Computers Math

NIST scientist Gary Zabow had never intended to use candy in his lab. It was only as a last resort that he had even tried burying microscopic magnetic dots in hardened chunks of sugar — hard candy, basically — and sending these sweet packages to colleagues in a biomedical lab. The sugar dissolves easily in water, freeing the magnetic dots for their studies without leaving any harmful plastics or chemicals behind.
By chance, Zabow had left one of these sugar pieces, embedded with arrays of micromagnetic dots, in a beaker, and it did what sugar does with time and heat — it melted, coating the bottom of the beaker in a gooey mess.
“No problem,” he thought. He would just dissolve away the sugar, as normal. Except this time when he rinsed out the beaker, the microdots were gone. But they weren’t really missing; instead of releasing into the water, they had been transferred onto the bottom of the glass where they were casting a rainbow reflection.
“It was those rainbow colors that really surprised me,” Zabow recalls. The colors indicated that the arrays of microdots had retained their unique pattern.
This sweet mess gave him an idea. Could regular table sugar be used to bring the power of microchips to new and unconventional surfaces? Zabow’s findings on this potential transfer printing process were published in Science on Nov. 25.
Semiconductor chips, micropatterned surfaces, and electronics all rely on microprinting, the process of putting precise but minuscule patterns millionths to billionths of a meter wide onto surfaces to give them new properties. Traditionally, these tiny mazes of metals and other materials are printed on flat wafers of silicon. But as the possibilities for semiconductor chips and smart materials expand, these intricate, tiny patterns need to be printed on new, unconventional, non-flat surfaces. More

Picture two teams squaring off on a football field. The players can cooperate to achieve an objective, and compete against other players with conflicting interests. That’s how the game works.
Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. A key challenge is enabling AI agents to anticipate future behaviors of other agents when they are all learning simultaneously.
Because of the complexity of this problem, current approaches tend to be myopic; the agents can only guess the next few moves of their teammates or competitors, which leads to poor performance in the long run.
Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed a new approach that gives AI agents a farsighted perspective. Their machine-learning framework enables cooperative or competitive AI agents to consider what other agents will do as time approaches infinity, not just over a few next steps. The agents then adapt their behaviors accordingly to influence other agents’ future behaviors and arrive at an optimal, long-term solution.
This framework could be used by a group of autonomous drones working together to find a lost hiker in a thick forest, or by self-driving cars that strive to keep passengers safe by anticipating future moves of other vehicles driving on a busy highway.
“When AI agents are cooperating or competing, what matters most is when their behaviors converge at some point in the future. There are a lot of transient behaviors along the way that don’t matter very much in the long run. Reaching this converged behavior is what we really care about, and we now have a mathematical way to enable that,” says Dong-Ki Kim, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS) and lead author of a paper describing this framework. More

