If you believe this, and you have not studied quantum chemistry, I invite you to consider as to how you could possibly be sure about this. This is a mathematical question. There is a hard, mathematical limit to the accuracy that can be achieved in finite time.
Doesn’t the existence of AlphaFold basically invalidate this? The exact same problems you describe for band-gap computation exist for protein folding: the underlying true equations that need to be solved are monstrously complicated in both cases, and previous approximate models made by humans aren’t that accurate in both cases… yet this didn’t prevent AlphaFold from destroying previous attempts made by humans by just using a lot of protein structure data and the magic generalisation power of deep networks. This tells me that there’s a lot of performance to be gained in clever approximations to quantum mechanical problems.
I think there’s a real sense in which the band gap problem is genuinely more quantum-mechanical in nature than the protein folding problem. It’s very common that people will model proteins with a classical approximation, where you assume that eg. each bond has a specific level of stiffness, etc. (Often these values themselves are calculated using density functional theory.) But even given this classical approximation, many proteins take so long to settle into a folded configuration that simulating them is very expensive.
Also, last time I looked in any detail, the current version of Alpha Fold did use multiple sequence alignment, which means that some of its utility comes from the fact that it’s predicting evolved sequences, and so generalization to synthetic sequences might be iffy.
In the same sense you could say this is exactly the same. For any classical computer:
-protein folding is intractable in general, then whatever natural selection found must constitute special cases that are tractable, and that’s most probably what alphafold found. This was extraordinary cool, but that doesn’t mean alphafold solved protein folding in general. Even nature can get prions wrong.
-quantum computing is intractable in general, but one can find special cases that are actually tractable, or where good approximations is all you need, and that what occupy most of physicists time.
In other words, you can expect a superintelligence to find marvelous pieces of science, or to kill everyone with classical guns, or to kill everyone with techs that looks like magic, but it won’t actually break RSA, for the same reason it won’t beat you at tic-tac-toe: superintelligences won’t beat math.
In a literal sense, of course it doesn’t invalidate it. It just proves that the mathematical limit of accuracy was higher than we thought it was for the particular problem of protein folding. In general, you should not expect two different problems in two different domains to have the same difficulty, without a good reason to (like that they’re solving the same equation on the same scale). Note that Alphafold is extremely extremely impressive, but by no means perfect. We’re talking accuracies of 90%, not 99.9%, similar to DFT. It is an open question as to how much better it can get.
However, the idea that perhaps machine learning techniques can push bandgap modelling further in the same way that alphafold did is a reasonable one. Currently, from my knowledge of the field, it’s not looking likely, although of course that could change . At the last big conference I did see some impressive results for molecular dynamics, but not for atom scale modelling. The professors I have talked to have been fairly dismissive of the idea. I think there’s definitely room for clever, modest improvements, but I don’t think it would change the overall picture.
If I had to guess the difference between the problems I would say I don’t think the equations for protein folding were “known” in quite the way the equations for solving the Schrodinger equation were. We know the exact equation that governs where an electron has to go, but the folding of proteins is an emergent property at a large scale, so I assume they had to work out the “rules” of folding semi-empirically using human heuristics, which is inherently easier to beat.
Doesn’t the existence of AlphaFold basically invalidate this? The exact same problems you describe for band-gap computation exist for protein folding: the underlying true equations that need to be solved are monstrously complicated in both cases, and previous approximate models made by humans aren’t that accurate in both cases… yet this didn’t prevent AlphaFold from destroying previous attempts made by humans by just using a lot of protein structure data and the magic generalisation power of deep networks. This tells me that there’s a lot of performance to be gained in clever approximations to quantum mechanical problems.
I think there’s a real sense in which the band gap problem is genuinely more quantum-mechanical in nature than the protein folding problem. It’s very common that people will model proteins with a classical approximation, where you assume that eg. each bond has a specific level of stiffness, etc. (Often these values themselves are calculated using density functional theory.) But even given this classical approximation, many proteins take so long to settle into a folded configuration that simulating them is very expensive.
Also, last time I looked in any detail, the current version of Alpha Fold did use multiple sequence alignment, which means that some of its utility comes from the fact that it’s predicting evolved sequences, and so generalization to synthetic sequences might be iffy.
In the same sense you could say this is exactly the same. For any classical computer:
-protein folding is intractable in general, then whatever natural selection found must constitute special cases that are tractable, and that’s most probably what alphafold found. This was extraordinary cool, but that doesn’t mean alphafold solved protein folding in general. Even nature can get prions wrong.
-quantum computing is intractable in general, but one can find special cases that are actually tractable, or where good approximations is all you need, and that what occupy most of physicists time.
In other words, you can expect a superintelligence to find marvelous pieces of science, or to kill everyone with classical guns, or to kill everyone with techs that looks like magic, but it won’t actually break RSA, for the same reason it won’t beat you at tic-tac-toe: superintelligences won’t beat math.
In a literal sense, of course it doesn’t invalidate it. It just proves that the mathematical limit of accuracy was higher than we thought it was for the particular problem of protein folding. In general, you should not expect two different problems in two different domains to have the same difficulty, without a good reason to (like that they’re solving the same equation on the same scale). Note that Alphafold is extremely extremely impressive, but by no means perfect. We’re talking accuracies of 90%, not 99.9%, similar to DFT. It is an open question as to how much better it can get.
However, the idea that perhaps machine learning techniques can push bandgap modelling further in the same way that alphafold did is a reasonable one. Currently, from my knowledge of the field, it’s not looking likely, although of course that could change . At the last big conference I did see some impressive results for molecular dynamics, but not for atom scale modelling. The professors I have talked to have been fairly dismissive of the idea. I think there’s definitely room for clever, modest improvements, but I don’t think it would change the overall picture.
If I had to guess the difference between the problems I would say I don’t think the equations for protein folding were “known” in quite the way the equations for solving the Schrodinger equation were. We know the exact equation that governs where an electron has to go, but the folding of proteins is an emergent property at a large scale, so I assume they had to work out the “rules” of folding semi-empirically using human heuristics, which is inherently easier to beat.
Do you have a name/link for that conference? I’d be interested in reading those molecular dynamics papers.