Proteins and other chemical interactions are governed by quantum mechanics, so the AGI would probably need a quantum computer to do a faithful simulation. And that’s for a single, local interaction of chemicals; for a larger system, there are too many particles to simulate, so some systems will be as unpredictable as the weather in 3 weeks.
The distribution of outcomes is much more achievable and much more useful than determining the one true way some specific thing will evolve. Like, it’s actually in-principle achievable, unlike making a specific pointlike prediction of where a molecular ensemble is going to be given a starting configuration (QM dependency? Not merely a matter of chaos). And it’s actually useful, in that it shows which configurations have tightly distributed outcomes and which don’t, unlike that specific pointlike prediction.
What does “the distribution of outcomes” mean? I feel like you’re just not understanding the issue.
The interaction of chemical A with chemical B might always lead to chemical C; the distribution might be a fixed point there. Yet you may need a quantum computer to tell you what chemical C is. If you just go “well I don’t know what chemical it’s gonna be, but I have a Bayesian probability distribution over all possible chemicals, so everything is fine”, then you are in fact simulating the world extremely poorly. So poorly, in fact, that it’s highly unlikely you’ll be able to design complex machines. You cannot build a machine out of building blocks you don’t understand.
Maybe the problem is that you don’t understand the computational complexity of quantum effects? Using a classical computer, it is not possible to efficiently calculate the “distribution of outcomes” of a quantum process. (Not the true distribution, anyway; you could always make up a different distribution and call it your Bayesian belief, but this borders on the tautological.)
Not am expert at all here, so please correct me if I am wrong, but I think that quantum systems are routinely simulated with non quantum computers. Nothing to argue against the second part
You are correct (QM-based simulation of materials is what I do). The caveat is that exact simulations are so slow that they are impossible, that would not be the case with quantum computing I think. Fortunately, we have different levels of approximation for different purposes that work quite well. And you can use QM results to fit faster atomistic potentials.
You are wrong in the general case—quantum systems cannot are are not routinely simulated with non-quantum computers.
Of course, since all of the world is quantum, you are right that many systems can be simulated classically (e.g. classical computers are technically “quantum” because the entire world is technically quantum). But on the nano level, the quantum effects do tend to dominate.
IIRC some well-known examples where we don’t know how to simulate anything (due to quantum effects) are the search for a better catalyst in nitrogen fixation and the search for room-temperature superconductors. For both of these, humanity has basically gone “welp, these are quantum effects, I guess we’re just trying random chemicals now”. I think that’s also the basic story for the design of efficient photovoltaic cells.
Simulating a quantum computer on a classical one does indeed require a phenomenal amount of resources when no approximations are made and noise is not considered. Exact algorithms for simulating quantum computers require time or memory that grows exponentially with the number of qubits or other physical resources. Here, we discuss a class of algorithms that has attracted little attention in the context of quantum-computing simulations. These algorithms use quantum state compression and are exponentially faster than their exact counterparts. In return, they possess a finite fidelity (or finite compression rate, similar to that in image compression), very much as in a real quantum computer.
This paper is about simulating current (very weak, very noisy) quantum computers using (large, powerful) classical computers. It arguably improves the state of the art for this task.
Virtually no expert believes you can efficiently simulate actual quantum systems (even approximately) using a classical computer. There are some billon-dollar bounties on this (e.g. if you could simulate any quantum system of your choice, you could run Shor’s algorithm, break RSA, break the signature scheme of bitcoin, and steal arbitrarily many bitcoins).
It remains to be seen whether it’s easier. It could also be harder (the nanobot interacts with a chaotic environment which is hard to predict).
“Also a big unsolved problem like P=NP might be easy to an ASI.”
I don’t understand what this means. The ASI may indeed be good at proving that P does not equal NP, in which case it has successfully proven that it itself cannot do certain tasks (the NP complete ones). Similarly, if the ASI is really good at complexity theory, it could prove that BQP is not equal to BPP, at which point is has proven that it itself cannot simulate quantum computation on a classical computer. But that still does not let it simulate quantum computation on a classical computer!
The reason for the exponential term is that a quantum computer uses a superposition of exponentially many states. A well functioning nanomachine doesn’t need to be in a superposition of exponentially many states.
For that matter, the AI can make its first nanomachines using designs that are easy to reason about. This is a big hole in any complexity theory based argument. Complexity theory only applies in the worst case. The AI can actively optimize its designs to be easy to simulate.
Its possible the AI shows P!=NP, but also possible the AI shows P=NP, and finds a fast algorithm. Maybe the AI realizes that BQP=BPP.
Maybe the AI can make its first nanomachines easy to reason about… but maybe not. We humans cannot predict the outcome of even relatively simple chemical interactions (resorting to the lab to see what happens). That’s because these chemical interactions are governed by the laws of quantum mechanics, and yes, they involve superpositions of a large number of states.
“Its possible the AI shows P!=NP, but also possible the AI shows P=NP, and finds a fast algorithm. Maybe the AI realizes that BQP=BPP.”
It’s also possible the AI finds a way to break the second law of thermodynamics and to travel faster than light, if we’re just gonna make things up. (I have more confidence in P!=NP than in just about any phsyical law.) If we only have to fear an AI in a world where P=NP, then I’m personally not afraid.
Not sure why you are quite so confident P!=NP. But that doesn’t really matter.
Consider bond strength. Lets say the energy taken to break a C-C bond varies by ±5% based on all sorts of complicated considerations involving the surrounding chemical structure. An AI designing a nanomachine can just apply 10% more energy than needed.
A quantum computer doesn’t just have a superposition of many states, its a superposition carefully chosen such that all the pieces destructively and constructively interfere in exactly the right way. Not that the AI needs exact predictions anyway.
