HPC YEARBOOK 2021/22


longer and so the classical hardware has a big constant advantage over the quantum hardware. But it can overcome that by the quantum algorithm having to do far fewer operations. And what we need for that, then, are quantum algorithms with exponential quantum speed-up so that I can beat the constant advantage that the most simple classical system has.


What are the main applications being targeted for quantum computing right now?


The main applications in the mid-term will be for chemistry and material science, where you can predictively calculate the properties of materials, molecules, catalysts and chemicals. When you look at what is needed to do that, for a size of a problem that they can’t do classically, then you see we need about one million Qubits. There is a big range of problems that, in principle, can profit from quantum computing including protein folding, drug design, weather modelling, climate modelling and finance. For all of those, there is a quantum


speed-up that means as the problem size increases, ultimately, when the problem is long enough, when the runtime is long enough, the quantum computer will be faster than the


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classical one. But in many cases, when looking


at that, we realised that when the crossover time is large enough, quantum will win. But the crossover time might be that I have to wait for 100 million years for quantum to beat classical and that’s not practical. We want to look only at smaller problems, where, within a week or a month, the quantum computer can beat the classic one.


And that’s where then many of those


problems drop out for now. We’re left with those problems with an exponential quantum speed-up where I need to use exponentially more operations if I were to solve a classically, rather than quantumly – then it’s much easier to beat it with a quantum computer.


If a problem is seemingly intractable, how can a scientist think about how to solve it?


I am tackling problems that are seemingly intractable, but where I have an idea of how a quantum computer could solve it. And then, in order to solve it, on quantum hardware, I think about a different approach because I want to use the power of the quantum computer. I formulate the problem in a different way. So that I can then apply a certain


quantum algorithm that they know will have to speed up on future quantum hardware. And as I do that, and as I reformulate the problem to fit the acceleration offered by the quantum computer, I then sometimes realise that this new viewpoint, this new way of attacking the problem, gives me new ideas, how it can be solved classically. Sometimes we have seen that this


can be thousands of times faster than the typical way that they did classically. And so instead of intractable in


this case, I say, these are seemingly intractable problems where we didn’t see a way of doing it classically. But we saw a way of maybe doing it using quantum, and then working out the details, we find a new way of solving the problem, potentially very fast on classical hardware. If I think I can solve it classically, then


I don’t even think about it. But, as quantum opens the


possibility to take a new look, then I realise – maybe I was wrong – there is a better way of tackling the problems classically.


And that gets triggered by quantum


thinking by just the possibility that quantum will offer. And then of course, once we have quantum hardware, it will work even better and faster in those cases as well. l


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