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Credit: Johan Jarnestad/ The Royal Swedish Academy of Sciences
Scientists have used AI to determine a simpler method to form a quantum knitting between subatomar particles and to pave the way for simpler quantum technologies.
If particles such as photons scatter, you can share quantum properties – including information – regardless of the distance between you. This phenomenon is important in Quantum physics and is one of the characteristics that make Quantum computer So powerful.
However, the ties of quantum knitting have usually proven to be difficult for scientists. This is due to the fact that the preparation of two separate distorted pairs is required and then the strength of the entangling of the bell state measurement is designated-measured on a photon from each of the couples.
These measurements mean that the quantum system breaks down and the two not measured photons are involved, even though they have never interacted directly. This process of “exchanging entanglement” could be used for quantum removal.
In a new study published on December 2, 2024 in the magazine Physical reviewsScientists used PyheusA AI tool that was specially created for designing quantum-optical experiments. The authors of the paper initially wanted to reproduce established protocols for the exchange of entanglement into quantum communication. However, the AI tool always created a much simpler method to achieve the quantum mixing of photons.
“The authors were able to train a neural network in a number of complex data in which it is described how they have set up this type of experiment under many different conditions, and the network actually learned physics behind it”, ” Sofia VallecorsaA research physicist for quantum technology initiative at CernLive Science said that was not involved in the new research.
Related: Quantum data that have been broadcast for the first time in addition to ‘classic data’ in the same fiber -optical connection
Tap the AI to simplify quantum knitting
The proposed AI tool that could arise because the paths of the photons could not be distinguished: if there are several possible sources from which the photons could have come from and their origin cannot be distinguished from each other, delegates can be made if there were no previously existed.
Although the scientists were initially skeptical about the results, the tool always returned the same solution and tested the theory. By adapting the photon sources and ensuring that they could not be distinguished, the physicists created conditions in which the detection of photons in certain ways guarantees that two others were involved.
This breakthrough in quantum physics has simplified the process through which quantum knitting can be formed. In the future, it could have an impact on quantum networks that are used for safe messaging and make these technologies much more practical.
“The more we can rely on simple technology, the more we can increase the area of application,” said Vallecorsa. “The possibility of building more complex networks that can be used in various geometries could have a major impact on the only end-to-end case.”
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It remains to be seen whether it is practical to scale the technology into a commercially sustainable process, since environmental noises and unstints of devices can lead to instability in the quantum system.
The new study has also presented a convincing argument for the use of AI as a research instrument by physicist. “We are looking for more in the introduction of AI, but there is still a bit of skepticism, mainly because of the role of the physicist when we will go,” said Vallecorsa. “It is an opportunity to achieve a very interesting result and to show in a very convincing way how this can be a tool that the physicist use.”
