In a new paper published in Physical Review Letters, University of Arizona engineering and optical sciences researchers, in collaboration with engineers from General Dynamics Mission Systems, demonstrate how a combination of two techniques – radio frequency photonics sensing and quantum metrology – can give sensor networks a previously unheard-of level of precision. The work involves transferring information from electrons to photons, then using quantum entanglement to increase the photons’ sensing capabilities.
“Entanglement allows sensors to more precisely extract features from the parameters being sensed, allowing for better performance in machine learning tasks such as sensor data classification and principal component analysis,” said assistant ECE professor Quntao Zhuang. “Our previous work provides a theoretical design of an entanglement-enhanced machine learning system that outperforms classical systems.”
Traditional antenna sensors transform information from RF signals to an electrical current made up of moving electrons. However, optical sensing, which uses photons, or units of light, to carry information, is much more efficient. Not only can photons hold more data than electrons, giving the signal larger bandwidth, but photonics-based sensing can transmit that signal much farther than electronics-based sensing, and with less interference.
Because optical signals offer so many advantages, the researchers used an electro-optical transducer to convert RF waves into the optical domain in a method called RF-photonics sensing.
Zhuang had previously demonstrated a theoretical framework to boost performance by teaming up entangled sensors. This new experiment demonstrated for the first time that a network of three sensors can be entangled with one another, meaning they all receive the information from probes and correlate it together simultaneously. The experiment opens the door to the possibility of applying the technique to networks of hundreds of sensors.