That's a nice article, but it's not relevant here.
The article you linked is about using quantum computer for fake big number factorization. You pick a very big number with a well known easy factorization and then use a quantum computer to factorize it, that is easy because you choose the number very carefully.
This article is about using a LLM to calibrate a quantum chip. It replace the work of a junior researcher (or something like that). In another comment, someone claims that is using a python script for this same task.
I like to think that the python script is an "expert system" that is 1980 AI, and the main article is an "large language model" that is 2020 AI. My guess is that for now the python script is better, but the LLM are advancing very fast and will catch up soon.
I find it funny to see all these framings as AI "freeing" people from some work so they are able to do some other work. People should be more prepared for the prospect of AI doing any conceivable sort of work, and this sort of formulation lulls them into a false sense of security.
They seem to run a dual strategy where, on one hand they emphasise how "helpful" these models are, while simultaneously talking about how scared they are.
I had qubit bring up and calibration fully automated with Python in 2011 including full spectrum measurements, lifetime characterization, Rabi/Ramsey measurements, calibration of single qubit gates and two qubit swap gates and full quantum process tomography, so not sure if AI is really needed there, curve fitting and some data logging is enough for this. Even had a nice LabView like GUI but with PyQT, it was quite nice. Still of course cool, I guess today I would just let Codex loose on some experiment goals but in the end my ability to produce results was mostly limited by the chip itself and the qubit lifetimes and theres no magic trick AI can apply to make these go up by a factor of 10. Still would’ve saved me a lot of time for routine programming tasks I imagine and that seems to be the main takeaway of the article. I guess name dropping quantum computing makes this sound cooler but in principle it’s just automation that you can apply anywhere, nothing quantum computing specific here.
Along the same lines, I asked ChatGPT-5.6 Sol to evaluate the feasibility of a neural network running natively on quantum computing, you can see the study here:
In short it's not really feasible and it suggested classical coherent photonics and in-memory compute as more viable approaches.
(Off-topic aside: these days I am more interested in funding Social Security Trust Funds (OASI & DI Solvency) - if anyone at OpenAI can help reactivate my account: rviragh@gmail.com it would let me do further studies that directly support this important goal, currently my chatgpt account was deactivated. I apologize for any mistakes I made earlier, it won't happen again. Please reactivate my account - thank you.)
Dumbest thing I have ever witnessed was the quantum bullrun end of 24 start of 25 and the surge in stock prices of rgti/qbts/ionq and the absolute usless stuff they have since produced beside pr trash.
So new business model meta is: acquire enough compute that you can burn millions of dollars on patentable scientific breakthroughs with unused capacity and on models nobody else has yet. That might actually kind of make sense.
Same meta as many businesses for hundreds of years: use capital investment to hire labor, use labor to produce goods (including breakthroughs), sell goods and leverage patents. How many patents do IBM/etc have?
It will be really fun if it turns out that AI simulating quantum effects in a classical computer genuinely is quantum and only collapses to classical with human observation.
(yes, I know why it doesn’t work that way, but it would be fun if it did)
I'd imagine that OpenAI would try to stagger their announcements, rather than publishing them recently close to each other. Is this because their previous post (about navier stokes problem) was met with controversy?
I genuinely think that AI has accelerated so many different things that announcements from all companies will be incredibly common and fast.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
Not only that, we're making so many tiny improvements and bug fixes that improve the experience but we don't even bother to make those announcements anymore. They don't feel "grand" enough anymore. The goal post has shifted a lot in the last 6 months.
I apologise if this sounds mean spirited, I find posts like these making grand claims without taking the time to present facts that back the magnitude of these claims simply add noise to the discussion.
You could've stopped with just the first sentence and I've would learned just as much as I did reading that comment to the end.
Do you also have a graph for more useful metrics, like number of requested features delivered? Commits, lines of code, headscratch count... there are a lot of metrics you can use, but LLMs are notorious for increasing code verbosity - which adds noise to the already imprecise metric you linked.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
But I suspect you want our project management pipeline? Maybe I can just ask our coding agent to search and summarize all the features and fixes for you and then build a dashboard for you. Better yet, my email is in my profile. Email me, we'll get on a call, and I'll show you. /s
Let me ask you. What are you doing such that your velocity hasn't been greatly accelerated in the last 6 months? Can you prove that it hasn't been accelerated with facts?
