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.
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.
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.
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
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.
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.
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?
Yes I agree. AI has fundamental problems but a lot of "AI sucks" is the huge, trivial capability for misuse.
It will be fun if the AI realize that quantum is not needed and just simulate the results in a classical computer
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.
Which company?
This is just a blogpost, rather than a major announcement (FLT proof was closer to the latter than the former)
They are desperately rushing to IPO before the bubble bursts.
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?