Researchers optimize simulations of molecules on quantum computers
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Then there's potential quantum algorithms for protein folding. These folds are astronomically complex and can benefit from quantum simulations.
"Problems that are undecidable using classical computers remain undecidable using quantum computers.[4]: 127 What makes quantum algorithms interesting is that they might be able to solve some problems faster than classical algorithms because the quantum superposition and quantum entanglement that quantum algorithms exploit generally cannot be efficiently simulated on classical computers (see Quantum supremacy)."
"Problems that are undecidable using classical computers remain undecidable using quantum computers.[4]: 127 What makes quantum algorithms interesting is that they might be able to solve some problems faster than classical algorithms because the quantum superposition and quantum entanglement that quantum algorithms exploit generally cannot be efficiently simulated on classical computers (see Quantum supremacy)."
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In many cases quantum computers seem to be closer to physical models than number crunchers. Computer almost seems like a misnomer.
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We've been on the cusp of quantum computers being useful to simulate quantum systems now for almost 10 years. Even moving the goalposts all the way over in quantum computer's favor the technology still seems unsuitable. I still have a hard time understanding so many people being enamored with this technology.
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You know the article is going to get into the heavy stuff when it starts off with the premise that catalyst behavior is "easier" and "simple".
edit: on having now read the full article, I'd like to complement John on making a fairly complex subject accessible and interesting for me to read.
edit: on having now read the full article, I'd like to complement John on making a fairly complex subject accessible and interesting for me to read.
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Great write up Dr Timmer. I simultaneously definitely/kinda/almost/didn't understood it. Which is orders of magnitude better than my understanding of most quantum experiments. (Except the horror that was the 1980's VW vehicle, I understood those were to be avoided, unless you got paid, like the folks at the repair shop in Corvallis, named "The Quantum Mechanics")
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Let me define this for everyone: "not for at least ten years".
I am a PhD computational chemist working in the field at a company that is actively developing new methods, so I know a little bit about this. Quantum computing is far from useful in this field, and there are still many massive hurdles it needs to clear before it becomes viable. Even then, it will be far from cost competitive. These results are interesting from an academic perspective, but have no practical relevance. Every quantum computing for chemistry paper I have read either runs models that have no practical relevance for practitioners in this field, or uses massive amounts of computational power to achieve something I can do on my laptop.
Furthermore, the massive improvements we have recently seen in this space due to the advent of improved semiempirical methods (e.g. xTB), improved local methods (e.g. DLPNO, and recently DLPNO-CCSDT(Q)), and neural network potentials (NNPs) means that the goalposts are quickly moving further away from what quantum computing. What used to take a day to run with traditional models (e.g. DFT) can be run in seconds on a laptop with NNPs. For those who are interested in learning more about what is actually useful for most chemists, I recommend:
- AIMNet2 - a really good NNP
- A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems - a great overview of how these models work
- An Introduction to Neural Network Potentials - an overview of state of the art NNPs (from my company)
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That is because this is actually new technology. Not new applications of old technology, not a logical conclusion to a technology trend, but something that requires fundamental scientific research into new phenomena and techniques. Takes time, and there are always blind canyons in that kind of R&D.
Realistically, 10 years is nothing. But the technology industry has taught us to think in 'new phone every year' kinds of time frames.
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Historically, there are a number of machines that blur the line between physical model and calculator. I remember reading about an economics calculator that used water pressure and flow to predict the economic behavior of markets.
Quantum computing seems to be similar to those machines in a particular way: They're not arbitrary problem solvers, but if you happen to find a problem where the behaviors are similar to the parts of a quantum computer, you can make a surprisingly effective problem solver.
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"The researchers then used this approach to explore a chemical, Mn4O5Ca, that plays a key role in photosynthesis."
Not that I don't think plants are important but if you're modeling catalysts wouldn't it be easier to obtain research money for studying catalysts used in hydrogen production or battery materials?
Not that I don't think plants are important but if you're modeling catalysts wouldn't it be easier to obtain research money for studying catalysts used in hydrogen production or battery materials?
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So, your saying, a lot like many of the LLM/AI academic papers (from a researcher in that field).
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“We're not quite ready to run this system on today's quantum computers, as the error rates are still >a< bit too high.”
Only one?
Only one?
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Great article. It is interesting to see that there may be a different branch of "quantum algorithms" to be run on quantum computers. I recall something similar to this to simulate the quantum state of a hydrogen atom a while back.
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Yes, they're more like analog computers or physical emulators than digital computers.
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That molecule that the article mentions, shouldn't it be written as MN4CaO5 instead?
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