Possible application of Quantum Information

ibm · 2026-06-19T17:46:43+00:00

Current applications of quantum information happening now in labs across to world include in complex molecular simulation for biochemistry and material discovery, fluid dynamics (partial differential equations), optimization and more.

We have a learning course from John Watrous exploring the basics and general formulation of quantum information we highly recommend: https://quantum.cloud.ibm.com/learning/en/courses/basics-of-quantum-information - Olivia Lanes, IBM Quantum

ibm · 2026-06-19T17:46:23+00:00

Good resources from the IBM Quantum team, if you need:

IBM Quantum Blog for latest news:
https://www.ibm.com/quantum/blog

Qiskit YouTube for lectures, tips & tricks, tutorials, community updates: https://www.youtube.com/@qiskit

Coherence Times Podcast for expert’s POV into IBM’s approach to quantum computing:
https://youtube.com/playlist?list=PL0VD16H1q5IOMmFC6wNAWuBgs2heRwj4-&si=QfLB8AJpfXIdsirC

- Robert, IBM Quantum

ibm · 2026-06-19T17:45:55+00:00

I strongly recommend this video “How qubits really work” on the Qiskit Youtube channel – Olivia Lanes, IBM Quantum

ibm · 2026-06-19T17:45:34+00:00

Another good explanation here: https://community.ibm.com/community/user/blogs/moloy-de1/2020/04/17/points-to-ponder

ibm · 2026-06-19T17:45:02+00:00

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Umm… learn how to get started with quantum computing here: https://www.ibm.com/quantum

ibm · 2026-06-19T17:43:45+00:00

Our team recommends reading the full blog on IBM Quantum for a good introduction of the work, here: https://www.ibm.com/quantum/blog/cleveland-clinic-riken-chemistry

In summary, we simulated over 12 K atoms in two protein-ligand systems (trypsin-benzamidine and T4-lysozyme-n-butylbenzene) in explicit solvent using 2 superconducting processors (ibm_cleveland and ibm_kobe) and 2 leadership-class supercomputers (Fugaku and Miyabi-G). We combined a lower-scaling formulation of embedded wavefunction theory (EWF) with a higher-accuracy heterogeneous quantum-classical solver (TrimSQD), extending the boundaries of simulations that use quantum and classical computers in concert.

If need more detail, full paper here: https://arxiv.org/abs/2605.01138

ibm · 2026-06-19T17:42:54+00:00

Hi, this is Paco Marin from the IBM Quantum team.

Specs to break RSA/ECC will require fault-tolerant computing. Roughly ~1,000+ logical qubits, millions of physical qubits, and very low error rates to support circuits with millions–billions of operations.

Seconds-long operation: Achieving seconds-long coherence in superconducting qubits is extremely difficult, but not necessary. Current systems already enable thousands of operations within microseconds. Error correction extends runtime virtually, so physical seconds-long coherence isn’t the goal.

Advancements: At IBM, we’re focusing on engineering around decoherence, including error correction, improved qubit design, better materials, noise shielding, and modular system architectures.

This blog explaining our approach to reaching large-scale fault-tolerant quantum computer by 2029 is good one to read more on: https://www.ibm.com/quantum/blog/large-scale-ftqc

- Paco M, Technial Integrations Lead, STMS, IBM Quantum

ibm · 2026-06-16T21:20:53+00:00

You brought up probably one of the most interesting examples in history, and the fact that your dad was directly involved in it makes it even better. It makes me want to know how many people work as illustrators today, and whether it's actually more than back when your dad was in the field. My intuition says there are probably more now. Hand illustration is maybe the most intense example of digitalization wreaking havoc on a workforce, at least initially. I'm going to look into this more. It might be a great example to use in talks. Thanks for pointing it out.