Also, the AI can cheat. As well as fundamental physics, it has access to a huge dataset of experiments conducted by humans. It doesn’t need to deduce everything from QED, it can deduce things from random chemistry papers too.
Proteins and other chemical interactions are governed by quantum mechanics, so the AGI would probably need a quantum computer to do a faithful simulation. And that’s for a single, local interaction of chemicals; for a larger system, there are too many particles to simulate, so some systems will be as unpredictable as the weather in 3 weeks.
The distribution of outcomes is much more achievable and much more useful than determining the one true way some specific thing will evolve. Like, it’s actually in-principle achievable, unlike making a specific pointlike prediction of where a molecular ensemble is going to be given a starting configuration (QM dependency? Not merely a matter of chaos). And it’s actually useful, in that it shows which configurations have tightly distributed outcomes and which don’t, unlike that specific pointlike prediction.
What does “the distribution of outcomes” mean? I feel like you’re just not understanding the issue.
The interaction of chemical A with chemical B might always lead to chemical C; the distribution might be a fixed point there. Yet you may need a quantum computer to tell you what chemical C is. If you just go “well I don’t know what chemical it’s gonna be, but I have a Bayesian probability distribution over all possible chemicals, so everything is fine”, then you are in fact simulating the world extremely poorly. So poorly, in fact, that it’s highly unlikely you’ll be able to design complex machines. You cannot build a machine out of building blocks you don’t understand.
Maybe the problem is that you don’t understand the computational complexity of quantum effects? Using a classical computer, it is not possible to efficiently calculate the “distribution of outcomes” of a quantum process. (Not the true distribution, anyway; you could always make up a different distribution and call it your Bayesian belief, but this borders on the tautological.)
Not am expert at all here, so please correct me if I am wrong, but I think that quantum systems are routinely simulated with non quantum computers. Nothing to argue against the second part
You are correct (QM-based simulation of materials is what I do). The caveat is that exact simulations are so slow that they are impossible, that would not be the case with quantum computing I think. Fortunately, we have different levels of approximation for different purposes that work quite well. And you can use QM results to fit faster atomistic potentials.
You are wrong in the general case—quantum systems cannot are are not routinely simulated with non-quantum computers.
Of course, since all of the world is quantum, you are right that many systems can be simulated classically (e.g. classical computers are technically “quantum” because the entire world is technically quantum). But on the nano level, the quantum effects do tend to dominate.
IIRC some well-known examples where we don’t know how to simulate anything (due to quantum effects) are the search for a better catalyst in nitrogen fixation and the search for room-temperature superconductors. For both of these, humanity has basically gone “welp, these are quantum effects, I guess we’re just trying random chemicals now”. I think that’s also the basic story for the design of efficient photovoltaic cells.
Quick search found this
This paper is about simulating current (very weak, very noisy) quantum computers using (large, powerful) classical computers. It arguably improves the state of the art for this task.
Virtually no expert believes you can efficiently simulate actual quantum systems (even approximately) using a classical computer. There are some billon-dollar bounties on this (e.g. if you could simulate any quantum system of your choice, you could run Shor’s algorithm, break RSA, break the signature scheme of bitcoin, and steal arbitrarily many bitcoins).
Simulating a nanobot is a lower bar than simulating a quantum computer. Also a big unsolved problem like P=NP might be easy to an ASI.
It remains to be seen whether it’s easier. It could also be harder (the nanobot interacts with a chaotic environment which is hard to predict).
“Also a big unsolved problem like P=NP might be easy to an ASI.”
I don’t understand what this means. The ASI may indeed be good at proving that P does not equal NP, in which case it has successfully proven that it itself cannot do certain tasks (the NP complete ones). Similarly, if the ASI is really good at complexity theory, it could prove that BQP is not equal to BPP, at which point is has proven that it itself cannot simulate quantum computation on a classical computer. But that still does not let it simulate quantum computation on a classical computer!
The reason for the exponential term is that a quantum computer uses a superposition of exponentially many states. A well functioning nanomachine doesn’t need to be in a superposition of exponentially many states.
For that matter, the AI can make its first nanomachines using designs that are easy to reason about. This is a big hole in any complexity theory based argument. Complexity theory only applies in the worst case. The AI can actively optimize its designs to be easy to simulate.
Its possible the AI shows P!=NP, but also possible the AI shows P=NP, and finds a fast algorithm. Maybe the AI realizes that BQP=BPP.
Maybe the AI can make its first nanomachines easy to reason about… but maybe not. We humans cannot predict the outcome of even relatively simple chemical interactions (resorting to the lab to see what happens). That’s because these chemical interactions are governed by the laws of quantum mechanics, and yes, they involve superpositions of a large number of states.
“Its possible the AI shows P!=NP, but also possible the AI shows P=NP, and finds a fast algorithm. Maybe the AI realizes that BQP=BPP.”
It’s also possible the AI finds a way to break the second law of thermodynamics and to travel faster than light, if we’re just gonna make things up. (I have more confidence in P!=NP than in just about any phsyical law.) If we only have to fear an AI in a world where P=NP, then I’m personally not afraid.
Not sure why you are quite so confident P!=NP. But that doesn’t really matter.
Consider bond strength. Lets say the energy taken to break a C-C bond varies by ±5% based on all sorts of complicated considerations involving the surrounding chemical structure. An AI designing a nanomachine can just apply 10% more energy than needed.
A quantum computer doesn’t just have a superposition of many states, its a superposition carefully chosen such that all the pieces destructively and constructively interfere in exactly the right way. Not that the AI needs exact predictions anyway.
Also, the AI can cheat. As well as fundamental physics, it has access to a huge dataset of experiments conducted by humans. It doesn’t need to deduce everything from QED, it can deduce things from random chemistry papers too.