Invented in 1970 by Corning Incorporated, low-loss optical fiber became the best means to efficiently transport information from one place to another over long distances without loss of information. The most common way of data transmission nowadays is through conventional optical fibers — one single core channel transmits the information. However, with the exponential increase of data generation, these systems are reaching information-carrying capacity limits. Thus, research now focuses on finding new ways to utilize the full potential of fibers by examining their inner structure and applying new approaches to signal generation and transmission. Moreover, applications in quantum technology are enabled by extending this research from classical to quantum light.
In the late 50s, the physicist Philip W. Anderson (who also made important contributions to particle physics and superconductivity) predicted what is now called Anderson localization. For this discovery, he received the 1977 Nobel Prize in Physics. Anderson showed theoretically under which conditions an electron in a disordered system can either move freely through the system as a whole, or be tied to a specific position as a “localized electron.” This disordered system can for example be a semiconductor with impurities.
Later, the same theoretical approach was applied to a variety of disordered systems, and it was deduced that also light could experience Anderson localization. Experiments in the past have demonstrated Anderson localization in optical fibers, realizing the confinement or localization of light — classical or conventional light — in two dimensions while propagating it through the third dimension. While these experiments had shown successful results with classical light, so far no one had tested such systems with quantum light — light consisting of quantum correlated states. That is, until recently.
In a study published in Communications Physics, ICFO researchers Alexander Demuth, Robing Camphausen, Alvaro Cuevas, led by ICREA Prof at ICFO Valerio Pruneri, in collaboration with Nick Borrelli, Thomas Seward, Lisa Lamberson and Karl W. Koch from Corning, together with Alessandro Ruggeri from Micro Photon Devices (MPD) and Federica Villa and Francesca Madonini from Politecnico di Milano, have been able to successfully demonstrate the transport of two-photon quantum states of light through a phase-separated Anderson localization optical fibre (PSF).
A conventional optical fiber vs an Anderson localization fiber
Contrary to conventional single mode optical fibers, where data is transmitted through a single core, a phase separated fiber (PSF) or phase separated Anderson localization fiber is made of many glass strands embedded in a glass matrix of two different refractive indexes. During its fabrication, as borosilicate glass is heated and melted, it is drawn into a fiber, where one of the two phases of different refractive indexes tends to form elongated glass strands. Since there are two refractive indexes within the material, this generates what is known as a lateral disorder, which leads to transverse (2D) Anderson localization of light in the material.
Experts in optical fiber fabrication, Corning created an optical fiber that can propagate multiple optical beams in a single optical fiber by harnessing Anderson localization. Contrary to multicore fiber bundles, this PSF showed to be very suitable for such experiments since many parallel optical beams can propagate through the fiber with minimal spacing between them.
The team of scientists, experts in quantum communications, wanted to transport quantum information as efficiently as possible through Corning’s phase-separated optical fiber. In experiment, the PSF connects a transmitter and a receiver. The transmitter is a quantum light source (built by ICFO). The source generates quantum correlated photon pairs via spontaneous parametric down-conversion (SPDC) in a non-linear crystal, where one photon of high energy is converted to pairs of photons, which have lower energy each. The low-energy photon pairs have a wavelength of 810 nm. Due to momentum conservation, spatial anti-correlation arises. The receiver is a single-photon avalanche diode (SPAD) array camera, developed by Polimi and MPD. The SPAD array camera, unlike common CMOS cameras, is so sensitive that it can detect single photons with extremely low noise; it also has very high time resolution, such that the arrival time of the single photons is known with high precision.
Quantum light
The ICFO team engineered the optical setup to send the quantum light through the phase-separated Anderson localization fiber and detected its arrival with the SPAD array camera. The SPAD array enabled them not only to detect the pairs of photons but also to identify them as pairs, as they arrive at the same time (coincident). As the pairs are quantum correlated, knowing where one of the two photons is detected tells us the other photon’s location. The team verified this correlation right before and after sending the quantum light through PSF, successfully showing that the spatial anti-correlation of the photons was indeed maintained.
After this demonstration, the ICFO team then set out to show how to improve their results in future work. For this, they conducted a scaling analysis, in order to find out the optimal size distribution of the elongated glass strands for the quantum light wavelength of 810 nm. After a thorough analysis with classical light they were able to identify the current limitations of phase-separated fiber and propose improvements of its fabrication, in order to minimize attenuation and loss of resolution during transport.
The results of this study have shown this approach to be potentially attractive for scalable fabrication processes in real-world applications in quantum imaging or quantum communications, especially for the fields of high-resolution endoscopy, entanglement distribution and quantum key distribution. More

Physicists at the University of Basel have experimentally demonstrated for the first time that there is a negative correlation between the two spins of an entangled pair of electrons from a superconductor. For their study, the researchers used spin filters made of nanomagnets and quantum dots, as they report in the scientific journal Nature.
The entanglement between two particles is among those phenomena in quantum physics that are hard to reconcile with everyday experiences. If entangled, certain properties of the two particles are closely linked, even when far apart. Albert Einstein described entanglement as a “spooky action at a distance.” Research on entanglement between light particles (photons) was awarded this year’s Nobel Prize in Physics.
Two electrons can be entangled as well — for example in their spins. In a superconductor, the electrons form so-called Cooper pairs responsible for the lossless electrical currents and in which the individual spins are entangled.
For several years, researchers at the Swiss Nanoscience Institute and the Department of Physics at the University of Basel have been able to extract electron pairs from a superconductor and spatially separate the two electrons. This is achieved by means of two quantum dots — nanoelectronic structures connected in parallel, each of which only allows single electrons to pass.
Opposite electron spins from Cooper pairs
The team of Prof. Dr. Christian Schönenberger and Dr. Andreas Baumgartner, in collaboration with researchers led by Prof. Dr. Lucia Sorba from the Istituto Nanoscienze-CNR and the Scuola Normale Superiore in Pisa have now been able to experimentally demonstrate what has long been expected theoretically: electrons from a superconductor always emerge in pairs with opposite spins.
Using an innovative experimental setup, the physicists were able to measure that the spin of one electron points upwards when the other is pointing downwards, and vice versa. “We have thus experimentally proven a negative correlation between the spins of paired electrons,” explains project leader Andreas Baumgartner.
The researchers achieved this by using a spin filter they developed in their laboratory. Using tiny magnets, they generated individually adjustable magnetic fields in each of the two quantum dots that separate the Cooper pair electrons. Since the spin also determines the magnetic moment of an electron, only one particular type of spin is allowed through at a time.
“We can adjust both quantum dots so that mainly electrons with a certain spin pass through them,” explains first author Dr. Arunav Bordoloi. “For example, an electron with spin up passes through one quantum dot and an electron with spin down passes through the other quantum dot, or vice versa. If both quantum dots are set to pass only the same spins, the electric currents in both quantum dots are reduced, even though an individual electron may well pass through a single quantum dot.”
“With this method, we were able to detect such negative correlations between electron spins from a superconductor for the first time,” Andreas Baumgartner concludes. “Our experiments are a first step, but not yet a definitive proof of entangled electron spins, since we cannot set the orientation of the spin filters arbitrarily — but we are working on it.”
The research, which was recently published in Nature, is considered an important step toward further experimental investigations of quantum mechanical phenomena, such as the entanglement of particles in solids, which is also a key component of quantum computers.
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Materials provided by University of Basel. Original written by Christel Möller. Note: Content may be edited for style and length. More

