HPC YEARBOOK 2021/22


Quantum computing opens up new possibilities for research


Matthias Troyer, distinguished scientist at Microsoft and keynote speaker at ISC High Performance conference, discusses his research into quantum computing and the development of quantum hardware and algorithms for scientists.


M


atthias Troyer is a Fellow of the American Physical Society, vice president


of the Aspen Center for Physics, a recipient of the Rahman Prize for Computational Physics of the American Physical Society for ‘for pioneering numerical work in many seemingly intractable areas of quantum many- body physics and for providing efficient sophisticated computer codes to the community’, and of the Hamburg Prize for Theoretical Physics. He works on a variety of topics in quantum computing, from the simulation of materials and quantum devices to quantum software, algorithms and applications of future quantum computers. His broader research interests span


from high performance computing and quantum computing to the simulations of quantum devices and island ecosystems.


How will the first quantum hardware solutions be implemented for scientists? The first quantum hardware will be an accelerator. It will not replace classical computers, it will be an accelerator like GPUs, but they will be disruptively powerful for some applications – so it will be a special-purpose accelerator for certain problems. For most users you need to solve


problems, like on classical HPC machines, most people don’t write the kernel for GPUs they use libraries and application packages built using


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accelerators. Those people will just have to know where quantum will be useful, and how to call the library that then uses the quantum hardware. But then there are those people


who write those libraries, who write the algorithms for quantum hardware. They will have to understand more deeply how to use quantum computing to accelerate their calculations. And then, finally, there will be the people who build the hardware. And they will most likely be electrical engineers, fabrication engineers with more knowledge of what is needed to get the quantum hardware to run and operate. First we develop the programming


tools, the programming languages, like Q#, the compilers, the libraries, and so on so that people can start to learn about quantum computing, learn the principles and then start to invent the quantum algorithms. And then they can run them on simulators (classical computing systems that simulate Qubits).


That way, they can test the quantum algorithms, debug them, profile them, optimise them, and find out the resources required to use a certain application to scale in the future.


How do you identify applications that are suitable for quantum computing? What are the problems that I can solve much better using quantum hardware in the future?


When looking at that, we see not just which problems we can solve


programming languages, like Q#, the compilers, the libraries, and so on so that people can start to learn about quantum computing


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quantumly, but which problems can be solved better and faster than on any potential classical machine. Then we found that there are three


conditions we need for that. First, we need a problem where there is a quantum algorithm with quantum speed-up. Secondly, we need a problem that acts on small data, not big data. And the reason is that your quantum computers will have a slower clock speed than classical ones, and so I/O will be a bigger challenge than even classical. Thirdly, again, because the clock speed is slower, and because the Qubits are much more complex than transistors, every operation will take


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First we develop the programming tools, the


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