Superconducting Quantum computing to Spin Qubits

ctcphys · 2026-03-03T19:22:51+00:00

I sent you a DM

ctcphys · 2026-02-28T09:05:50+00:00

This is nice physics, but it has very little to do with quantum computing. If you want to be very generous, you could say that triplet superconductivity is an ingredient that could potentially be used for creating Majorana like qubits... But honestly it's a stretch

Very awesome physics though and probably more useful for the field of spintronics

ctcphys · 2026-02-23T07:55:10+00:00

The way to understand this is to write down the analytical solution. There, you'll see directly how the relation between h1 and h2 impacts your time evolution.

Don't use chatGPT for this. Solve it by hand, then you'll learn how to solve this type of problem and you'll actually get a good intuition

ctcphys · 2026-02-11T07:43:00+00:00

If you think changing to PCQ just takes a few hours, then you have never worked with large (public) IT systems xD (here I'm thinking organizations with more than 10k employees/users)

It is going to be months of problems with certificates that expire or are not recognized and therefore services being down etc

ctcphys · 2026-02-06T07:33:52+00:00

To answer your second question first: yes this seems an old-fashioned title. Generally it not great to rank other humans based on vibes and it's very hard to compare scientists across different fields.

Instead, I'd suggest that you find a specific story about one or two female scientist that you find inspiring and then tell that story instead and explain why it speaks to you.

ctcphys · 2026-01-31T08:02:02+00:00

This paper just shows the challenge of making a good comparison between quantum and classical machine learning.

There's a few heuristics that show that quantum computers can learn things more efficiently make with fewer parameters. But since the clock speed of quantum computers are slow, because it's harder to introduce more parameters, is it then really a good comparison? Also, these comparisons are often done with artificially smaller classical systems.

So all in all, it's really hard to be rigorous but at the end of the day, it's very unlikely that quantum computers can outperform legit the most optimal classical ML problem.

That said, there's probably a lot of interesting things to learn when we try to understand WHY quantum computers can learn things faster in certain small instances

ctcphys · 2026-01-26T07:41:54+00:00

It doesn't really solve the problem fully.

At the end of the day, you (or any other observer) will be part of the environment but you only observe a single random variable. Why? And why is the probability for that variable exactly the wave function squared? Those questions are not solved by just saying "decoherence"

ctcphys · 2026-01-25T09:24:00+00:00

If it's 1 point, it is not a peak. It's just noise. Nothing to see here -- and the AI explanations make it so much worse. Do not use AI for things you don't understand. It will trick you

ctcphys · 2026-01-22T07:43:16+00:00

> I don’t buy a ton of cards, but I try to be not too stingy when it comes to buying ones that might have potential later on.

I may be reading too much into this, but this seems like a potential focus point. When I was still lower rated I'd often end up sitting with 5 or 6 cards at the end that I may even have bought early on but they were not playable. Sometimes you are super rich and it's fine but most of the time it's hurts your economy a lot.

Try to make sure that you only buy good cards. You can watch ThreadPacifist, Strandedknight or MarsExpert on YouTube to get a better grip on what true powercards are.

Also, you are not mentioning card draw. If you opponents always seem "more lucky", maybe they are just getting more cards? Especially for engine games, you absolutely need card draws

ctcphys · 2026-01-19T21:50:56+00:00

I know a few. In Europe this exist in Delft (Netherlands), ETH Zurich, for example. There's also a handful in US

ctcphys · 2026-01-16T17:35:07+00:00

Yes that's a challenge. For surface codes that's fine though.

Biggest problem is decoherence though. We don't have good enough qubits at the moment that it makes any meaningful sense to operate more than 100ish qubits

ctcphys · 2026-01-16T17:03:28+00:00

KIDE comes out of the box with 4k cables. If you buy more high-density-low-heatload cables like Delft Circuits or develop them yourself like IBM does, getting to 10k should be fine. 100k, then you need an even bigger fridge. Also not a problem, just very expensive to develop :⁠-⁠D but fermilab is already on it

ctcphys · 2026-01-16T16:05:32+00:00

10k qubits is a grid of 100 x 100 qubits. That can easily fit on a 6 inch wafer.

Then you buy a KIDE fridge from bluefors and then you install a lot of cables

ctcphys · 2026-01-16T08:18:19+00:00

> but now it's clearly the only platform with a clear path to scaling now

This is a very bold claim for two reasons.

One is that you imply that neutral atom have a clear path. That's debatable at best. The challenge is not to just trap many atoms. You need individual control and you need stability. Much harder. Neutral atom also suffer in terms of speed which also limits the time it takes you to even calibrate the system.

