will be in the scalable control and readout of the qubits, because right now when you look at the lab experiments or the first small quantum machines, they are controlled with negative 10s of wires going into a cryostat, which is cooled down to millikelvin temperatures. This will not scale to millions of qubits because you cannot put a million wires into your cryostat and keep it cold. So we have to move to a more scalable cryogenic control. We also have to develop control technology that will not cause noise, will not heat and will not disturb the quantum bits. That may ultimately be the biggest challenge. There is a large range of problems that, in principle, can profit protein folding, drug design, weather modelling, climate modelling, finance – there have been many, many proposals. 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 run time is long enough, the quantum computer will be faster than the classical one. But in many cases, when looking at that,

we realised, when the crossover time is large enough, quantum will win. But the crossover time might be I have to wait

“Fugaku’s peak performance is actually above an exaflop. Such an achievement has caused some to introduce this machine as the first ‘exascale’ supercomputer”

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 classical one. And that’s where many of those

problems drop out for now. So we’re left with those problems with an exponential quantum speedup, where I need to use exponentially more operations if I were to solve [that problem] classically, rather than quantumly. It’s much easier to beat [those problems] with a quantum computer. Those [include] problems in chemistry, and problems in material science and quantum science more broadly. As well as

problems in cryptanalysis – code breaking, for example, so problems when looking for certain structures.

Fugaku holds the number 1 position on the Top500

The 57th edition of the TOP500 saw little change in the top 10. The only new entry in the top 10 systems was the Perlmutter supercomputer system by the National Energy Research Scientific Computing Center (NERSC) at the Lawrence Berkeley National Laboratory. The machine, which will be used within the US Department of Energy, is based on the HPE Cray ‘Shasta’ platform and is a heterogeneous system, with both GPU-accelerated and CPU-only nodes. Perlmutter achieved 64.6 Pflop/s, putting the supercomputer at number five in the latest version of the TOP500 list. The Japanese supercomputer Fugaku held onto the top spot on the list. A system co-developed by RIKEN and Fujitsu, Fugaku has an HPL benchmark score of 442 Pflop/s. The performance of Fugaku is approximately three times that of the number two machine, Summit. The machine is based on Fujitsu’s custom ARM A64FX processor. What’s more, in single or

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