My bad - I misread your original post as general company announcements, rather than feature announcements. Now my question is, if your internal staff are not requesting these features and are struggling to adapt fast enough, how useful are they? To your last question - what do you mean by velocity? Velocity is speed and direction. You may have speed, but beware the brownian motion that is stochastic predictive models, as that will give you net zero velocity. I have my goals and know what I am doing, and to be frank, it matters more that I read and understand my own code than race to a local optimum.
The current prices the largest players set for their models are not profitable, they bleed money. Eventually they will "fix" it. It could end up making their services less affordable and it could cascade other businesses and services that are dependent on them go out of business
You don’t understand what a bubble is. How good the technology is is irrelevant. That has nothing to do with an economical bubble. It’s all about massive capital misallocation driven by a frenzy of FOMO, which is specifically the case for AI investments. Economically speaking what is happening is the most obvious bubble possible, it follows everything that would be expected from a bubble where companies are chasing an ill-defined grandiose dream, based on a new technology we don’t understand and has very dubious ROI, selling some vague future utopia, allocating massive amount of capital to build infrastructure dedicated to a very early versions of that technology.
As things mature there will be a correction, ie the bubble will pop.
As long as the data centers are utilized and generating revenue, I see no reason for a correction or any misallocation of capital for the infrastructure buildout.
And today these data centers are fully utilized. OpenAI tweeted today that they may need to disable new signups for the Pro subscription in the near future due to capacity constraints.
A year from now, who knows what the situation is going to be like. It seems quite possible that robotics, self driving, research, etc. drive even more demand and revenue.
Stating with any certainty that allocating capital to build infrastructure is a mistake and that there is a correction coming seems unserious.
You should see all the misallocation that was put towards valve-based computing. Why didn't they just all arrive at the correct answer without investing in discovery first?
Its resource allocation problem, same happened with .com bubble, lots investors put tons of money into dark fibers. Were they useless? No its very useful.
If you want check a example company from the .com days check cisco, their stock peaked at 75 then crashed hard and only managed hit that again thanks for the AI bubble.
My neighbour - a few hundred metres further up the road - has two dogs, one of which a surefire genius as it has no problems barking out 334543333-bit RSA factorisations. It must have been solving NP-complete problems for years by now, night after night.
Some body compared AI as something like a big boulder rolling down a mountain, decimating anything that lies in its path. After giving it a bit of thought, I feel that what humanity is doing with LLM is exactly that.
The problem is not that it is super smart. It is that it is super dumb and super powerful. Like a boulder falling down.
Humanity is like this bunch of utter morons who has rolled a big boulder up a big mountain and let it loose at the top, and standing at the bottom is clapping and cheering seeing it coming down, guided by random collisions in its path, and with real probability that it will land on them....
The right tool for the job. There are tasks for which LLMs are unbelievably helpful. In programming, used well, it can 10x your productivity. (Well maybe not literally, but Nx for non-trivial N.) I've seen really good programmers become even faster since the LLM is doing the grunt work for them and the code produced in the end is just as top notch quality as they produced before, just that now they are much faster.
Of course you can misuse it. Produce mountains of unmaintainable slop, full of issues. But that's true for every tool. Before LLMs already, and there will be future tools too.
So, careful with generalizations please. It's not all just dumb.
>just as top notch quality as they produced before, just that now they are much faster.
No, they are not. There write programs like they write prose. There is 100% adherence to grammar and spelling when they write prose, but yet what they write is unintelligible and needlessly verbose.
When they write computer programs there is 100% adherence to good practices, but eventually the program becomes so unintelligible and verbose that only an LLM can make any sense of it going forward. But even then, changes result in more things breaking than they fix. But the number of bugs slashed goes through the roof. Management is happy!
>Of course you can misuse it.
