The “JVG algorithm” only wins on tiny numbers
Posted by jhalderm 1 day ago
Comments
Comment by MathMonkeyMan 1 day ago
Comment by Strilanc 18 hours ago
It's inaccurate to say it wins on small numbers because on small numbers you would use classical computers. By the time you get to numbers that take more than a minute to factor classically, and start dreaming of quantum computers, you're well beyond the size where you could tractably do the proposed state preparation.
Comment by measurablefunc 1 day ago
Comment by Strilanc 17 hours ago
[1]: https://quantumfrontiers.com/2026/01/06/has-quantum-advantag...
[2]: https://quantumfrontiers.com/2026/01/25/has-quantum-advantag...
[3]: https://quantumfrontiers.com/2026/02/28/what-is-next-in-quan...
Comment by gsf_emergency_7 16 hours ago
Comment by adgjlsfhk1 23 hours ago
Comment by measurablefunc 22 hours ago
Comment by adgjlsfhk1 22 hours ago
Comment by gsf_emergency_7 19 hours ago
Comment by RcouF1uZ4gsC 1 day ago
Comment by kittikitti 23 hours ago
Shor's algorithm is part of BQP. Is the JVC algorithm part of BQP, even though it utilizes classical components? I think so.
I believe that the precomputational step is the leading factor in the algorithm's time complexity, so it isn't technically a lower complexity than Shor's. If I had to speculate, there will be another class in quantum computational complexity theory that accommodates precomputation utilizing classical computing.
I welcome the work, and after a quick scroll through the original paper, I think there is a great amount of additional research that could be done in this computational complexity class.
Comment by amluto 23 hours ago
The JVG algorithm is not a high quality example of this or really anything else. If you think of it as “classical advice”, then it fails, because the advice depends on the input and not just the size of the input. If you think of it as precomputation, it’s useless, because the precomputation involved already fully solves the discrete log problem. And the JVG paper doesn’t even explain how to run their circuit at respectable sizes without the sheer size of the circuit making the algorithm fail.
It’s a bit like saying that one could optimize Stockfish to run 1000x faster by giving it an endgame table covering all 16-or-fewer-piece-positions. Sure, maybe you could, but you also already solved chess by the time you finish making that table.
Comment by adgjlsfhk1 23 hours ago
Comment by kmeisthax 1 day ago
Comment by dehrmann 1 day ago
Comment by bawolff 14 hours ago
Quantum computers are still cool and things worthy of research. Its going to be a very long road though. Where we are with quantum computers is like equivalent to where we were with regular computers in the 1800s.
The hype people just make everything suck and should be ignored.
Comment by dekhn 1 day ago
People get taken by the theoretical coolness and ultimate utility of the idea, and assume it's just a matter of clever ideas and engineering to make it a reality. At some point, it becomes mandatory to work on it because the win would be so big it would make them famous and win all sorts of prizes and adulation.
QC is far earlier than "linear regression" because linear regression worked right away when it was invented (reinvented multiple times, I think). Instead, with QC we have: an amazing theory based on our current understanding of physics, and the ability to build lab machines that exploit the theory, and some immediate applications were a powerful enough quantum computer built. On the other side, making one that beats a real computer for anything other than toy challenges is a huge engineering challenge, and every time somebody comes up with a QC that does something interesting, it spurs the classical computing folks to improve their results, which can be immediately applied on any number of off-the-shelf systems.
Comment by antonvs 17 hours ago
Good description. Commercial fusion power seems to be in the same category currently.
The next step once you have enough thinkers working on the problem is to start pretending that commercial success is merely a few years away, with 5 or 10 years being the ideal number.
Comment by jerf 12 hours ago
Everything else I consider pretty silly. "It can improve logistics" - I'm fairly sure computers are already as good as they can be, what dominates logistics calculations isn't an inability to optimize but the fact the real world can only conform so closely to any model you build. "It can improve finance" - same deal, really. All the other examples I see cited are problem where we've probably already got running code that is at the noise floor imposed by reality and its stubborn unwillingness to completely conform to plans.
If I had $1 to invest between AI and quantum computing I'd end up rounding the fraction of a cent that should rationally go to quantum computing and put the whole dollar in AI.
By far the most exciting possibility is one that Scott Aaronson has cited, which is, what if quantum computers fail somehow? To put it in simple and unsophisticated terms, what if we could prove that you can't entangle more than 1024 qubits and do a certain amount of calculation with them? What if the universe actually refuses to factor a thousand-digit prime number? The way in which it fails would inevitably be incredibly interesting.
