Quantum "entangled" state where photons exist in multiple places simultaneously. Quantum effect-based sensor with enhanced precision level surpassing the limit of conventional mechanisms

Precise data measurement and securing images and videos using highly sensitive sensors are crucial in the era of the Fourth Industrial Revolution. To this end, multiple parameters that change in real-time such as the location of an object, temperature, and time should be estimated with high precision, and quantum effects (superposition and entanglement) can help enhance the measurement precision or estimate the parameters that were not measurable with conventional sensors. The research on such quantum sensors along with quantum supercomputers and communication studies are the major application areas of quantum information technology, in which there is fierce competition between the U.S. and China over technological hegemony. 

A research team led by Dr. Hyang-Tag Lim from the Center for Quantum Information, Korea Institute of Science and Technology (KIST) demonstrated quantum sensors that can estimate multiple parameters changing in real-time with high precision beyond the standard limits.

The KIST research team demonstrated "multi-mode N00N states" where multiple parameters changing in real-time can be precisely estimated. The "multi-mode N00N state" was known to show the highest precision in theory, but its demonstration has been highly challenging.

The research team succeeded in demonstrating quantum-enhanced multiple-phase estimation by generating multi-mode N00N states, a quantum entanglement state with photon number N = 2 and mode number m = 4 that is applied to an interferometer so that its multiple phase difference can be simultaneously measured with high precision beyond the standard quantum limit. 

Dr. Hyang-Tag Lim from the KIST who led this research stated, "Multi-mode N00N states, the key technology of this achievement, will contribute to the further development of quantum sensing as a foundation technique for quantum imaging and sensor networks." He added, "I hope it will also be used in a high-performance quantum microscope and bio-imaging sensors.“

Researchers have long strived to develop computers to work as energy efficiently as our brains. A study, led by researchers at the University of Gothenburg in Sweden's second-largest city, Gothenburg, has succeeded for the first time in combining a memory function with a calculation function in the same component. The discovery opens the way for more efficient technologies.

In recent years, supercomputers have been able to tackle advanced cognitive tasks, like language and image recognition or displaying superhuman chess skills, thanks in large part to artificial intelligence (AI). At the same time, the human brain is still unmatched in its ability to perform tasks effectively and energy efficiently.

“Finding new ways of performing calculations that resemble the brain’s energy-efficient processes has been a major goal of research for decades. Cognitive tasks, like image and voice recognition, require significant computing power, and mobile applications, in particular, like mobile phones, drones, and satellites, require energy-efficient solutions,” says Johan Åkerman, professor of applied spintronics at the University of Gothenburg.

Important breakthrough

Working with a research team at Tohoku University, Åkerman led a study that has now taken a step forward in achieving this goal. In the study, the researchers have succeeded for the first time in linking the two tools for advanced calculations: oscillator networks and memristors.

Åkerman describes oscillators as oscillating circuits that can perform calculations and that are comparable to human nerve cells. Memristors are programable resistors that can also perform calculations and that have integrated memory. This makes them comparable to memory cells. Integrating the two is a major advancement by the researchers.

“This is an important breakthrough because we show that it is possible to combine a memory function with a calculating function in the same component. These components work more like the brain’s energy-efficient neural networks, allowing them to become important building blocks in future, more brain-like computers.”

Enables energy-efficient technologies 

According to Johan Åkerman, the discovery will enable faster, easier to use, and less energy-consuming technologies in many areas. He feels that it is a huge advantage that the research team has successfully produced the components in a small footprint: hundreds of components fit into an area equivalent to a single bacterium. This can be of particular importance in smaller applications like mobile phones.
“More energy-efficient calculations could lead to new functionality in mobile phones. An example is digital assistants like Siri or Google.

Today, all processing is done by servers since the calculations require too much energy for the small size of a phone. If the calculations could instead be performed locally, on the actual phone, they could be done faster and easier without a need to connect to servers.”
He notes self-driving cars and drones as other examples of where more energy-efficient calculations could drive developments.
“The more energy-efficiently that cognitive calculations can be performed, the more applications become possible. That’s why our study really has the potential to advance the field.”