A multi-institution research team has developed an optical chip that can train machine learning hardware.
Machine learning applications skyrocketed to $165B annually, according to a recent report from McKinsey. But, before a machine can perform intelligence tasks such as recognizing the details of an image, it must be trained. Training of modern-day artificial intelligence (AI) systems like Tesla’s autopilot costs several million dollars in electric power consumption and requires supercomputer-like infrastructure. This surging AI “appetite” leaves an ever-widening gap between computer hardware and demand for AI. Photonic integrated circuits, or simply optical chips, have emerged as a possible solution to deliver higher computing performance, as measured by the number of operations performed per second per watt used, or TOPS/W. However, though they’ve demonstrated improved core operations in machine intelligence used for data classification, photonic chips have yet to improve the actual front-end learning and machine training process.
Machine learning is a two-step procedure. First, data is used to train the system and then other data is used to test the performance of the AI system. IIn a new paper, a team of researchers from the George Washington University, Queens University, University of British Columbia and Princeton University set out to do just that. After one training step, the team observed an error and reconfigured the hardware for a second training cycle followed by additional training cycles until a sufficient AI performance was reached (e.g. the system is able to correctly label objects appearing in a movie). Thus far, photonic chips have only demonstrated an ability to classify and infer information from data. Now, researchers have made it possible to speed up the training step itself.
This added AI capability is part of a larger effort around photonic tensor cores and other electronic-photonic application-specific integrated circuits (ASIC) that leverage photonic chip manufacturing for machine learning and AI applications.
“This novel hardware will speed up the training of machine learning systems and harness the best of what both photonics and electronic chips have to offer. It is a major leap forward for AI hardware acceleration. These are the kinds of advancements we need in the semiconductor industry as underscored by the recently passed CHIPS Act.”
-Volker Sorger, Professor of Electrical and Computer Engineering at the George Washington University and founder of the start-up company Optelligence.
“The training of AI systems costs a significant amount of energy and carbon footprint. For example, a single AI transformer takes about five times as much CO2 in electricity as a gasoline car spends in its lifetime. Our training on photonic chips will help to reduce this overhead.”
-Bhavin Shastri, Assistant Professor of Physics Department Queens University
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Materials provided by George Washington University. Note: Content may be edited for style and length. More

Quantum computers promise significantly shorter computing times for complex problems. But there are still only a few quantum computers worldwide with a limited number of so-called qubits. However, quantum computer algorithms can already run on conventional servers that simulate a quantum computer. A team at HZB has succeeded to calculate the electron orbitals and their dynamic development on the example of a small molecule after a laser pulse excitation. In principle, the method is also suitable for investigating larger molecules that cannot be calculated using conventional methods.
“These quantum computer algorithms were originally developed in a completely different context. We used them here for the first time to calculate electron densities of molecules, in particular also their dynamic evolution after excitation by a light pulse,” says Annika Bande, who heads a group on theoretical chemistry at HZB. Together with Fabian Langkabel, who is doing his doctorate with Bande, she has now shown in a study how well this works.
Error-free quantum computer
“We developed an algorithm for a fictitious, completely error-free quantum computer and ran it on a classical server simulating a quantum computer of ten Qbits,” says Fabian Langkabel. The scientists limited their study to smaller molecules in order to be able to perform the calculations without a real quantum computer and to compare them with conventional calculations.
Faster computation
Indeed, the quantum algorithms produced the expected results. In contrast to conventional calculations, however, the quantum algorithms are also suitable for calculating significantly larger molecules with future quantum computers: “This has to do with the calculation times. They increase with the number of atoms that make up the molecule,” says Langkabel. While the computing time multiplies with each additional atom for conventional methods, this is not the case for quantum algorithms, which makes them much faster.
Photocatalysis, light reception and more
The study thus shows a new way to calculate electron densities and their “response” to excitations with light in advance with very high spatial and temporal resolution. This makes it possible, for example, to simulate and understand ultrafast decay processes, which are also crucial in quantum computers made of so-called quantum dots. Also predictions about the physical or chemical behaviour of molecules are possible, for example during the absorption of light and the subsequent transfer of electrical charges. This could facilitate the development of photocatalysts for the production of green hydrogen with sunlight or help to understand processes in the light-sensitive receptor molecules in the eye.
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Materials provided by Helmholtz-Zentrum Berlin für Materialien und Energie. Note: Content may be edited for style and length. More

