Army-funded researchers have demonstrated a machine learning approach to modifying quantum information in photon-based systems, improving the prospects for deploying quantum sensing and quantum communication technologies on the battlefield.
When photons are used as carriers of quantum information to transmit data, that information is often distorted by environmental changes that destroy the fragile quantum states needed to store it.
Researchers at Louisiana State University have used some sort of machine learning to correct information distortion in quantum systems made up of photons.Was announced in Advanced quantum technology, The team has demonstrated that machine learning techniques using the self-learning and self-evolution capabilities of artificial neural networks can help correct distorted information. As a result, it surpasses traditional protocols that rely on adaptive optics.
“We are still in the very early stages of understanding the potential of machine learning technology to play a role in quantum information science,” said Sarah, Program Manager at the Army Institute, a component of the US Army Combat Capability Development Command. Dr. Gamble said. , DEVCOM, known as the Army Research Institute. “The team’s results are an exciting step in deepening this understanding and could ultimately enhance the Army’s sensing and communications capabilities on the battlefield.”
In this study, the team used a kind of neural network to correct the spatial mode of distorted light at the single photon level.
“Random phase distortion is one of the biggest challenges in using spatial modes of light in a variety of quantum technologies, including quantum communications, quantum cryptography, and quantum sensing,” said Narayan Bhusal, a candidate for LSU’s doctoral program. Mr. says. “Our method is very effective and time-efficient compared to traditional technology. This is an exciting development for the future of free space quantum technology.”
According to the research team, this smart quantum technology is a realistic communication protocol affected by atmospheric eddy, showing the possibility of encoding multiple bits of information into a single photon.
“Our technology has a huge impact on optical communications and quantum cryptography,” said Omar Magaña Loaiza, an assistant professor of physics at LSU. “We are currently implementing a machine learning scheme on the Louisiana Optical Network Initiative, looking for ways to be smart, secure, and quantization.”
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Machine learning shows the potential to enhance quantum information transfer-ScienceDaily
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