Computers Math

Floating sea macro-litter is a threat to the conservation of marine ecosystems worldwide. The largest density of floating litter is in the great ocean gyres — systems of circular currents that spin and catch litter — but the polluting waste is abundant in coastal waters and semi closed seas such as the Mediterranean.
MARLIT, an open access web app based on an algorithm designed with deep learning techniques, will enable the detection and quantification of floating plastics in the sea with a reliability over 80%, according to a study published in the journal Environmental Pollution and carried out by experts of the Faculty of Biology and the Biodiversity Research Institute of the University of Barcelona (IRBio).
This methodology results from the analysis through artificial intelligence techniques of more than 3,800 aerial images of the Mediterranean coast in Catalonia, and it will allow researchers to make progress in the assessment of the presence, density and distribution of the plastic pollutants in the seas and oceans worldwide. Among the participants in the study, published in the journal Environmental Pollution, are the experts of the Consolidated Research Group on Large Marine Vertebrates of the UB and IRBio, and the Research Group on Biostatistics and Bioinformatics (GRBIO) of the UB, integrated in the Bioinformatics Barcelona platform (BIB).
Litter that floats and pollutes the ocean
Historically, direct observations (boats, planes, etc.) are the base for the common methodology to assess the impact of floating marine macro-litter (FMML). However, the great ocean area and the volume of data make it hard for the researchers to advance with the monitoring studies.
“Automatic aerial photography techniques combined with analytical algorithms are more efficient protocols for the control and study of this kind of pollutants,” notes Odei Garcia-Garin, first author of the article and member of the CRG on Large Marine Mammals, led by Professor Àlex Aguilar.

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“However,” he continues, “automated remote sensing of these materials is at an early stage. There are several factors in the ocean (waves, wind, clouds, etc.) that harden the detection of floating litter automatically with the aerial images of the marine surface. This is why there are only a few studies that made the effort to work on algorithms to apply to this new research context.”
The experts designed a new algorithm to automate the quantification of floating plastics in the sea through aerial photographs by applying the deep learning techniques, automatic learning methodology with artificial neuronal networks able to learn and take the learning to higher levels.
“The great amount of images of the marine surface obtained by drones and planes in monitoring campaigns on marine litter -also in experimental studies with known floating objects- enabled us to develop and test a new algorithm that reaches a 80% of precision in the remote sensing of floating marine macro-litter,” notes Garcia-Garin, member of the Department of Evolutionary Biology, Ecology and Environmental Sciences of the UB and IRBio.
Preservation of the oceans with deep learning techniques
The new algorithm has been implemented to MARLIT, an open access web app described in the article and which is available to all managers and professionals in the study of the detection and quantification of floating marine macro-litter with aerial images. In particular, this is a proof of concept based on an R Shiny package, a methodological innovation with great interest to speed up the monitoring procedures of floating marine macro-litter.
MARLIT enables the analysis of images individually, as well as to divide them into several segments, according to the user’s guidelines, identify the presence of floating litter in each certain area and estimate their density with the image metadata (height, resolution). In the future, it is expected to adapt the app to a remote sensor (for instance, a drone) to automate the remote sensing process.
At a European level, the EU Marine Strategy Framework Directive indicates the application of FMML monitoring techniques to fulfill the continuous assessment of the environmental state of the marine environment. “Therefore, the automatization of monitoring processes and the use of apps such as MARLIT would ease the member states’ fulfilment of the directive,” conclude the authors of the study. More