A new study has revealed how the glass-like shells of diatoms help these microscopic organisms perform photosynthesis in dim conditions. A better understanding of how these phytoplankton harvest and interact with light could lead to improved solar cells, sensing devices and optical components.
“The computational model and toolkit we developed could pave the way toward mass-manufacturable, sustainable optical devices and more efficient light harvesting tools that are based on diatom shells,” said research team member Santiago Bernal from McGill University in Canada. “This could be used for biomimetic devices for sensing, new telecommunications technologies or affordable ways to make clean energy.”
Diatoms are single-celled organisms found in most bodies of water. Their shells are covered in holes that respond to light differently depending on their size, spacing and configuration. In the journal Optical Materials Express, the researchers, led by McGill University’s David V. Plant and Mark Andrews, report the first optical study of an entire diatom shell. They analyzed how different sections of the shell, or frustule, respond to sunlight and how this response is connected to photosynthesis.
“Based on our findings, we estimate that the frustule can contribute a 9.83 percent boost to photosynthesis, especially during transitions from high to low sunlight,” said Yannick D’Mello, first author of the paper. “Our model is the first to explain the optical behavior of the entire frustule. So, it contributes to the hypothesis that the frustule enhances photosynthesis in diatoms.”
Combining microscopy and simulation
Diatoms have evolved for millions of years to survive in any aquatic environment. This includes their shell, which is composed of many regions that work together to harvest sunlight. To study the optical response of diatom frustules, the researchers combined computer optical simulations with several microscopy techniques. More

LMU researchers have developed a new model to describe how biological or technical systems form complex structures without external guidance.
Amoebae are single-cell organisms. By means of self-organization, they can form complex structures — and do this purely through local interactions: If they have a lot of food, they disperse evenly through a culture medium. But if food becomes scarce, they emit the messenger known as cyclic adenosine monophosphate (cAMP). This chemical signal induces amoebae to gather in one place and form a multicellular aggregation. The result is a fruiting body.
“The phenomenon is well known,” says Prof. Erwin Frey from LMU’s Faculty of Physics. “Before now, however, no research group has investigated how information processing, at a general level, affects the aggregation of systems of agents when individual agents — in our case, amoebae — are self-propelled.” More knowledge about these mechanisms would also be interesting, adds Frey, as regards translating them to artificial technical systems.
Together with other researchers, Frey describes in Nature Communications how active systems that process information in their environment can be used — for technological or biological applications. It is not about understanding all details of the communication between individual agents, but about the specific structures formed through self-organization. This applies to amoebae — and also to certain kinds of robots. The research was undertaken in collaboration with Prof. Igor Aronson during his stay at LMU as a Humboldt Research Award winner.
From biological mechanism to technological application
Background: The term “active matter” refers to biological or technical systems from which larger structures are formed by means of self-organization. Such processes are based upon exclusively local interactions between identical, self-propelled units, such as amoebae or indeed robots.
Inspired by biological systems, Frey and his co-authors propose a new model in which self-propelled agents communicate with each other. These agents recognize chemical, biological, or physical signals at a local level and make individual decisions using their internal machinery that result in collective self-organization. This orientation gives rise to larger structures, which can span multiple length scales.
The new paradigm of communicating active matter forms the basis of the study. Local decisions in response to a signal and the transmission of information, lead to collectively controlled self-organization.
Frey sees a possible application of the new model in soft robots — which is to say, robots that are made of soft materials. Such robots are suitable, for example, for performing tasks in human bodies. They can communicate with other soft robots via electromagnetic waves for purposes such as administering drugs at specific sites in the body. The new model can help nanotechnologists design such robot systems by describing the collective properties of robot swarms.
“It’s sufficient to roughly understand how individual agents communicate with each other; self-organization takes care of the rest,” says Frey. “This is a paradigm shift specifically in robotics, where researchers are attempting to do precisely the opposite — they want to obtain extremely high levels of control.” But that does not always succeed. “Our proposal, by contrast, is to exploit the capacity for self-organization.”
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Materials provided by Ludwig-Maximilians-Universität München. Note: Content may be edited for style and length. More