Better Sensors with Entangled Photons

Illustration: Quantum capabilities could improve functions like GPS, medical imaging, astronomy observation and more. (Source: U. Arizona)

Your phone’s GPS, the WiFi in your house and communi­cations on aircraft are all powered by radio-frequency waves, which carry infor­mation from a transmitter at one point to a sensor at another. The sensors interpret this infor­mation in different ways. For example, a GPS sensor determines its location by using the amount of time it takes to receive a signal from a satellite. For appli­cations such as in-door locali­zation and defeating spoofing GPS signals, a wireless sensor measures the angle at which it receives an RF wave. The more precisely the sensor can measure this time delay or angle of arrival, the more it can accu­rately deter­mine location or enhance security.

Now, 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.

“This quantum sensing paradigm could create opportunities to improve GPS systems, astronomy labora­tories and biomedical imaging capa­bilities,” said Zheshen Zhang, assistant professor of materials science and engineering and optical sciences, and principal investigator of the university’s Quantum Information and Materials Group. “It could be used to improve the performance of any appli­cation that requires a network of sensors.” 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 RF-photonics sensing. “We designed a bridge between an optical system and a physical quantity in a completely different domain,” Zhang explained. “We demons­trated that with an RF domain in this experiment, but the idea could also be applied to other scenarios. For example, if you want to measure tempera­ture using photons, you could use a thermo-optical transducer to convert the temperature into an optical property.”

After converting information to the optical domain, the researchers applied quantum metrology. Usually, a sensor’s precision is limited by the standard quantum limit. For example, smartphone GPS systems are usually accurate within a 16-foot radius. Quantum metro­logy uses entangled particles to break past the standard quantum limit and take ultra­sensitive measurements. How does it work? Entangled particles are tied together so anything that happens to one particle affects the particles it’s entangled with as well, as long as appropriate measure­ments are taken.

Picture a super­visor and an employee working together on a project. Because it takes time for the employee to share information with his supervisor through methods like emails and meetings, the efficiency of their partnership is limited. But if the two could entangle their brains together, the employee and the supervisor would auto­matically have the same infor­mation _ saving time and allowing them to jointly tackle a common problem more efficiently. Quantum metrology has been used to improve sensor precision in places like the Laser Inter­ferometer Gravi­tational-Wave Obser­vatory, or LIGO, which has opened up a new window for astronomers. However, almost all prior quantum metrology demons­trations, including LIGO, only involve a single sensor.

However, RF waves are usually received by a network of sensors, each of which processes information individually – more like a group of independent employees working with their supervisors. Quntao Zhuang, UA assistant professor of electrical and computer engi­neering, 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 with one another simul­taneously. It’s more like if a group of employees could share infor­mation instantly with their bosses, and the bosses could instantly share that infor­mation with each other, making their workflow ultra-efficient. “Typically, in a complex system – for example, a wireless communi­cations network or even our cellphones – there’s not just a single sensor, but a set of sensors that work together to undertake a task,” Zhang said. “We’ve developed a tech­nology to entangle these sensors, rather than having them operate indi­vidually. They can use their entanglement to talk to each other during the sensing period, which can signi­ficantly improve sensing per­formance.”

While the experiment only used three sensors, it opens the door to the possi­bility of applying the technique to networks of hundreds of sensors. “Imagine, for example, a network for bio­logical sensing: You can entangle these biosensors so that they work together to identify the species of a biological molecule, or to detect neural activities more precisely than a classical sensor array,” Zhang said. “Really, this technique could be applied to any appli­cation that requires an array or network of sensors.”

One potential application is in the entangled photon network being built on the University of Arizona campus. Now, Zhuang presented how machine learning techniques can train sensors in a large-scale entangled sensor network like this one to take ultra-precise measure­ments. “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,” Zhuang said. “Our previous work provides a theoretical design of an ent­anglement-enhanced machine learning system that out­performs classical systems.” (Source: U. Arizona)

Reference: Y. Xia et al.: Demonstration of a Reconfigurable Entangled Radio-Frequency Photonic Sensor Network, Phys. Rev. Lett. 124, 150502 (2020); DOI: 10.1103/PhysRevLett.124.150502

Link: James C. Wyant College of Optical Sciences, University of Arizona, Tucson, USA

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