A new machine-learning framework uses reported test results and death rates to calculate estimates of the actual number of current COVID-19 infections within all 50 U.S. states and 50 countries. Jungsik Noh and Gaudenz Danuser of the University of Texas Southwestern Medical Center present these findings in the open-access journal PLOS ONE on February 8, 2021.
During the ongoing pandemic, U.S. states and many countries have reported daily counts of COVID-19 infections and deaths confirmed by testing. However, many infections have gone undetected, resulting in under-counting of the total number of people currently infected at any given point in time — an important metric to guide public health efforts.
Now, Noh and Danuser have developed a computational model that uses machine-learning strategies to estimate the actual daily number of current infections for all 50 U.S. states and the 50 most-infected countries. To make the calculations, the model draws on previously published pandemic parameters and publicly available daily data on confirmed cases and deaths. Visualizations of these daily estimates are freely available online.
The model’s estimates indicate severe undercounting of cases across the U.S. and worldwide. The cumulative number of actual cases in 9 out of 50 countries is estimated to be at least five times higher than confirmed cases. Within the U.S., estimates of the cumulative number of actual cases within states were in line with the results of an antibody testing study conducted in 46 states.
For some countries, such as the U.S., Belgium, and the U.K., estimates indicate that more than 20 percent of the total population has experienced infection. As of January 31, 2021, some U.S. states — including Pennsylvania, Arizona, and Florida — have currently active cases totaling more than 5 percent of the state’s entire population. In Washington, the active cases were estimated to be one percent of the population that day.
Looking ahead, the model has been estimating current COVID-19 case counts within communities, which could help inform contact-tracing and other public health efforts.
The authors add: “Given that the confirmed cases only capture the tip of the iceberg in the middle of the pandemic, the estimated sizes of current infections in this study provide crucial information to determine the regional severity of COVID-19 that can be misguided by the confirmed cases.”

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Materials provided by PLOS. Note: Content may be edited for style and length. More

Cold Spring Harbor Laboratory (CSHL) Assistant Professor Peter Koo and collaborator Matt Ploenzke reported a way to train machines to predict the function of DNA sequences. They used “neural nets,” a type of artificial intelligence (AI) typically used to classify images. Teaching the neural net to predict the function of short stretches of DNA allowed it to work up to deciphering larger patterns. The researchers hope to analyze more complex DNA sequences that regulate gene activity critical to development and disease.
Machine-learning researchers can train a brain-like “neural net” computer to recognize objects, such as cats or airplanes, by showing it many images of each. Testing the success of training requires showing the machine a new picture of a cat or an airplane and seeing if it classifies it correctly. But, when researchers apply this technology to analyzing DNA patterns, they have a problem. Humans can’t recognize the patterns, so they may not be able to tell if the computer identifies the right thing. Neural nets learn and make decisions independently of their human programmers. Researchers refer to this hidden process as a “black box.” It is hard to trust the machine’s outputs if we don’t know what is happening in the box.
Koo and his team fed DNA (genomic) sequences into a specific kind of neural network called a convolutional neural network (CNN), which resembles how animal brains process images. Koo says:
“It can be quite easy to interpret these neural networks because they’ll just point to, let’s say, whiskers of a cat. And so that’s why it’s a cat versus an airplane. In genomics, it’s not so straightforward because genomic sequences aren’t in a form where humans really understand any of the patterns that these neural networks point to.”
Koo’s research, reported in the journal Nature Machine Intelligence, introduced a new method to teach important DNA patterns to one layer of his CNN. This allowed his neural network to build on the data to identify more complex patterns. Koo’s discovery makes it possible to peek inside the black box and identify some key features that lead to the computer’s decision-making process.
But Koo has a larger purpose in mind for the field of artificial intelligence. There are two ways to improve a neural net: interpretability and robustness. Interpretability refers to the ability of humans to decipher why machines give a certain prediction. The ability to produce an answer even with mistakes in the data is called robustness. Usually, researchers focus on one or the other. Koo says:
“What my research is trying to do is bridge these two together because I don’t think they’re separate entities. I think that we get better interpretability if our models are more robust.”
Koo hopes that if a machine can find robust and interpretable DNA patterns related to gene regulation, it will help geneticists understand how mutations affect cancer and other diseases.

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Materials provided by Cold Spring Harbor Laboratory. Original written by Jasmine Lee. Note: Content may be edited for style and length. More