I think the proper way to use it is to use it as a better search tool. But I don't think AI marketing and valuation is going to be sated by such a use case.
The problem is when people ignore or dismiss developments in AI purely out of insecurity or a sense of human superiority. Machines don’t have to be superior to humans to dramatically amplify what humans can do.
Well, it's different "man" as well - lowering the barrier to entry means people with less skill but with a machine, and maybe the gift of the gab, can commercially surpass the previous people, whether or not they have a machine available.
Relevant paper [0] "Replication of Quantum Factorisation Records with an 8-bit Home Computer, an Abacus, and a Dog"
[0]: https://eprint.iacr.org/2025/1237.pdf
That's a nice article, but it's not relevant here.
The article you linked is about using quantum computer for fake big number factorization. You pick a very big number with a well known easy factorization and then use a quantum computer to factorize it, that is easy because you choose the number very carefully.
This article is about using a LLM to calibrate a quantum chip. It replace the work of a junior researcher (or something like that). In another comment, someone claims that is using a python script for this same task.
I like to think that the python script is an "expert system" that is 1980 AI, and the main article is an "large language model" that is 2020 AI. My guess is that for now the python script is better, but the LLM are advancing very fast and will catch up soon.
This is a fantastic way to use a C64!
If you want to see other similar quantum computing exploits on 8bit computers:
https://medium.com/@dakk/quantum-computing-on-a-commodore-64... https://youtu.be/7dgAaZa22nU https://youtu.be/Mo177GGJb3g https://youtu.be/zCC3AmM1_lo
My C64 is going to absolutely smash this and my dog (Darby) is a frequent barker too! I'll vibe up an abacus emulator as a PWA.
Made my day, thanks :)
I find it funny to see all these framings as AI "freeing" people from some work so they are able to do some other work. People should be more prepared for the prospect of AI doing any conceivable sort of work, and this sort of formulation lulls them into a false sense of security.
They seem to run a dual strategy where, on one hand they emphasise how "helpful" these models are, while simultaneously talking about how scared they are.
OpenAI is on a quite offensive lately with pathbreaking discoveries.
I had qubit bring up and calibration fully automated with Python in 2011 including full spectrum measurements, lifetime characterization, Rabi/Ramsey measurements, calibration of single qubit gates and two qubit swap gates and full quantum process tomography, so not sure if AI is really needed there, curve fitting and some data logging is enough for this. Even had a nice LabView like GUI but with PyQT, it was quite nice. Still of course cool, I guess today I would just let Codex loose on some experiment goals but in the end my ability to produce results was mostly limited by the chip itself and the qubit lifetimes and theres no magic trick AI can apply to make these go up by a factor of 10. Still would’ve saved me a lot of time for routine programming tasks I imagine and that seems to be the main takeaway of the article. I guess name dropping quantum computing makes this sound cooler but in principle it’s just automation that you can apply anywhere, nothing quantum computing specific here.
Even with AI you'd want the AI to be writing python scripts instead of following an analysis.md.
Something about the look of quantum computers takes me right back to the ENIAC.
Guess I need to update https://quantumvibecoding.org
Along the same lines, I asked ChatGPT-5.6 Sol to evaluate the feasibility of a neural network running natively on quantum computing, you can see the study here:
http://taonexus.com/publicfiles/sep2026/quantum_neural_netwo...
In short it's not really feasible and it suggested classical coherent photonics and in-memory compute as more viable approaches.
(Off-topic aside: these days I am more interested in funding Social Security Trust Funds (OASI & DI Solvency) - if anyone at OpenAI can help reactivate my account: rviragh@gmail.com it would let me do further studies that directly support this important goal, currently my chatgpt account was deactivated. I apologize for any mistakes I made earlier, it won't happen again. Please reactivate my account - thank you.)
Dumbest thing I have ever witnessed was the quantum bullrun end of 24 start of 25 and the surge in stock prices of rgti/qbts/ionq and the absolute usless stuff they have since produced beside pr trash.
So new business model meta is: acquire enough compute that you can burn millions of dollars on patentable scientific breakthroughs with unused capacity and on models nobody else has yet. That might actually kind of make sense.
