401–410 of 557 posts
They opened the API for it and I'm sending requests but the response always comes back 300ms before I send the request, is there a way of handling that with try{} predestined{} blocks? Or do I need to use the Bootstrap Paradox library?
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Google's SHA-1 collision took 2^63.1 hash operations to find. Given that a single hash operation takes more than 1000 cycles, that's only less than three doublings away. Cryptographers worry about big numbers. 2^80 is not considered secure.
It's early so I'm thinking out loud here but I don't think the algorithm scales like this, does it? We're talking about something that can search a list of size N in sqrt(N) iterations. Splitting the problem in two doesn't halve the compute required for each half. If you had to search 100 items on one machine it's taken 10x iterations but split over two it'd take ~7x on each or ~14 in total.
This is not at all a surprising property. The same things happens with binary search: it has complexity O(log(N)), which means that running it on a list of size 1024 will take about 10 operations, but running it in parallel on two lists of size 512 will take 2 * 9 operations = 18.
This is actually easy to intuit when it comes to search problems: the element you're looking for is either in the first half of the list or in the second half, it can't be in both. So, if you are searching for it in parallel in both halves, you'll have to do extra work that just wasn't necessary (unless your algorithm is to look at every element in order, in which case it's the same).
In the case of binary search, with the very first comparison, you can already tell in which half of the list your element is: searching the other half is pointless. In the case of Grober's algorithm, the mechanism is much more complex, but the basic point is similar: Grover's algorithm has a way to just not look at certain elements of the list, so splitting the list in half creates more work overall.
In view of these and other findings my conclusion is that Google Quantum AI’s claims (including published ones) should be approached with caution, particularly those of an extraordinary nature. These claims may stem from significant methodological errors and, as such, may reflect the researchers’ expectations more than objective scientific reality.
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Google is betting on digital quantum computers. There are, however, analog quantum computers, e.g. by Pasqal, which hope to capitalize on this to optimize AI-like high dimension optimization problems.
Why do quantum computers need to be analog to be applied to such problems?
At least as far as I'm aware by digital they probably mean a generally programmable QC, whereas another approach is to encode a specific class of problems in the physical structure of an analog QC so that it solves those problems much faster than classical. This latter approach is less general (so for instance you won't use it to factor primes) but much more attainable. I think D-wave or someone like that already had commercial application for optimization problems (either traveling salesman or something to do with port organization)
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How can I, a regular software engineer, learn about quantum computing without having to learn quantum theory? > Worth spending a little time doing some long tail strategizing I’d say any tips for starters?
Start here: https://youtu.be/F_Riqjdh2oM You don't need to know quantum theory necessarily, but you will need to know some maths. Specifically linear algebra. There are a few youtube courses on linear algebra For a casual set of video: - https://youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFit... For a more formal approach: - https://youtube.com/playlist?list=PL49CF3715CB9EF31D And the corresponding open coursewa…
I’m a quantum dabbler so I’ll throw out an armchair reaction: this is a significant announcement. My memory is that 256 bit keys in non quantum resistant algos need something like 2500 qubits or so; and by that I mean generally useful programmable qubits. To show a bit over 100 qubits with stability, meaning the information survives a while, long enough to be read, and general enough to run some benchmarks on is some…
> Worth spending a little time doing some long tail strategizing I’d say. What do you mean by this?
Honestly, there's not that much to discuss on this though. The only things you can do from this strategizing is to consider even encrypted data as not safe to store, unless you're using quantum resistant encryption such as AES; and to budget time for switching to PQC as it becomes available.
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Because this result is still very far from anything related to practical decryption.
And if they were, would they tell the world?
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The required number of qubits to execute Shor’s algorithm is way larger than 2500 qubits as the error ceiling for logical qubits must decrease exponentially with every logical qubit added to produce meaningful results. Hence, repeated applications of error correction or an increase in the surface code would be required. That would significantly blow up the number of physical qubits needed.
Isn't that what they are claiming is true now? That the errors do decrease exponentially with each qubit added?
To see how it plays out consider adding a single logical qubit. First you need to increase the number of physical qubits to accommodate the new logical qubit at the same error rate. Then multiply the number of physical qubits to accommodate for exponentially decreased error rate which would be a constant factor N ( or polynomial but let’s keep things simple) by which the number of physical qubits need to be multiplied to produce a system with one additional logical qubit with an error rate to produce meaningful results.
To attain 1024 logical qubits for Schor algorithm one would need N^1024 physical qubits. The case where N<1 would be possible if error would decrease by itself without additional error correction.