Researchers have identified a new form of magnetism in so-called magnetic graphene, which could point the way toward understanding superconductivity in this unusual type of material.
The researchers, led by the University of Cambridge, were able to control the conductivity and magnetism of iron thiophosphate (FePS3), a two-dimensional material which undergoes a transition from an insulator to a metal when compressed. This class of magnetic materials offers new routes to understanding the physics of new magnetic states and superconductivity.
Using new high-pressure techniques, the researchers have shown what happens to magnetic graphene during the transition from insulator to conductor and into its unconventional metallic state, realised only under ultra-high pressure conditions. When the material becomes metallic, it remains magnetic, which is contrary to previous results and provides clues as to how the electrical conduction in the metallic phase works. The newly discovered high-pressure magnetic phase likely forms a precursor to superconductivity so understanding its mechanisms is vital.
Their results, published in the journal Physical Review X, also suggest a way that new materials could be engineered to have combined conduction and magnetic properties, which could be useful in the development of new technologies such as spintronics, which could transform the way in which computers process information.
Properties of matter can alter dramatically with changing dimensionality. For example, graphene, carbon nanotubes, graphite and diamond are all made of carbon atoms, but have very different properties due to their different structure and dimensionality.
“But imagine if you were also able to change all of these properties by adding magnetism,” said first author Dr Matthew Coak, who is jointly based at Cambridge’s Cavendish Laboratory and the University of Warwick. “A material which could be mechanically flexible and form a new kind of circuit to store information and perform computation. This is why these materials are so interesting, and because they drastically change their properties when put under pressure so we can control their behaviour.”
In a previous study by Sebastian Haines of Cambridge’s Cavendish Laboratory and the Department of Earth Sciences, researchers established that the material becomes a metal at high pressure, and outlined how the crystal structure and arrangement of atoms in the layers of this 2D material change through the transition.

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“The missing piece has remained however, the magnetism,” said Coak. “With no experimental techniques able to probe the signatures of magnetism in this material at pressures this high, our international team had to develop and test our own new techniques to make it possible.”
The researchers used new techniques to measure the magnetic structure up to record-breaking high pressures, using specially designed diamond anvils and neutrons to act as the probe of magnetism. They were then able to follow the evolution of the magnetism into the metallic state.
“To our surprise, we found that the magnetism survives and is in some ways strengthened,” co-author Dr Siddharth Saxena, group leader at the Cavendish Laboratory. “This is unexpected, as the newly-freely-roaming electrons in a newly conducting material can no longer be locked to their parent iron atoms, generating magnetic moments there — unless the conduction is coming from an unexpected source.”
In their previous paper, the researchers showed these electrons were ‘frozen’ in a sense. But when they made them flow or move, they started interacting more and more. The magnetism survives, but gets modified into new forms, giving rise to new quantum properties in a new type of magnetic metal.
How a material behaves, whether conductor or insulator, is mostly based on how the electrons, or charge, move around. However, the ‘spin’ of the electrons has been shown to be the source of magnetism. Spin makes electrons behave a bit like tiny bar magnets and point a certain way. Magnetism from the arrangement of electron spins is used in most memory devices: harnessing and controlling it is important for developing new technologies such as spintronics, which could transform the way in which computers process information.

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“The combination of the two, the charge and the spin, is key to how this material behaves,” said co-author Dr David Jarvis from the Institut Laue-Langevin, France, who carried out this work as the basis of his PhD studies at the Cavendish Laboratory. “Finding this sort of quantum multi-functionality is another leap forward in the study of these materials.”
“We don’t know exactly what’s happening at the quantum level, but at the same time, we can manipulate it,” said Saxena. “It’s like those famous ‘unknown unknowns’: we’ve opened up a new door to properties of quantum information, but we don’t yet know what those properties might be.”
There are more potential chemical compounds to synthesise than could ever be fully explored and characterised. But by carefully selecting and tuning materials with special properties, it is possible to show the way towards the creation of compounds and systems, but without having to apply huge amounts of pressure.
Additionally, gaining fundamental understanding of phenomena such as low-dimensional magnetism and superconductivity allows researchers to make the next leaps in materials science and engineering, with particular potential in energy efficiency, generation and storage.
As for the case of magnetic graphene, the researchers next plan to continue the search for superconductivity within this unique material. “Now that we have some idea what happens to this material at high pressure, we can make some predictions about what might happen if we try to tune its properties through adding free electrons by compressing it further,” said Coak.
“The thing we’re chasing is superconductivity,” said Saxena. “If we can find a type of superconductivity that’s related to magnetism in a two-dimensional material, it could give us a shot at solving a problem that’s gone back decades.” More

