Micro­cavities as a sensor plat­form

Nano particles trapped between mirrors might be a promising platform for quantum sensors.
Credit: IQOQI Innsbruck

Sensors are a pillar of the Internet of Things, providing the data to control all sorts of objects. Here, precision is essential, and this is where quantum technologies could make a difference. Researchers in Innsbruck and Zurich are now demonstrating how nanoparticles in tiny optical resonators can be transferred into quantum regime and used as high-precision sensors.

Advances in quantum physics offer new opportunities to significantly improve the precision of sensors and thus enable new technologies. A team led by Oriol Romero-Isart of the Institute of Quantum Optics and Quantum Information at the Austrian Academy of Sciences and the Department of Theoretical Physics at the University of Innsbruck and a team lead by Romain Quidant of ETH Zurich are now proposing a new concept for a high-precision quantum sensor. The researchers suggest that the motional fluctuations of a nanoparticle trapped in a microscopic optical resonator could be reduced significantly below the zero-point motion, by exploiting the fast unstable dynamics of the system.

Does this artificial intelligence think like a human?

MIT researchers developed a method that helps a user understand a machine-learning model’s reasoning, and how that reasoning compares to that of a human.
Credits: Christine Daniloff, MIT

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo.

While tools exist to help experts make sense of a model’s reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns.

Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model’s behavior. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model’s reasoning matches that of a human.

Shared Interest could help a user easily uncover concerning trends in a model’s decision-making — for example, perhaps the model often becomes confused by distracting, irrelevant features, like background objects in photos. Aggregating these insights could help the user quickly and quantitatively determine whether a model is trustworthy and ready to be deployed in a real-world situation.

Let quantum dots grow regularly

With this experimental setup, the researchers check the quality of the quantum dots. Green laser light is used to stimulate the quantum dots that then emit infrared light.
© İsmail Bölükbaşı

With the previous manufacturing process, the density of the structures was difficult to control. Now researchers can create a kind of checkerboard pattern. A step towards application, for example in a quantum computer.

Quantum points could one day form the basic information units of quantum computers. Researchers at the Ruhr University Bochum (RUB) and the Technical University of Munich (TUM) have significantly improved the manufacturing process for these tiny semiconductor structures, together with colleagues from Copenhagen and Basel. The quantum dots are generated on a wafer, a thin semiconductor crystal disc. So far, the density of the structures on it has been difficult to control. Now scientists can create specific arrangements - an important step towards an applicable component that should have a large number of quantum dots.

The team published the results on 28. March 2022 in the journal Nature Communications. A group led by Nikolai Bart, Prof. Dr. Andreas Wieck and Dr. Arne Ludwig from the RUB Chair for Applied Solid State Physics with the team around Christian Dangel and Prof. Dr. Jonathan Finley from the TUM working group semiconductor nanostructures and quantum systems as well as with colleagues from the universities of Copenhagen and Basel.

A tool for predicting the future

MIT researchers created a tool that enables people to make highly accurate predictions using multiple time-series data with just a few keystrokes. The powerful algorithm at the heart of their tool can transform multiple time series into a tensor, which is a multi-dimensional array of numbers (pictured). Credits: Figure courtesy of the researchers Source: MIT

Whether someone is trying to predict tomorrow’s weather, forecast future stock prices, identify missed opportunities for sales in retail, or estimate a patient’s risk of developing a disease, they will likely need to interpret time-series data, which are a collection of observations recorded over time.

Making predictions using time-series data typically requires several data-processing steps and the use of complex machine-learning algorithms, which have such a steep learning curve they aren’t readily accessible to nonexperts.

To make these powerful tools more user-friendly, MIT researchers developed a system that directly integrates prediction functionality on top of an existing time-series database. Their simplified interface, which they call tspDB (time series predict database), does all the complex modeling behind the scenes so a nonexpert can easily generate a prediction in only a few seconds.

The new system is more accurate and more efficient than state-of-the-art deep learning methods when performing two tasks: predicting future values and filling in missing data points.