Comment by adrian_b 5 hours ago
It breaks only classical public-key encryption.
Public-key encryption is not necessary within a closed organization, e.g. for the personal use of an individual or group of individuals, or within a spy agency or for military applications, though it can make slightly simpler the process of key distribution, which otherwise needs an initial physical pairing between devices.
The most important application of public-key encryption is for allowing relations between parties who have never met in person, by the use of digital signatures and of Diffie-Hellman key establishment protocols.
This has been essential to enable online shopping and online banking, but not for the more traditional uses of cryptography.
Comment by adgjlsfhk1 23 hours ago
Comment by Tyr42 1 day ago
Comment by omoikane 9 hours ago
https://news.ycombinator.com/item?id=44608622 - Replication of Quantum Factorisation Records with a VIC-20, an Abacus, and a Dog (2025-07-18, 25 comments)
Comment by ashivkum 1 day ago
Comment by Strilanc 18 hours ago
The trickiest part of the circuit is they compile conditional multiplication by 4 (mod 15) into two controlled swaps. That's a very elegant way to do the multiplication, but most modular multiplication circuits are much more complex. 15 is a huge outlier on the difficulty of actually doing the modular exponentiation. Which is why so far 15 is the only number that's been factored by a quantum computer while meeting the bar of "yes you have to actually do the modular exponentiation required by Shor's algorithm".
Comment by adgjlsfhk1 15 hours ago
Comment by Strilanc 10 hours ago
Shor's algorithm specifies that you should pick the base (which determines the multipliers) at random. Somehow picking a rare base that is cheap to do really does start overlapping with knowing the factors as part of making the circuit. By far the biggest cheat you can do is to "somehow" pick a number g such that g^2=1 (mod n) but g isn't 1 or N-1. Because that's exactly the number that Shor's algorithm is looking for, and the whole thing collapses into triviality.
Comment by guy4261 1 day ago
Comment by apnorton 20 hours ago
From another view, Adelson-Velsky and Landis called their tree algorithm "an algorithm for the organization of information" (or, rather, they did so in Russian --- that's the English translation). RSA was called "a method" by Rivest, Shamir, and Adleman. Methods/algorithms/numbers/theorems/etc. generally are not given overly specific names in research papers, in part for practical reasons: researchers will develop many algorithms or theorems, but a very small proportion of these are actually relevant or interesting. Naming all of them would be a waste of time, so the names tend to be attached well after publication.
To name something after oneself requires a degree of hubris that is looked down upon in the general academic community; the reason for this is that there is at least a facade (if not an actual belief) that one's involvement in the sciences should be for the pursuit of truth, not for the pursuit of fame. Naming something after yourself is, intrinsically, an action taken in the seeking of fame.
Comment by johncarlosbaez 1 day ago
In my "crackpot index", item 20 says:
20 points for naming something after yourself. (E.g., talking about the "The Evans Field Equation" when your name happens to be Evans.)
Comment by ajkjk 23 hours ago
Comment by yccs27 16 hours ago
> By doing so, we aim to provide a novel paradigm [...]
also made me think of item 19 on your list:
> 10 points for claiming that your work is on the cutting edge of a "paradigm shift".
I'm sad though that you didn't call it the "Baez crackpot index"...
Comment by zahlman 1 day ago
Comment by goodmythical 1 day ago
Comment by ot 1 day ago
In the original paper they do not give it any name: https://people.csail.mit.edu/rivest/Rsapaper.pdf
Comment by abound 1 day ago
Comment by PLenz 1 day ago
Comment by antonvs 17 hours ago
Comment by croes 1 day ago
Comment by zimpenfish 18 hours ago
But also note that naming an algorithm, in and of itself, is fine; it's naming it after yoursel(f,ves) in the initial paper that's a sign of crackpottery.
* Named by: Probably fine but heavily weighted on the grandiosity of the title.
* Named after: Almost certainly fine (unless it's something like "X's Absolute Drivel Faced Garbage That Never Works Because X Kidnapped My Dog And Is A Moral Degenerate Algorithm", obvs.)
* Named by yoursel(f,ves) after yoursel(f,ves): In the initial paper? Heavy likelihood of crackpottery. Years later? Egotistical but strong likelihood of being a useful algorithm.