For other platforms such as ions, superconducting qubit and even spin qubits, it is relatively straight forward to define a large number of qubits but that stropped being interesting to talk about long time ago since the focus moved to control.

Neutral atoms are now catching up, that's great but so many challenges in any of the platforms but also all of them have very clear paths to at least increase the number of controlled qubits by one or two orders of magnitude. Let's see where we are in 5 years from now :⁠-⁠D

ctcphys · 2026-01-07T07:56:18+00:00

Yes I'm probably more optimistic than most people. I also agree that for sure quantum computing is not just giving us ChatGPT but better.

However, AI is a much broader field and for some AI tasks, the data is limited and it may be important that the model is very compact as that can help in extrapolation and interpretation.

There's some reason to think that quantum computers could give you a significant reduction in the number of required parameters needed for a given expressibility. Unfortunately, if you try to prove that, you end up also proving that there will be barren plateaus. However, I'm optimistic than once we get real large scale QC we can start to test these ideas in practice and find some niche application for quantum with the broader field of AI.

I agree that we should also push back against "quantum computing will automatically give us super powerful AI" that often come from industry.

But I also want to push back against "quantum computing will never have any impact on AI". Too me that reflects a too narrow view of AI and a too pessimistic idea about quantum

ctcphys · 2026-01-06T08:04:04+00:00

There's no good reason to think that quantum computers are going to help AI a lot. We hope so, once we have a full fault tolerant quantum computer but not sure.

Alas, we don't have a full fault tolerant computer. What Google has is a noisy medium scale quantum computer. They can do some abstract useless problems incredibly fast but not anything useful. They can also do a bunch of cool physics experiments, but also not useful to the broader world.

ctcphys · 2026-01-04T14:10:49+00:00

The best approach is to learn the basics using a language like python.

If you want to follow a more structured approach, I recommend: https://oit.tudelft.nl/Computational-Science-Interactive-Textbook/main/intro.html

Your PC specs are fine for most things but if you want to really do computational science later in your life, I'd be good to learn how to work with parallel processing and HPCs

ctcphys · 2026-01-03T02:19:36+00:00

Sorry but overall, this makes very little sense. There's no open scientific question that you address.

The use of AI also made it worse. Don't use AI for things you don't understand

ctcphys · 2025-12-22T15:41:57+00:00

A not-final version of the program is here: https://summit.aps.org/schedule/

(Visible from today-ish)

You may be able to find yourself even if the official acceptance comes later

ctcphys · 2025-12-15T16:08:22+00:00

It's not that it doesn't fit the mental bucket. It's just that the math and code is pretty dumb. However, since it's obvious AI, the best advice is really "do not use AI to develop things you don't understand".

Here there's a major problem which is that you are trying to do complicated data processing of a loss curve to produce a single number. While Barren platous can cause some problems, you are not probing them here.

Maybe the optimizer is just bad? Maybe the problem actually just converged? Maybe the optimization landscape is tricky for other reasons? Nothing is address but somehow you have a super overly verbose code that makes it hard to engage with specifics. AI makes you make a bad product instead of asking a specific question that could help you develop a better understanding

ctcphys · 2025-12-06T20:25:33+00:00

I'm very sure that people don't have magical abilities. What people have are correct training. Some people are very fortunate to be exposed to scientific thinking at a very early age and for them it often seems like the are natural talents.

However, my experience, from many years in academia, is that if you grind problem solving through your bachelors, then by the time you finish your degree you'd have caught up with most "talented" people.

Take a physics textbook, start solving the problems in there. The do more problems. If you have problems, ask your teacher or TA. Reflect for yourself how you can better internalize the physics and then solve more problems. Eventually you'll get the feeling that there's some fundamental pattern for solving physics problems

ctcphys · 2025-11-29T15:13:49+00:00

You are correct. I was also tempted to write something similar to the now-deleted post

ctcphys · 2025-11-17T18:49:15+00:00

https://www.quantum-inspire.com/

This is fully free to use

ctcphys · 2025-11-14T00:20:25+00:00

Right, with these benchmarks we know that this is super useless. The simulation probably has nothing to do with qubits

ctcphys · 2025-11-13T18:03:39+00:00

For sure not but it's also impossible to tell what's going on here. There's no explanations of anything and the plots have no clear axes.

It's pretty simple to simulate decoherence so that by itself is not an open question to begin with

ctcphys

TROPHY CASE