampling from the summation of an exponentially large numbers of FFT's, then a quantum computer will destroy a classical computer.
It's largely an open research problem whether there are useful quantum algorithms between those two problem classes.
The big one is simulation of quantum systems, I don’t get how that’s not enough by itself? Classical computers will never be able to simulate quantum systems efficiently so we need quantum computers for that. In general achieving arbitrary precision control of quantum states is something that will open avenues in many areas like sensing as well, so just from that angle alone a working quantum computer would open a new branch of technological progress.
But yeah it’s not guaranteed or even likely that quantum computers will be very useful for many computer science problems, though I’m also optimistic about that given the progress made in the last 30 years. Physics / science isn’t guaranteed to be easy or progress at a steady pace.
I had a guy I knew in a PhD in quantum computing and he told me that he was working on making an algorithm to perform edge detection on an image in O(1), apparently it's possible to do some operation much quicker with quantum computing
Yes, you are. Quantum computers are only believed to be faster than classical computers for a handful of very specific problems. And even those are not 100% proven (we don't have a proof that a faster classical algorithm doesn't exist for any of them, except Grover's search, which is only a quadratic speedup, not an exponential one).
With things like these, I like to think how much money went into the development of the random PR assets like the weird progression diagram. Apparently, that money is better spent of these than say, someone on the Python Language Committee, or your entire Python team.
I just looked at the Killed By Google gallery and it's got 295 rows of 3 dead projects per row (except for the first and last rows of 2). That's 883 kills. That is truly impressive!
I look forward to welcoming "Quantum AI" to that graveyard.
The goog has no qualms in pulling the trigger, that's for sure. Jacquard by ATAP was a favourite of mine. I was going to say that's the price of innovation, but it has been a while since a new successful product release. Maybe the graveyard's fate is to go down in history like xerox parc (innovative but remembered as a footnote). Quite sad.
How is this related to AI in any way? Is it just branded as AI to get some share of that sweet AI money (Probably Google only allows funding new projects if they are AI related at this point)
I had the same question. The site suggests it is a serious research organization focused on quantum computing—but with no obvious connection to artificial intelligence that I could find.
Archived versions of the website [1] mention work on machine learning more prominently, as do some of the linked research publications from a few years back [2].
Perhaps the unit was given the name AI initially but its focus later shifted to quantum computing?
Later: A Google blog post from 2021 explains the connection of quantum to AI better than the current website does [3].
I asked this exact question to one of the leaders of QuantumAI, and he explained it’s a leftover name from when this started out with an idea by some researcher at Google that is no longer part of the project.
I thought you were being hyperbolic and that this was maybe related to quantum neural networks or ML in at least some way, but no - you seem to be totally correct that this is about as 'AI' as snake oil health products are 'quantum.'
Hilarious. Even the connection between AI and Quantum computing is deeply opaque.
When computers first began development during WW2, they were a response to immediate demands for particular functionality from many technical areas. Quantum computing seems to go the exact opposite route, first of building up an (admittedly very interesting) technology and then later figuring out if there actually is anything useful to do with it. The connection to AI is particularly interesting, because it seems to be built entirely out of a combination of two buzzwords.
idk enough physics to say quantum computing is it or not intuitively it seems like understanding matter at a sub atomic level and having a machine that can interact with it seems like a super powerful thing to have. but I think quantum computing is in the same place ML was in the 80s; influential people (mainly Marvin Minsky) said the perceptron and neural nets were a dead end same thing could be happening to quantum computing because of the high expectations
Sure on some level it is extremely interesting and I don't think there is anything wrong with doing research on it. The real issue is pretending that it is something that in the near future will change the world, while having yet to figure what you actually could do with it.
IBM and Google have invested massively because someone there thought that it actually would be useful. But that hasn't happened yet and to be honest it doesn't look like that will change any time soon.
Inregards to Neural Networks, they were pretty much a complete dead end until computing power increased enough to make them viable. In that case an external technology had to come along to make it work.
The milestones specify number of physical qubits, and error rate of logical qubits.
Shouldn't they also specify how many physical qubits are needed to build one logical qubit with the given error rate?
Milestone 6 reduces the error rate from 10^-6 to 10^-13 while increasing physical qubits by only 10x. That doesn't work out well if you need 1000x more physical qubits per logical one...
This is an intimidating contest, are there many groups working on these problems besides the people on their website? I wonder if they put "AI" in the branding to grab the attention of people in quantum about to pivot to LLM's..
> I wonder if they put "AI" in the branding to grab the attention of people in quantum about to pivot to LLM's..
The Quantum AI lab was founded in 2013, about a decade before LLM's took off. The founder of the lab is researching how quantum can be applied to AI: https://www.youtube.com/watch?v=3iEEvRfTTEs
Readit News
I have heard this mentioned several times in the last decade or so : "The only thing a quantum computer definitively does better than a classical computer is simulating a quantum computer."
Whether this capability is useful is up in the air.
Note that in practice, classical computers are going to be better at factoring numbers for the foreseeable future.
Whether or not quantum computers have practical applications, the prize itself is not evidence of that.
All of which are based on existing technologies that have been delivering for decades if not an entire century (vehicle efficiency). Even something as nebulous as "AI systems" has been around for twenty years in the form of Google's original semantic search capabilities.