An important class of challenging computational problems, with applications in graph theory, neural networks, artificial intelligence and error-correcting codes can be solved by multiplying light signals, according to researchers from the University of Cambridge and Skolkovo Institute of Science and Technology in Russia.
In a paper published in the journal Physical Review Letters, they propose a new type of computation that could revolutionise analogue computing by dramatically reducing the number of light signals needed while simplifying the search for the best mathematical solutions, allowing for ultra-fast optical computers.
Optical or photonic computing uses photons produced by lasers or diodes for computation, as opposed to classical computers which use electrons. Since photons are essentially without mass and can travel faster than electrons, an optical computer would be superfast, energy-efficient and able to process information simultaneously through multiple temporal or spatial optical channels.
The computing element in an optical computer — an alternative to the ones and zeroes of a digital computer — is represented by the continuous phase of the light signal, and the computation is normally achieved by adding two light waves coming from two different sources and then projecting the result onto ‘0’ or ‘1’ states.
However, real life presents highly nonlinear problems, where multiple unknowns simultaneously change the values of other unknowns while interacting multiplicatively. In this case, the traditional approach to optical computing that combines light waves in a linear manner fails.
Now, Professor Natalia Berloff from Cambridge’s Department of Applied Mathematics and Theoretical Physics and PhD student Nikita Stroev from Skolkovo Institute of Science and Technology have found that optical systems can combine light by multiplying the wave functions describing the light waves instead of adding them and may represent a different type of connections between the light waves.

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They illustrated this phenomenon with quasi-particles called polaritons — which are half-light and half-matter — while extending the idea to a larger class of optical systems such as light pulses in a fibre. Tiny pulses or blobs of coherent, superfast-moving polaritons can be created in space and overlap with one another in a nonlinear way, due to the matter component of polaritons.
“We found the key ingredient is how you couple the pulses with each other,” said Stroev. “If you get the coupling and light intensity right, the light multiplies, affecting the phases of the individual pulses, giving away the answer to the problem. This makes it possible to use light to solve nonlinear problems.”
The multiplication of the wave functions to determine the phase of the light signal in each element of these optical systems comes from the nonlinearity that occurs naturally or is externally introduced into the system.
“What came as a surprise is that there is no need to project the continuous light phases onto ‘0’ and ‘1’ states necessary for solving problems in binary variables,” said Stroev. “Instead, the system tends to bring about these states at the end of its search for the minimum energy configuration. This is the property that comes from multiplying the light signals. On the contrary, previous optical machines require resonant excitation that fixes the phases to binary values externally.”
The authors have also suggested and implemented a way to guide the system trajectories towards the solution by temporarily changing the coupling strengths of the signals.
“We should start identifying different classes of problems that can be solved directly by a dedicated physical processor,” said Berloff. “Higher-order binary optimisation problems are one such class, and optical systems can be made very efficient in solving them.”
There are still many challenges to be met before optical computing can demonstrate its superiority in solving hard problems in comparison with modern electronic computers: noise reduction, error correction, improved scalability, guiding the system to the true best solution are among them.
“Changing our framework to directly address different types of problems may bring optical computing machines closer to solving real-world problems that cannot be solved by classical computers,” said Berloff. More

The lung is a complex organ whose main function is to exchange gases. It is the largest organ in the human body and plays a key role in the oxygenation of all the organs. Due to its structure, cellular composition and dynamic microenvironment, is difficult to mimic in vitro.
A specialized laboratory of the ARTORG Center for Biomedical Engineering Research, University of Bern, headed by Olivier Guenat has developed a new generation of in-vitro models called organs-on-chip for over 10 years, focusing on modeling the lung and its diseases. After a first successful lung-on-chip system exhibiting essential features of the lung, the Organs-on-Chip (OOC) Technologies laboratory has now developed a purely biological next-generation lung-on-chip in collaboration with the Helmholtz Centre for Infection Research in Germany and the Thoracic Surgery and Pneumology Departments at Inselspital.
A fully biodegradable life-sized air-blood-barrier
Pauline Zamprogno, who developed the new model for her PhD thesis at the OOC, summarizes its characteristics: “The new lung-on-chip reproduces an array of alveoli with in vivo like dimensions. It is based on a thin, stretchable membrane, made with molecules naturally found in the lung: collagen and elastin. The membrane is stable, can be cultured on both sides for weeks, is biodegradable and its elastic properties allow mimicking respiratory motions by mechanically stretching the cells.”
By contrast to the first generation, which was also built by the team around Olivier Guenat, the developed system reproduces key aspects of the lung extracellular matrix (ECM): Its composition (cells support made of ECM proteins), its structure (array of alveoli with dimension similar to those found in vivo + fiber structure) and its properties (biodegradability, a key aspect to investigating barrier remodeling during lung diseases such as IPF or COPD). Additionally, the fabrication process is simple and less cumbersome than that of a polydimethylsiloxane stretchable porous membrane from the first-generation lung-on-chip.
Broad potential clinical applications
Cells to be cultured on the new chip for research are currently obtained from cancer patients undergoing lung resections at the Inselspital Department of Thoracic Surgery. Department Head Ralph Schmid sees a double advantage in the system: “The second generation lung-on-chip can be seeded with either healthy or diseased lung alveolar cells. This provides clinicians with both a better understanding of the lung’s physiology and a predictive tool for drug screening and potentially also for precision medicine, identifying the specific therapy with the best potential of helping a particular patient.”