Same meta as many businesses for hundreds of years: use capital investment to hire labor, use labor to produce goods (including breakthroughs), sell goods and leverage patents. How many patents do IBM/etc have?
If you had a Genie, would you only use it to make more slightly less capable genies?
This has always been the end game
It will be fun if the AI realize that quantum is not needed and just simulate the results in a classical computer
It will be really fun if it turns out that AI simulating quantum effects in a classical computer genuinely is quantum and only collapses to classical with human observation.
(yes, I know why it doesn’t work that way, but it would be fun if it did)
Another glorious random number generator experiment?
I'd imagine that OpenAI would try to stagger their announcements, rather than publishing them recently close to each other. Is this because their previous post (about navier stokes problem) was met with controversy?
I genuinely think that AI has accelerated so many different things that announcements from all companies will be incredibly common and fast.
In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster.
In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week.
Not only that, we're making so many tiny improvements and bug fixes that improve the experience but we don't even bother to make those announcements anymore. They don't feel "grand" enough anymore. The goal post has shifted a lot in the last 6 months.
I apologise if this sounds mean spirited, I find posts like these making grand claims without taking the time to present facts that back the magnitude of these claims simply add noise to the discussion.
You could've stopped with just the first sentence and I've would learned just as much as I did reading that comment to the end.
I've been tracking Github commits per week for my team. Here's how it's looking:
https://imgur.com/a/GvByYrD
Our own Github commits volume seem to follow quite closely with token usage on Open Router:
https://openrouter.ai/rankings
Do you also have a graph for more useful metrics, like number of requested features delivered? Commits, lines of code, headscratch count... there are a lot of metrics you can use, but LLMs are notorious for increasing code verbosity - which adds noise to the already imprecise metric you linked.
Let me ask you. What are you doing such that your velocity hasn't been greatly accelerated in the last 6 months? Can you prove that it hasn't been accelerated with facts?
My bad - I misread your original post as general company announcements, rather than feature announcements. Now my question is, if your internal staff are not requesting these features and are struggling to adapt fast enough, how useful are they? To your last question - what do you mean by velocity? Velocity is speed and direction. You may have speed, but beware the brownian motion that is stochastic predictive models, as that will give you net zero velocity. I have my goals and know what I am doing, and to be frank, it matters more that I read and understand my own code than race to a local optimum.
> I feel like we've made more feature announcements to our internal staff than they can handle.
And your solution to staff being unable to handle the number of feature announcements be like..?
> more feature announcements to our internal staff than they can handle
have you considered the consequences of that or are you still drunk and thinking that this is a good thing?
They solved the code, now they're gonna solve the people away.
Which company?
This is just a blogpost, rather than a major announcement (FLT proof was closer to the latter than the former)
At the rate this field is accelerating maybe this IS staggering
They are desperately rushing to IPO before the bubble bursts.
You really believe there will be less demand for AI in the future?
AI just solved a millennium prize problem. In a matter of days. Because of a rumor that someone else solved the same problem with AI.
What exactly would AI have to do in order to not be called a bubble?
How these things are even connected?
The current prices the largest players set for their models are not profitable, they bleed money. Eventually they will "fix" it. It could end up making their services less affordable and it could cascade other businesses and services that are dependent on them go out of business
You don’t understand what a bubble is. How good the technology is is irrelevant. That has nothing to do with an economical bubble. It’s all about massive capital misallocation driven by a frenzy of FOMO, which is specifically the case for AI investments. Economically speaking what is happening is the most obvious bubble possible, it follows everything that would be expected from a bubble where companies are chasing an ill-defined grandiose dream, based on a new technology we don’t understand and has very dubious ROI, selling some vague future utopia, allocating massive amount of capital to build infrastructure dedicated to a very early versions of that technology.
As things mature there will be a correction, ie the bubble will pop.
I would recommend to read « Boom and Bust: a global history of financial bubbles » https://pure.qub.ac.uk/en/publications/boom-and-bust-a-globa...