This "Quantum AI" prize, however, is a solution in search of a problem.
If you have significantly better quantum computers, you can solve realistic problems, yes.
But what's not being spelled out here is that as far as we know classical computers will still totally smoke them unless you allow a large probability of inaccurate results.
And if you are fine with inaccurate results, classical randomized algorithms make it a much more difficult deadline to beat.
A perhaps more relevant example: Einstein did not have the cell phone camera in mind when developing his theory of the photoelectric effect.
I'm not saying quantum computing won't pan out, but if it has to there's some fundamental piece that is missing so far.
In contrast this effort is trying to imagine and monetize GPS before relativity is discovered.
Number factorization and anything else in BQP is also an use for them.
Quantum-everything is linear, how is this distinction overcome?
Also, doesn't solving this problem hint at quantum gravity?
"[N]ature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical, and by golly it's a wonderful problem, because it doesn't look so easy."
0. https://s2.smu.edu/~mitch/class/5395/papers/feynman-quantum-...
That is an absurd claim, if taken by itself. Most of nature relevant to us behaves classically. If you want to do simulations of a building or a car or an earthquake or the climate, modeling it as a quantum system would be absurd.
And, hopefully, other quantum systems in general!
I can see that helping with material science. That can have huge multiplier effects on the rest of the economy.
But I agree with you, that other serious applications of quantum computers seem to be thin on the ground.
If by "AI" you include operations research (as opposed to statistical machine learning), yes, adiabatic quantum annealing makes sense for certain optimisation problems which you can manage to naturally formulate as a QUBO problem. By 'naturally' I mean it won't blow up the number of variables/qubits, as otherwise a far simpler algorithm on classical computer would be more efficient. I know someone who published QUBO algorithms and ran them on a NASA D-Wave machine, while I myself was using a lot of annealing for optimisation, I didn't want to get involved in that field.
But if you want to do machine learning on a large amount of data using quantum annealing, no, that's terribly badly matched, because the number of physical qubits needed is proportional to the amount of data you want to feed in.
Dead Comment
If a problem can be reduced to efficiently sampling from the summation of an exponentially large numbers of FFT's, then a quantum computer will destroy a classical computer.
It's largely an open research problem whether there are useful quantum algorithms between those two problem classes.
You can get a Turing award if you can show this, even theoretically.
Deleted Comment
But yeah it’s not guaranteed or even likely that quantum computers will be very useful for many computer science problems, though I’m also optimistic about that given the progress made in the last 30 years. Physics / science isn’t guaranteed to be easy or progress at a steady pace.
And developing new materials.
A paper i found on edge detection https://arxiv.org/html/2404.06889v1#:~:text=Quantum%20Hadama....
It is my understanding that a use is very straightforward: quickly solving problems in the Big(O) Factorial Class (n!).
I could be misunderstanding QC though.
Dead Comment
I look forward to welcoming "Quantum AI" to that graveyard.
(Or Promotion Oriented Programming.)
Where is the AI?
The founder of the Google Quantum AI lab is researching how quantum computers can be applied to AI: https://www.youtube.com/watch?v=3iEEvRfTTEs
And if so, why aren't they throwing crypto into the mix?
"Quantum Blockchain AI" does kind roll off the tongue nicely...
Archived versions of the website [1] mention work on machine learning more prominently, as do some of the linked research publications from a few years back [2].
Perhaps the unit was given the name AI initially but its focus later shifted to quantum computing?
Later: A Google blog post from 2021 explains the connection of quantum to AI better than the current website does [3].
[1] https://web.archive.org/web/20201210053216/https://quantumai...
[2] https://scholar.google.com//citations?view_op=view_citation&...
[3] https://blog.google/technology/ai/unveiling-our-new-quantum-...
1. Barren plateaus in quantum neural network training landscapes: https://www.nature.com/articles/s41467-018-07090-4
2. Power of data in quantum machine learning: https://www.nature.com/articles/s41467-021-22539-9
3. Quantum advantage in learning from experiments: https://www.science.org/doi/10.1126/science.abn7293
When computers first began development during WW2, they were a response to immediate demands for particular functionality from many technical areas. Quantum computing seems to go the exact opposite route, first of building up an (admittedly very interesting) technology and then later figuring out if there actually is anything useful to do with it. The connection to AI is particularly interesting, because it seems to be built entirely out of a combination of two buzzwords.
Deleted Comment
https://www.xprize.org/about/people/hartmut-neven
IBM and Google have invested massively because someone there thought that it actually would be useful. But that hasn't happened yet and to be honest it doesn't look like that will change any time soon.
Inregards to Neural Networks, they were pretty much a complete dead end until computing power increased enough to make them viable. In that case an external technology had to come along to make it work.
Shouldn't they also specify how many physical qubits are needed to build one logical qubit with the given error rate?
Milestone 6 reduces the error rate from 10^-6 to 10^-13 while increasing physical qubits by only 10x. That doesn't work out well if you need 1000x more physical qubits per logical one...
The Quantum AI lab was founded in 2013, about a decade before LLM's took off. The founder of the lab is researching how quantum can be applied to AI: https://www.youtube.com/watch?v=3iEEvRfTTEs