It Is true! Now you just have to convince them to have dinner together and spend the night together.

I remember suse making quite a few back in the days, some were pretty nice.

Hello, thank you for the lead. I tried replying the other day but I had just woken up drunk in the campment of a homeless guy and I was not in the best conditions to go through the math.

I took a look at this worked example: https://dojo.qulacs.org/en/qp_main/notebooks/7.1_quantum_phase_estimation_detailed.html

As far as I understand they use the iterative approach because it requires less qbits and are able to decompose the eigenvalues of a Hamiltonian in more or less a single step.

If it is as I understand it this would be quite huge, since you’d be able to directly apply this to the Hartree-Fock equation or Density Functional Theory without having to come up with new ways to represent molecules.

One thing which appears quite critical is:

Prepare an initial state with sufficient overlap with the ground state

What does sufficient overlap mean? Could we take an AlphaFold model and that’s sufficient to then determine the ground state?

I guess this is easy with helium when you have 2 atoms, but when you have hundred of thousands it becomes a difficult task even to get to that point.

Moreover, I’m not exactly sure what they’re calculating: they plot an error; but it appears to be an error over the computed energy and not atom positions.

We already have reliable ways, and moderately fast, to compute the energy of a system. What we’re missing is a quick way to explore different spatial conformations of atoms to identify the one which leads to the lowest energy.

Another problem which I could not determine is whether the amount of required qbits scales with the dimensions of the molecular system. I suppose it does. In that case, could we estimate how many qbits would be required for a protein or at least a peptide?

As far as I understand it a billion qbit computer is not possible because the time to setup the system would be too long to retain coherence.

But maybe new techniques will be developed, who knows.

I did not yet see a single quantum algorithm able to tackle the protein folding problem.

Sure, quantum computers could be faster at solving graph related problems, but I did not see an approach able to reduce protein folding to a graph problem.

On the other hand neural networks have been successfully applied to the protein folding problem, and they do that quite quickly.

Not a perfect solution indeed, quantum computers may be much better at that; but I still do not see a theoretical framework which justifies claims as to the applicability of quantum computers to the protein folding problem.

Agreed, I still do hope they can maintain some of their promises. However until now, I have not really seen any real advances towards making something useful.

I do not have a deep knowledge of quantum computers, but I know plenty people working on them and often get to talk about it.

I know people working on chemical problems who are basically approximating atoms to point charges. And either way those calculations are slower than on a CPU. For the uninformed, in chemistry the interactions between electronic orbitals is fundamental; this is in no way an approximation useful to obtain any kind of information.

This is fine, I understand methodologies take time to develop; however as far as I understand it those techniques they’re using are mathematically limited to using point charges: no matter how much they improve them that’ll be the highest level of accuracy.

I hope someone finds a way to handle such things better: as much as you can make a great machine learning model you’re always depending on available data.

Yes, X-ray is the gold standard. Technology has advanced in the sense that the protein crystallization is now more standardized and automated, as well as the analysis of the results.

It is not the only technique, for example there are cheaper ones based on mass spectrometry which do not resolve the full structure but allow to understand which amino acids are spatially near; such information is useful when developing a protein model and to validate whether a model is plausible.

The other two major techniques for structure resolution are NMR spectra analysis and the fairly novel technique of cryo electro microscopy.

These in general do not resolve the protein structure to the same resolution as X-ray but have other advantages: they allow you to observe the protein structure when in solution, which may be significantly different from the crystallized structure.

I doubt there is a comparable correctness metric between LLMs and protein structure prediction models.

You can measure how many times they correctly predict a thing, but results will greatly change according to what your objective is. Those are only comparable when you’re trying to predict the same thing.

As such my reply would be: sometimes more incorrect sometimes more correct. However, in general, a mishandled incorrect protein structure prediction is way more expensive than an LLM hallucination.

The interesting part would be to a reliable and computationally accessible way to handle disordered proteins. Seeing how they can move could be quite revolutionary.

I had to work on some disordered proteins and you’re pretty much just guessing, plausibly you’re better off going to a casino blindfolded and play blackjack.

AlphaFold is incorrect in plenty occasions.

Not solved, no. Definitely much much better than before. The difference AlphaFold made is significant: we’re talking about getting a decent model in a couple minutes using a PC compared to several months of calculations before.

However we still need experimental data: in many occasions AlphaFold gives an incorrect model. With some experimental data that model can be improved, but we still have no reliable way to know what the structure of a protein is starting from the amino acidic sequence without extensive experimentation.

That’s the big promise of quantum computers, there are however two major problems in my opinion:

1. There’s still no theoretical framework which explains how once we have a quantum computer we may tackle protein folding
2. Plenty quantum computing companies closed shortly after AlphaFold was published since they lost all funding because protein folding was “solved”

~100x it’s a logarithmic scale

Just prepare the sauce before you cook the pasta, that way you’re sure.

It’s better to cook the pasta together with the sauce for a minute or two.

Just add an appropriate amount of water to you pot.

Place a wooden spoon over the top of the pot, the foam will not boil over.

Use plenty water. If they stick together with the advised amount, use more water. Doing that won’t change the taste.

Regarding the boiling of water, it is not required. I believe pasta needs about 80°C to cook. If you have enough water and cover the pot with a lid you can turn off the fire and it will cook.

You have at most 15 seconds to thoroughly mix your pasta with the sauce.

Nobody in their right mind would ever let pasta sit without sauce.

Once I had to watch while some people strained the pasta, let it there and then started chatting about something else. They were wondering about why I was going crazy and then they complained the pasta stuck together…

Please, don’t let your pasta sit unattended. Also, Barilla is not a particularly good brand of pasta.

I imagined, basically no sci-hub alternatives. Anna’s archive is just serving the sci-hub articles.

Tldr?

So much unpublished work. They should have published it and retracted it the following day, just to prove they did the work.