Researchers Develop Micro-Lens to Link Quantum Computers to Network

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Researchers from Australian National University developed a micro camera lens that may lead to a device that links quantum computers to an optical fiber network

Quantum computers in the near future can help develop ultra-secure networks, artificial intelligence, and therapeutic drugs. These computers could solve certain problems much faster as compared to current computers. Now, an international team of researchers at The Australian National University (ANU) developed a micro camera lens. The lens can be a breakthrough for future devices that link quantum computers to an optical fiber network.

The research was led by Sukhorukov, associate professor at ANU. The lens was developed at the Nonlinear Physics Centre of the ANU Research School of Physics and Engineering. The novel lens is around 100 times thinner than a human hair and according to the researchers the lens could enable a fast and reliable transfer of information from quantum computers to optical fiber networks. The device manufactured from a silicon film has millions of nano-structures forming a metasurface. The metasurface can control light with functionalities that can outperform conventional systems. Moreover, the metasurface camera lens is highly transparent that enables efficient transmission and detection of information encoded in quantum light. The lens can image several quantum particles of light at a given instance, which facilitates observation of unconventional behavior of these particles.

Kai Wang, a PhD scholar at the Nonlinear Physics Centre who worked on all aspects of the project, said, “One challenge was making portable quantum technologies. Our device offers a compact, integrated and stable solution for manipulating quantum light. It is fabricated with a similar kind of manufacturing technique used by Intel and NVIDIA for computer chips.” The research was conducted in collaboration with researchers at the Oak Ridge National Laboratory in the U.S and the National Central University in Taiwan. The research was published in the journal Science on September 13, 2018.

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