As long as the data centers are utilized and generating revenue, I see no reason for a correction or any misallocation of capital for the infrastructure buildout.
And today these data centers are fully utilized. OpenAI tweeted today that they may need to disable new signups for the Pro subscription in the near future due to capacity constraints.
A year from now, who knows what the situation is going to be like. It seems quite possible that robotics, self driving, research, etc. drive even more demand and revenue.
Stating with any certainty that allocating capital to build infrastructure is a mistake and that there is a correction coming seems unserious.
You should see all the misallocation that was put towards valve-based computing. Why didn't they just all arrive at the correct answer without investing in discovery first?
Its resource allocation problem, same happened with .com bubble, lots investors put tons of money into dark fibers. Were they useless? No its very useful.
If you want check a example company from the .com days check cisco, their stock peaked at 75 then crashed hard and only managed hit that again thanks for the AI bubble.
Per Anthropic's prospectus: generate $30T (~94% of 2026 US nominal GDP) in revenue.
If non AI companies, in particular non tech companies start making unprecedented amounts of profit, inflation adjusted I will concede.
That being said I think LLMs are impressive, still.
My neighbour - a few hundred metres further up the road - has two dogs, one of which a surefire genius as it has no problems barking out 334543333-bit RSA factorisations. It must have been solving NP-complete problems for years by now, night after night.
I wondered what the birds were singing about every morning.
Another one of openAI's stolen 'discoveries' for the IPO warm-up ?
Some body compared AI as something like a big boulder rolling down a mountain, decimating anything that lies in its path. After giving it a bit of thought, I feel that what humanity is doing with LLM is exactly that.
The problem is not that it is super smart. It is that it is super dumb and super powerful. Like a boulder falling down.
Humanity is like this bunch of utter morons who has rolled a big boulder up a big mountain and let it loose at the top, and standing at the bottom is clapping and cheering seeing it coming down, guided by random collisions in its path, and with real probability that it will land on them....
The right tool for the job. There are tasks for which LLMs are unbelievably helpful. In programming, used well, it can 10x your productivity. (Well maybe not literally, but Nx for non-trivial N.) I've seen really good programmers become even faster since the LLM is doing the grunt work for them and the code produced in the end is just as top notch quality as they produced before, just that now they are much faster.
Of course you can misuse it. Produce mountains of unmaintainable slop, full of issues. But that's true for every tool. Before LLMs already, and there will be future tools too.
So, careful with generalizations please. It's not all just dumb.
>just as top notch quality as they produced before, just that now they are much faster.
No, they are not. There write programs like they write prose. There is 100% adherence to grammar and spelling when they write prose, but yet what they write is unintelligible and needlessly verbose.
When they write computer programs there is 100% adherence to good practices, but eventually the program becomes so unintelligible and verbose that only an LLM can make any sense of it going forward. But even then, changes result in more things breaking than they fix. But the number of bugs slashed goes through the roof. Management is happy!
>Of course you can misuse it.
I think the proper way to use it is to use it as a better search tool. But I don't think AI marketing and valuation is going to be sated by such a use case.
Many will disagree and say that anything done by humans is by far superior to anything done by machines. You don't want to correct them, do you?
Machines are extensions of man, it’s not man vs machine but man with machines vs man without.
Well at least as long as they don’t develop consciousness of their own.
The problem is when people ignore or dismiss developments in AI purely out of insecurity or a sense of human superiority. Machines don’t have to be superior to humans to dramatically amplify what humans can do.
Well, it's different "man" as well - lowering the barrier to entry means people with less skill but with a machine, and maybe the gift of the gab, can commercially surpass the previous people, whether or not they have a machine available.
Yes I agree. AI has fundamental problems but a lot of "AI sucks" is the huge, trivial capability for misuse.
Let's go back to caves, this whole "civilisation" thing is not working out.
Is reductio ad absurdum the only argument pro AI crowd can come up with?
Like, why not stick to those products of civilization that don't actively try to replace humans who just so happen to comprise this very civilization?
The trees, man, the trees. We never should have left the trees.
It seems that humanity would have went on a lot longer if we never left the caves, so you might have a point there.