Russian Physicists Discover Method to Increase Number of Atoms in Quantum Sensors
Physicists from the Institute of Spectroscopy of the Russian Academy of Sciences and HSE University have successfully trapped rubidium-87 atoms for over four seconds. Their method can help improve the accuracy of quantum sensors, where both the number of trapped atoms and the trapping time are crucial. Such quantum systems are used to study dark matter, refine navigation systems, and aid in mineral exploration. The study findings have been published in the Journal of Experimental and Theoretical Physics Letters.
Quantum sensors are devices that leverage the effects of quantum mechanics to study matter. They enable the detection of minute changes in gravitational and magnetic fields and allow for highly precise measurements of Earth's acceleration and rotation. Advancements in this field of modern applied physics have the potential to redefine the standards of accuracy in measuring physical quantities.
However, atoms cannot simply be placed in a sensor, as thermal motion prevents them from remaining in place for even a minute. To confine atoms within a specific area, scientists slow them down by cooling them through multiple stages using various techniques. The first stage involves cooling and trapping atoms in magneto-optical traps (MOTs), which are created using laser light and magnetic fields. Creating magnetic field distributions in compact devices requires the use of an atomic chip.
'Each cooling stage reduces the number of atoms in the sensor's working volume, which in turn decreases the accuracy of the device. Therefore, it is crucial to collect as many atoms as possible during the preparation of the initial ensemble to ensure that the accuracy of the quantum sensor remains high after all cooling stages.' This is how Daria Bykova, a doctoral student and teacher at the HSE Faculty of Physics, explains the key aspect of the problem.
Primary cooling to a temperature of around a hundred microkelvins significantly slows the thermal motion of atoms, helping retain them in a designated area of space. A decrease in temperature is achieved through laser radiation: exposure to a laser beam causes atoms to lose kinetic energy and move more slowly. Together, laser radiation and a magnetic field hold atoms in place long enough to conduct experiments, effectively forming a trap from which atoms cannot easily escape. In the next stage, which does not involve a laser field, atoms are cooled to a temperature of about a hundred nanokelvins, which is another thousand times lower.
'One could say that we use laser radiation to "push" the atoms toward the centre of the trap. They are trapped by a magnetic field and the pressure of light,' comments Bykova.
An atomic chip is an effective technology that enables researchers to reduce the size of quantum sensors and improve their energy efficiency. It generates a magnetic field near its surface, which is essential for creating traps, and allows for the cooling and localisation of atom ensembles near it.
At the Department of Laser Spectroscopy of the Institute of Spectroscopy (RAS), students and doctoral students from HSE University created traps using atomic chip technology. This configuration allowed them to retain atoms in the designated area for 4 seconds, a duration considered long in quantum technologies.
The researchers experimentally demonstrated that when using an atomic beam to load atoms into a MOT on a chip, the number of trapped atoms increases significantly compared to loading from atomic vapour in a vacuum chamber. The researchers also confirmed their ability to effectively control the loading of the atomic trap. They were able to adjust the position of the atomic beam using laser fields. This combination of technologies has significantly increased the loading speed while maintaining an ultra-high vacuum in the atomic chip area, compared to previous experiments.
'We discovered the optimal loading conditions in the MOT and trapped 4.9×10⁷ atoms, a number sufficient for stable operation. The ensemble's lifetime is 4.1 seconds, which is long enough to carry out the subsequent stages of deeper cooling and create a prototype of a quantum sensor,' explained Anton Afanasyev, Associate Professor at the Joint Department of Quantum Optics and Nanophotonics with the Institute for Spectroscopy (RAS) of the HSE Faculty of Physics, Senior Research Fellow at the Institute of Spectroscopy (RAS).
The study was supported by the HSE Academic Fund and carried out at the Department of Laser Spectroscopy of the Institute of Spectroscopy (RAS).
See also:
'Our Goal Is Not to Determine Which Version Is Correct but to Explore the Variability'
The International Linguistic Convergence Laboratory at the HSE Faculty of Humanities studies the processes of convergence among languages spoken in regions with mixed, multiethnic populations. Research conducted by linguists at HSE University contributes to understanding the history of language development and explores how languages are perceived and used in multilingual environments. George Moroz, head of the laboratory, shares more details in an interview with the HSE News Service.
