Winterthur - A team at the Zurich University of Applied Sciences (ZHAW) has for the first time been able to solve specific machine learning tasks partly using a quantum computer and partly using a traditional computer. The results were more accurate than those from generated with only one of the methods.

According to a press release, researchers at the ZHAW have combined the advantages of quantum computing with the reliability of traditional computers for the first time. With this hybrid method, they only transferred part of an algorithm to an IBM quantum computer. This meant that they were able to overcome the issue that, although quantum bits or qubits can deal with numerous processes in parallel and thus very quickly, they are prone to errors, such as those from external influences like temperature fluctuations or electromagnetic radiation.

However, miscalculations can also arise from internal processes, as the qubits only remain in a stable state for a brief period of time. This means that the smallest algorithms possible are required for quantum computers to rapidly reach a result before the qubits become unstable. The less complex part of the algorithm was calculated by a traditional computer. In this way, the team took advantage of the fact that quantum computers do not present any benefits versus traditional computers for simple tasks.

The team also used neural networks, for example for weather forecasts, as these can recognize patterns in large amounts of data on several levels. This hybrid process made it possible to create significantly more accurate weather predictions.

Prof. Dr. Kurt Stockinger from the ZHAW Institute of Applied Technology commented: “We have now progressed from the theory to application.” This means that the technology is now also of interest for companies, for example banks, “with a view to the possibilities of security technology as well”. ce/mm

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