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“The applications for such membranes are broad, from basic science investigations into lung functionalities and pathologies, to identifying new pathways, and to a more efficient discovery of potential new therapies,” says Thomas Geiser, Head of the Department of Pneumology at the Inselspital and Director of Teaching and Research of the Insel Gruppe.
Powerful alternative to animal models in research
As an additional plus, the new lung-on-chip can reduce the need for pneumological research based on animal models. “Many promising drug candidates successfully tested in preclinical models on rodents have failed when tested in humans due to differences between the species and in the expression of a lung disease,” explains Olivier Guenat. “This is why, in the long term, we aim to reduce animal testing and provide more patient-relevant systems for drug screening with the possibility of tailoring models to specific patients (by seeding organs-on-chip with their own cells).”
The new biological lung-on-chip will be further developed by Pauline Zamprogno and her colleagues from the OOC Technologies group to mimic a lung with idiopathic pulmonary fibrosis (IPF), a chronic disease of the lung leading to progressive scarring of the lung tissue within the framework of a research project funded by the Swiss 3R Competence Center (3RCC). “My new project consists in the development of an IPF-on- chip model based on the biological membrane. So far, we have develop a healthy air-blood barrier. Now it’s time to use it to investigate a real biological question,” says Zamprogno.
Research group Organs-On-Chip Technologies of the ARTORG Center
This specialized group of the ARTORG Center for Biomedical Engineering Research develops organs-on-chip, focusing on the lung and its diseases, in collaboration with the Departments of Pulmonary Medicine and Thoracic Surgery of the Inselspital. The group combines engineering, in particular microfluidics and microfabrication, cell biology and tissue engineering methods, material sciences and medicine.
Their first development of a breathing lung-on-chip is further developed in collaboration with the start-up AlevoliX, with the aim to revolutionize preclinical research. Recently the group has developed an entirely biological second-generation lung-on-chip focusing on recreating the air-blood barrier of the lung. A second research direction aims at developing a functional lung microvasculature. Here, lung endothelial cells are seeded in a micro-engineered environment, where they self-assemble to build a network of perfusable and contractile microvessels of only a few tens of micrometers in diameter.
Next to pharmaceutical applications, organs-on-chip are seen as having the potential to be used in precision medicine to test the patient’s own cells in order to tailor the best therapy. Furthermore, such systems have the significant potential to reduce animal testing in medical and life-science research. The OOC group operates the Organs-on-Chip Facility, providing scientists from the University of Bern, the University Hospital of Bern and beyond an infrastructure and equipment to produce microfluidic devices and test organs-on-chips. More