Slim vs Fat: Overweight Russians Earn Less
Overweight Russians tend to earn significantly less than their slimmer counterparts, with a 10% increase in body mass index (BMI) associated with a 9% decrease in wages. These are the findings made by Anastasiia Deeva, lecturer at the HSE Faculty of Economic Sciences and intern researcher in Laboratory of Economic Research in Public Sector. The article has been published in Voprosy Statistiki.
Scientists Reveal Cognitive Mechanisms Involved in Bipolar Disorder
An international team of researchers including scientists from HSE University has experimentally demonstrated that individuals with bipolar disorder tend to perceive the world as more volatile than it actually is, which often leads them to make irrational decisions. The scientists suggest that their findings could lead to the development of more accurate methods for diagnosing and treating bipolar disorder in the future. The article has been published in Translational Psychiatry.
Scientists Develop AI Tool for Designing Novel Materials
An international team of scientists, including researchers from HSE University, has developed a new generative model called the Wyckoff Transformer (WyFormer) for creating symmetrical crystal structures. The neural network will make it possible to design materials with specified properties for use in semiconductors, solar panels, medical devices, and other high-tech applications. The scientists will present their work at ICML, a leading international conference on machine learning, on July 15 in Vancouver. A preprint of the paper is available on arxiv.org, with the code and data released under an open-source license.
HSE Linguists Study How Bilinguals Use Phrases with Numerals in Russian
Researchers at HSE University analysed over 4,000 examples of Russian spoken by bilinguals for whom Russian is a second language, collected from seven regions of Russia. They found that most non-standard numeral constructions are influenced not only by the speakers’ native languages but also by how frequently these expressions occur in everyday speech. For example, common phrases like 'two hours' or 'five kilometres’ almost always match the standard literary form, while less familiar expressions—especially those involving the numerals two to four or collective forms like dvoe and troe (used for referring to people)—often differ from the norm. The study has been published in Journal of Bilingualism.
Overcoming Baby Duck Syndrome: How Repeated Use Improves Acceptance of Interface Updates
Users often prefer older versions of interfaces due to a cognitive bias known as the baby duck syndrome, where their first experience with an interface becomes the benchmark against which all future updates are judged. However, an experiment conducted by researchers from HSE University produced an encouraging result: simply re-exposing users to the updated interface reduced the bias and improved their overall perception of the new version. The study has been published in Cognitive Processing.
Mathematicians from HSE Campus in Nizhny Novgorod Prove Existence of Robust Chaos in Complex Systems
Researchers from the International Laboratory of Dynamical Systems and Applications at the HSE Campus in Nizhny Novgorod have developed a theory that enables a mathematical proof of robust chaotic dynamics in networks of interacting elements. This research opens up new possibilities for exploring complex dynamical processes in neuroscience, biology, medicine, chemistry, optics, and other fields. The study findings have been accepted for publication in Physical Review Letters, a leading international journal. The findings are available on arXiv.org.
Mathematicians from HSE University–Nizhny Novgorod Solve 57-Year-Old Problem
In 1968, American mathematician Paul Chernoff proposed a theorem that allows for the approximate calculation of operator semigroups, complex but useful mathematical constructions that describe how the states of multiparticle systems change over time. The method is based on a sequence of approximations—steps which make the result increasingly accurate. But until now it was unclear how quickly these steps lead to the result and what exactly influences this speed. This problem has been fully solved for the first time by mathematicians Oleg Galkin and Ivan Remizov from the Nizhny Novgorod campus of HSE University. Their work paves the way for more reliable calculations in various fields of science. The results were published in the Israel Journal of Mathematics (Q1).
Large Language Models No Longer Require Powerful Servers
Scientists from Yandex, HSE University, MIT, KAUST, and ISTA have made a breakthrough in optimising LLMs. Yandex Research, in collaboration with leading science and technology universities, has developed a method for rapidly compressing large language models (LLMs) without compromising quality. Now, a smartphone or laptop is enough to work with LLMs—there's no need for expensive servers or high-powered GPUs.
AI to Enable Accurate Modelling of Data Storage System Performance
Researchers at the HSE Faculty of Computer Science have developed a new approach to modelling data storage systems based on generative machine learning models. This approach makes it possible to accurately predict the key performance characteristics of such systems under various conditions. Results have been published in the IEEE Access journal.