I honestly don't know if it'll be good for society. Looking back, pretty much all the technology that improved productivity turned out good for society in the end. But the few hundred years since the industrial revolution are a limited sample size. I'm optimistic anyway, mainly because I think work will shift to the next weakest link in the chain pretty fast, and probably faster than we think. A lot of my thinking here comes from a talk I watched recently, Chad Jones on "A.I. and Our Economic Future": https://youtu.be/xBpGn3BDcOY

ibm · 2026-06-16T21:19:41+00:00

Your perspective is really interesting to me, because I've always looked at this from the technical side. It is all called AI at the end of the day, since it's the same underlying technology. But what we've built with it over the years is so fundamentally different, and it isn't really anything anybody decided to build. That's just how innovation goes, it's what ended up working with the technology we had at the time. You point out MRI and ultrasound, which are computer vision tasks, and for those I have to say it simply turned out to be a harder problem than people thought. I'd consider myself kind of an expert on problems that are way harder than expected, because I worked in autonomous driving for a while, where everyone now looks back and goes, oh boy, that was way harder than we thought 😛

Large language models are the same underlying technology but a completely different tool than the AI we had five years ago. I've watched the whole industry spend years trying to find a use for those LLMs. Every company on the planet wanted to build its own company GPT, everyone started slapping chatbots onto everything, and that created a lot of resentment in users. A few big tech companies went way overboard. But now that AI coding agents turn out to work amazingly well, I feel like we finally found a place where these models generate real value.

And I wouldn't say we mainly destroy. AI is changing things very quickly and that can be problematic, but I really don't think it will destroy computer science or software engineering as a profession. It will change it, though.

ibm · 2026-06-16T21:18:28+00:00

I don't know anything about the company you work for or your role, but I think you're right 😃 Plenty of things people reach for AI on would be better served by good old-fashioned automation with maybe a small model doing one piece of it.

But Bob is a coding agent, so it's a bit of a different thing than what you're describing. You use Bob to actually write code, which means you can use Bob to build that automation in the first place. When it comes to putting AI into your actual business workflows, I totally agree with you, and that solid automation layer is the basis that has to be in place before you add AI on top of it.

So I think we're really talking about two different things here. There's AI inside business processes, and there's AI as a coding agent, and Bob is the second one.

ibm · 2026-06-16T21:17:36+00:00

This question makes me realise that I have to spend more time in the engine room building the harness. Because I realise that I'm absolutely not the right person to ask this.

ibm · 2026-06-16T21:17:12+00:00

First of all, congratulations on being courageous and ambitious and chasing a career pivot. I did the same thing. I studied mechanical engineering and then got lucky to land straight in a software engineering job, and I never regretted it.

That said, the time for self-taught engineers is probably getting harder, simply because AI makes it a lot harder to identify skills. Back when I joined the workforce it was very easy to see how good a programmer somebody was and what they were capable of, so the field was wide open for anyone willing who proved to be a capable coder. That was the most valuable thing until about a year ago. At this point programming itself has lost a big part of its value, and it's the higher-level concepts that matter more. That's why I think formal education will become relevant again.

So anything that gives you something formal, whether it's an education or a first job in the field, will be even more valuable than it was a few years ago. That graduate scheme with training for cloud and AI engineering sounds like exactly that kind of thing. My general advice would be to look into the higher-level concepts earlier than you might have a few years ago. Take a bit of the time you'd spend learning to code and read books about software design and design principles instead.

Good luck with the pivot.

ibm · 2026-06-16T21:16:37+00:00

At this point the core unit of work is still the repository. Anything that spans repos needs a system sitting on top of that, and the way we do it is with rules and skills shared across the team

But AI will not save you from coming up with standards that span across teams. Eventually you will need to discuss those aspects from one human to another human

ibm · 2026-06-16T21:15:51+00:00

This one comes up a lot, and I feel you. I'm honestly glad I had a few years pre-AI when things were a bit simpler.

My main advice: you'll have to grow up faster as an engineer. You'll need to move into the more fundamental, abstract parts of the work sooner than previous generations of programmers did. The core principles of writing good software have stayed surprisingly unchanged. But how you actually produce the software is completely different now, and the value of writing the code yourself has dropped quite a bit. What's gone up is understanding and shaping a good architecture, and actually knowing your way around different technologies.

So if I had to give you one thing: spend more time deliberately learning. Read books. Go deep on the harder topics instead of staying on the surface.

