AI Could Make Quantum Computing Obsolete, Nobel Prize Winner Says

Last week, DeepMind’s Demis Hassabis said that AI might be able to solve problems that quantum computers were supposedly necessary for. Indeed he said that classical systems – AI run on conventional computers – can model quantum systems. Sounds like an innocent claim but is certain to upset a lot of quantum computing researchers. Hassabis bases his argument on the surprising success of Alphafold.

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[Jack_Redview]

Born too early for quantum computing
Born too late for quantum computing
Born just in time for quantum computing

[moletrap]

Quantum computing will neither be a success or a failure, it will forever be suspended in a state of uncertainty.

[parrotraiser]

The principal competition between AI and Quantum Computing is for speculative funding.

[datamatters]

"On the other hand I'm on YouTube...". Hilarious.. Love your humor and your channel.

[richj]

As a technician I have often found that I and other technicians occasionally run into technical system failures that we say "has teeth". That means after going through a troubleshooting process, many times, we can't seem to find the problem (s) to fix it. That is when we either set the device aside, and go work on another device and repair it, or have another technician come over and start the troubleshooting process. Either way, the problem is found pretty rapidly. Why? We in the initial troubleshooting process made an assumption that some component was good. Didn't verify it. A.I. systems, by narrowing down the possibilities might skip over something that fell outside of the narrowed scope of solutions available. That might be where a Quantum computer can be the refreshed eyes looking for the solution to a problem that eludes A.I.. That hybrid method of search might be best as it only gives the Quantum computer a limited number of problems, and lets A.I. find the solution quickly for problems that will fall within it's scope of applied rules.

[gurammarx]

Veritasium recently explored similar ideas behind what contributes a human to be an expert in chess and other professions, the chess master is not memorizing each chess pieces position but rather chunking the configuration/rule that constraints them to their positions

[stevenb]

Together with deep-learning, hyper-parallel algorithms are also emerging as a serious rival to quantum algorithms. Recently, problems seen as strong candidates for quantum advantage, like optimization and combinatorial tasks, have been solved faster using classical algorithms that leverage massive thread parallelism. That is a major problem for quantum firms since GPUs are becoming exponentially more numerous and powerful while quantum computers are struggling to do anything.

[Josh-yurk]

When I was a young teenager, my younger cousin and I had the nerdiest debate with each other. It came after I wondered aloud when in our lifetime we would see computers that could think. He was completely against the idea saying computers can only understand yes or no, ones and zeros, and fundamentally could not understand “maybe”. It occurs to me that AI is exactly that- a way to model on a deterministic system a way for it to learn what is probable. AI does understand maybe and maybe there is some link between how the rules that govern classical computing work and something fundamental we are missing in the rules of quantum mechanics. As for my cousin, he went on to become a physicist, so that’s his domain, but that argument we had long ago is what inspired me to become a computer scientist.

[OBGynKenobi]

It's like saying nothing will outrun a Horse when cars first came out in the late 1800's.

[aaronjennings]

Just take the conventional route until the first right, then follow the quantum path?

AI and quantum computing are not in competition but are synergistic technologies. When combined, they can solve complex problems more efficiently and effectively than either technology could alone, enhancing each other's capabilities

[amcluesent]

Quantum computers maybe able to quickly solve combinatorial optimization and problems with exponential complexity, but ironically building a quantum computer with enough error-free qubits itself becomes exponentially harder as scale increases.

[KGTiberius]

Both self-limiting data and negatives prune analysis. Reduction of questions/operations are the key.
Even the “tree” example is easier if reduced. Trees grow upwards and outwards. Leaves don’t grow from roots. There is a limit to the growth rate. Gravity/stability of the system are also self limiting. Environmental considerations can be applied: Altitude, wind, fire, lightning, walls/cliffs/ceilings, human pruning, animal damage, soil type, substrate issues & obstacles, considerations of time (always forward), etc.

Now if there was a true fractal, that would be quantum computing.

[justmillenialthings]

Yeah as a computer scientist, it doesn't even come down to concepts or ideas here. There is a physical difference in how classical computers compute, and they have physical limits. An actual functioning quantum computer will always be far more powerful than a classical computer with the same number of bits. The problem is the functioning part.

[Shnazzleboxxin]

This is free on the internet for anyone to watch. What an amazing time we live in.

[paralexvr]

I would say, the most useful applications of AI are going to be the ones that we needed the quantum advantage to solve, but not just because Quantum Computers are late, but because AI can not only "solve" but also "generate".

For example, AlphaFold can predict protein folding, that's amazing. But just recently it allowed to make AlphaProteo, which can suggest brand new protein that bind to a specific site of an existing protein. The possibility to transform a "solver" into a "generator" is incredibly valuable.

I don't know if an equivalent strategy exists for Quantum Computing?

[JohnSmith-gugl]

so quantum computing will be used only for encryption and game engines?

[SapienSpace]

What I find fascinating is how Reinforcement Learning (RL) is demoted as "too inefficient", yet it is used in Nobel Prizes winner Hassabis's AlphaFold and AlphaGo work, and Yann LeCunn often says to "get rid of it", but only use it if you are "fighting a ninja" or if your "plan does not work", it is just a "🍒on top of a cake".

[pezpro]

I still think the term "obsolete" is a bit of a stretch considering fields such as cybersecurity, specifically post-quantum encryption exist and offer challenges unique to quantum computing. Quantum computers will still have their uses in fields such as these where deep learning at the moment seems too different, and it's only a matter of time before new problems emerge that deep learning can't quite learn.
Not to mention the large concerns about power draw that arise concerning deep learning and overall scalability. Supercomputers at the current moment are more appealing but I hope to one day see that change as we approach a plateau.

[rich]

I just want to know which one can most persuasively arrive at the wrong answer.

[ErazerPT]

I think of this as "not all functions are continuous". There are constraints everywhere and if your model is good at figuring them out, convoluted as they might be, you cut the search space significantly. ML has been used against cryptography before, and that's one of the places where you simply need to reduce the search space to something more tractable for brute forcing.