I mean… modern CPUs can do multiple billions of summations and multiplications in a single second (let alone GPUs), and yet protein folding can take a long time.
A quantum computer is MUCH faster for finding viable solutions, though they’re MUCH harder to set up even for individual problems, and still need verification of the results.
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.
Ehhhhhh that’s purely a problem of the “setting up” a quantum computer (ok ok and how many qubits are involved). They SHOULD be extremely capable of it. Literally able to get a ‘correct’ answer in one cycle.
It’s just very unfortunate that quantum computers benefit greatly from slower cycles, and the setup of the computations are a real nightmare. As you say, I’m unaware of any that can solve protein folding in a single cycle, and the more cycles there are involved, the more subject it is to the algorithm implemented and setup steps taken.
Maybe one day, we’ll have billion-qubit quantum computers solving all sorts of problems reliably, but that day is definitely not today!
I still do not see a theoretical framework which justifies claims as to the applicability of quantum computers to the protein folding problem.
The quantum phase estimation (QPE) algorithm which calculates ground states of molecules is theoretically predicted to have exponential speedup over classical methods on unstructured problems. Protein folding is a ground state problem.
Not saying it will be best in practice, but that’s at least a theoretical framework.
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.
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?
Because checking a billion proteins for the shape we want is a hardware problem, and the hardware for doing that is very slow and expensive. If we can fold proteins using software, we can try billions of proteins far faster and cheaper. We could probably work backwards too, reducing the search space from billions to millions.
I mean… modern CPUs can do multiple billions of summations and multiplications in a single second (let alone GPUs), and yet protein folding can take a long time.
A quantum computer is MUCH faster for finding viable solutions, though they’re MUCH harder to set up even for individual problems, and still need verification of the results.
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.
Ehhhhhh that’s purely a problem of the “setting up” a quantum computer (ok ok and how many qubits are involved). They SHOULD be extremely capable of it. Literally able to get a ‘correct’ answer in one cycle.
It’s just very unfortunate that quantum computers benefit greatly from slower cycles, and the setup of the computations are a real nightmare. As you say, I’m unaware of any that can solve protein folding in a single cycle, and the more cycles there are involved, the more subject it is to the algorithm implemented and setup steps taken.
Maybe one day, we’ll have billion-qubit quantum computers solving all sorts of problems reliably, but that day is definitely not today!
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.
The quantum phase estimation (QPE) algorithm which calculates ground states of molecules is theoretically predicted to have exponential speedup over classical methods on unstructured problems. Protein folding is a ground state problem.
Not saying it will be best in practice, but that’s at least a theoretical framework.
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:
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?
Why are we protein folding? The largest quantum computer — the Universe — already solved it because we’re standing.
Do you recommend we go back to trial and error on humans, then? Do you want to volunteer as a guinea pig? If not, then STFU.
Because checking a billion proteins for the shape we want is a hardware problem, and the hardware for doing that is very slow and expensive. If we can fold proteins using software, we can try billions of proteins far faster and cheaper. We could probably work backwards too, reducing the search space from billions to millions.