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Research in high-energy physics produces masses of data, demanding extensive computational resources. The scientists responsible for managing these resources are now turning to cloud and high-performance computing.
Writing efficient scientific software that makes best use of the increasing complexity of computer architectures requires bringing together modelling, applied mathematics and computer engineering. Physics may help unite these approaches.
Granting access to publications and data may be a step towards open science, but it's not enough to ensure reproducibility. Making computer code available is also necessary — but the emphasis must be on the quality of the programming.
Research in quantum optics has already led to commercial technologies, but the gap between the lab and market products is still large. Looking from the industrial side, one can see ways of bridging this gap.
In our editorial in the April 2007 issue of Nature Physics we looked at the claim of the first demonstration of a commercial quantum computer — D-Wave's 16-qubit Orion. Eight years later, we ponder whether quantum technologies have really become commercial.
New quantum algorithms promise an exponential speed-up for machine learning, clustering and finding patterns in big data. But to achieve a real speed-up, we need to delve into the details.