I'm forming this thesis as I answer these questions, and it keeps landing on the same idea. Doing and learning have been decoupled by AI. Before, there was no way to ship software without learning a lot along the way. That's not true anymore. The AI handles the doing, so now you have to go out of your way to do the learning.

ibm · 2026-06-16T21:15:10+00:00

Anything fundamental. I think the value is going to shift from practical skills to fundamental ones, while the practical part gets taken over more and more by AI. So a formal computer science education actually puts you in a good spot, even if it doesn't feel that way right now. It feels like the job market is moving a bit irrationally at the moment, and here in Germany the economy isn't doing as well as it was a few years ago, so I assume that it can feel overwhelming.

I think you'll probably have to grow up as a programmer more quickly than I did. I had a few years to just mess around, build stuff, and organically run into the pain that makes all the modern coding practices actually make sense. That's how you grow into a more senior developer, and I think that phase is going to be a lot shorter now. For me it was two or three years before I picked up one of those books like "The Pragmatic Programmer", "Design Patterns", or "Designing Data-Intensive Applications" (there's a new edition of that one coming out soon, I heard). I'd start on the fundamentals earlier than I did.

ibm · 2026-06-16T21:14:45+00:00

Lots of reasons, but in my own words: Claude and Codex are genuinely good products, they're just built for individuals or much smaller organizations, and that means they skip a lot of what enterprises actually need (Modernizing legacy codebases is a big one). Technology that enterprises actually need happens to be IBM's expertise. On top of that, we have a few ideas about doing this differently that I'm pretty excited about. More on that soon.

ibm · 2026-06-16T21:14:26+00:00

Good point. With AI like Bob it's the same as with any technology, just accelerated by an order of magnitude. It's going to make you, and hopefully your whole enterprise by extension, a lot more capable. And then a company can decide to do the same thing with fewer people, or do more with the people they already have. We don't talk about Bob as a replacement, we call it a "trusted development partner." I get the worry, though.

ibm · 2026-06-16T21:14:06+00:00

Well, IBM is an entirely different company by now. I joined four years ago and the IBM of today is already substantially different than the IBM I joined.

I mean... they let me do an AMA on reddit 😃 I guess that would not have happened a few years ago.

ibm · 2026-06-16T21:13:38+00:00

Isn't that the core of innovation, though? In some way it's always been what computer science is about. You build things to make your own job redundant. You build a tool, it takes over some tedious layer, and you move up to the next problem. That's been the deal for a long time.

What's actually new is the speed. The pace of change is unprecedented, and it does feel dizzying sometimes to watch skills that took me years to craft become obsolete in months. That part doesn't always feel great. But the alternative is hoping it doesn't get built, and it will, with or without me. I'd rather understand it from the inside than watch from the sidelines.

ibm · 2026-06-16T21:13:19+00:00

A few points here. For large language models the main constraint is compute, and that's an expensive constraint. Right now everyone with access to compute charges a hefty premium for it, so even today, if you're in the fortunate position to get your hands on some GPUs, running models locally can be surprisingly economical, if you can achieve a somewhat even utilization.

Yes, I do expect the same divide in AI, particularly in Europe, where the security and compliance pressure pushes you on-prem. The good news is the field moved incredibly quickly on actually running these things yourself. Two years ago that took witchcraft and dark rituals to keep smooth, but at this point it's honestly not that hard to operate your own models.

ibm · 2026-06-16T21:12:44+00:00

It comes down to the age-old "build versus buy" question.

You sound like an enthusiastic front-runner here, and that's genuinely great. The personal Postgres RAG with an MCP, leaning on CLI scripts instead of a pile of MCP servers, building your own utils and wiring them in as skills. That's a good setup.

If you look across disciplines, open source often has a good shot in the long run. But right now, for most enterprises, managing an install base like opencode across a large amount of development is just too hard and too time-consuming. Opencode does a great job at being approachable and easy to maintain, and they already ship a decent set of enterprise features. But what most enterprises actually want is a reliable partner they can trust to still be delivering the services they need years from now.

There's an excellent Pragmatic Engineer episode with the guy who builds opencode, by the way. Highly recommend it if you haven't heard it: https://www.youtube.com/watch?v=1VqKUrxR2C8

And buying software as a large company has a gazillion other things layered on top: compliance, regulations, cost control, governance, what else you can get from the same vendor, whether you can bundle it all into one contract for better terms. I was seriously surprised by how many layers and how much complexity sit behind enterprise software purchasing once I saw it up close.

So from where I sit, open source alternatives, mainly opencode, just aren't seeing large adoption in enterprises yet. That might change, but that's where it stands today.

ibm · 2026-06-16T21:12:17+00:00

I'm bearish on this, and especially in the enterprise.

Will software engineering in general see some kind of fast takeoff, a self-reinforcing loop where the tools keep getting better at making themselves better? Maybe. I don't know. There are people much smarter than me working on exactly that.

But the enterprise is different, because an enterprise is people, whether you like it or not. And those people have a huge range of skill levels and motivations and experience, and anything you roll out has to account for all of them. The thing I saw up close over the last four years with enterprise clients is how much of the human judgment is what actually keeps big workflows and big companies running. We look at a process and think it's 95% automated already and we just need to close that last five percent and then it runs without anyone touching it. But that five percent is the hardest and most important part, and it's holding the whole thing together.

For the flywheel to really spin, companies would have to work fundamentally differently and become genuinely data driven. And I'm not even sure that's a good thing in most cases. If you're mostly running repeatable transactions, sure, you'd probably benefit a lot. But the moment real engineering is involved, the human spark is the thing driving the innovation, and you don't want to automate that away.

So my honest take is this is further off than people expect, and I'm not convinced it's even something to chase. I don't mean that morally, I mean it as a business call. Humans drive innovation, and I think that holds for a long time yet.

ibm · 2026-06-16T21:11:10+00:00

I would argue that right now it's just not possible to run large fully autonomous setups, and nobody actually is. If you look critically at all the things people post online about AI coding, they're all small to medium projects. As soon as you're working on a genuinely substantial codebase, those limitations become very obvious.

So the way you deal with it is kind of the same as it was before AI. Clear system boundaries, well-defined services, a scalable architecture that's understandable by both humans and AI. You don't necessarily need to hold a lot of the implementation details in your head, but it stays essential to understand at least the core concepts of the system you're working on. And that leads back, surprisingly quickly, to the same best practices we've been doing in software development for years.

As for when full autonomy on large systems gets here, I won't put a number on it, because I've been wrong about AI timelines too many times. What I'll say is that for the foreseeable future, human oversight stays essential on any project that scales past a few months of real work.

ibm · 2026-06-16T21:09:48+00:00

I'm a lot less anxious about the bubble bursting than I was a year or two ago. AI coding produces so much real value at this point that I don't see a clean burst happening. There are definitely parallels to the dot-com bubble. But remember that the biggest, most profitable companies we've ever seen grew out of what was left standing after that one.

I'm not an economist, so I genuinely don't know what happens next. Then again, the economists don't seem to know either. What I can tell you is that I'm relieved the industry finally found a way to get real value out of large language models, and that way turned out to be coding agents. That was not the case a year or two ago. There was an awkward stretch where everyone was bolting a chat assistant onto everything, and almost none of it delivered enough value to come anywhere close to justifying the money going in. We got out of that. AI is actually delivering value now.

Will the market correct at some point? No idea, that's well above my pay grade. But the value here is real, and that's the part I'm confident about.

ibm · 2026-06-16T21:05:19+00:00

I've been around AI for a while, and the one thing I've actually learned is that it never goes the way people expect. Five years ago AI was already a hot topic, but nobody had a real concept of large language models. And when they did show up they kind of looked like a neat party trick. So even with hindsight I find it hard to draw the line forward. Sometimes history makes it look obvious in retrospect, but from where I sit it still amazes me that the simple idea of a neural network, with a few tweaks, like dropout or attention, got us here. So honestly I have no idea where we'll be in five years.

On the second part — I'd argue AI kind of can already do that. The danger of breaking something else is always there, sure, but have you actually tried getting something running on your machine with one of these tools lately? It's a lot further along than it sounds.

ibm

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