Online searches for mobile and isolated activities can help to predict later surges and declines in COVID-19 cases, a team of researchers has found. Its findings, based on a four-month analysis of online searches, offer a potential means to anticipate the pathways of the pandemic — before new infections are reported.
“This is a first step towards building a tool that can help predict COVID case surges by capturing higher-risk activities and intended mobility, which searches for gyms and in-person dining can illuminate,” says Anasse Bari, a clinical assistant professor in computer science at New York University’s Courant Institute of Mathematical Sciences and one of the authors of the paper, which appears in the journal Social Network Analysis and Mining. “Using such ‘alternative data’ is nothing new and has been applied for other purposes — for instance, alternative data has been used in finance to generate data-driven investments, such as studying satellite images of cars in parking lots to predict businesses earnings.”
“Our research shows the same techniques could be applied to combatting a pandemic by spotting, ahead of time, where outbreaks are likely to occur,” adds Megan Coffee, a clinical assistant professor in the Division of Infectious Disease & Immunology at NYU Grossman School of Medicine. “Developing a barometer of behavior would, with further work and validation, allow policymakers and epidemiologists to track the impact of social interventions and brace for rising surges.”
The research also showed an association between intended activities outside the home after lockdown restrictions were lifted, pointing to how the effects of policy decisions can be measured using alternative data.
Since the onset of the pandemic, governments have restricted activities, often based on surges of COVID-19 cases, then loosened these restrictions after declines. However, these actions are in response to infection rates and are designed to limit the spread of future cases.
In the Social Network Analysis and Mining study, the researchers sought to determine if there were ways to spot behaviors known to be risky during the pandemic (e.g., visits to barbershops and nail salons) ahead of local and regional outbreaks — and conversely, identify behaviors known to be less risky (e.g., exercising at home) prior to declines in coronavirus cases.

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“Our goal was to capture the underlying social dynamics of an unprecedented pandemic using alternative data sources that are new to infectious disease epidemiology,” explains Bari. “When someone searches the closing time of a local bar or looks up directions to a local gym, they give some insight into what future risks they may have.”
To examine this, they studied online searches from March through June in 2020 in all 50 states. Here, they divided searches into two categories — or “tracks”: a mobility index track, which categorized searches linked to interactions with others outside the home (e.g., “theaters near me,” “flight tickets”), and an isolation index track, which categorized searches linked to activities done at home (“food delivery,” “at-home yoga”).
The team’s choice of search keywords was informed by a recent Democracy Fund + UCLA Nationscape survey that tracked activities individuals reported they would prioritize attending if “restrictions were lifted on the advice of public health officials regarding activities.” The most popular results included “going to a stadium/concert,” “going to the movies,” and “attending a sports event.”
Using Google Trends data, the researchers tracked search trends related to mobility and isolation to develop mobility and isolation indexes. They complemented these with a “net movement index,” which was the difference between the mobility index and the isolation index.
The researchers then looked at COVID-19 case growth 10 to 14 days later — the expected lag between exposure and symptoms — at the state level by examining data from state and local health agencies.

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Overall, they found that the net movement index correlated with new COVID-19 cases — reported weekly — in 42 of 50 states over the studied period (March-June 2020).
The researchers also looked more closely at five states (Arizona, California, Florida, New York, and Texas) to determine the impact of the ending of stay-at-home orders on searches. In all of these states, the mobility index, which decreased during the initial lockdown phase, increased as re-openings began. Subsequently, COVID-19 cases rose again nationwide in June 2020 and surged in Arizona, California, Florida, and Texas.
By contrast, an earlier sharp decline in mobility indices was followed by a sharp decline in the case growth data in these same five states.
“From this work, we hope to build a knowledge base on human behavior change from alternative data during the life cycle of the pandemic in order to allow machine learning to predict behavior in future epidemics,” says Aashish Khubchandani, an NYU undergraduate and one of the paper’s authors.
The researchers recognize that search-based methods to predict infection outbreaks raise privacy concerns. However, they emphasize that their tool uses large volumes of search queries, not individual ones, and relies on anonymized data in order to offer health-related projections.
The paper’s other authors were Courant researchers Matthias Heymann and Junzhang Wang, who are part of the Predictive Analytics and AI research lab at the Courant Institute.
The study was supported, in part, by an Amazon AI research grant and an NYU COVID-19 Catalyst Research grant. More

The modern world is powered by electrical circuitry on a “chip” — the semiconductor chip underpinning computers, cell phones, the internet, and other applications. In the year 2025, humans are expected to be creating 175 zettabytes (175trillion gigabytes) of new data. How can we ensure the security of sensitive data at such a high volume? And how can we address grand-challenge-like problems, from privacy and security to climate change, leveraging this data, especially given the limited capability of current computers?
A promising alternative is emerging quantum communication and computation technologies. For this to happen, however, it will require the widespread development of powerful new quantum optical circuits; circuits that are capable of securely processing the massive amounts of information we generate every day. Researchers in USC’s Mork Family Department of Chemical Engineering and Materials Science have made a breakthrough to help enable this technology.
While a traditional electrical circuit is a pathway along which electrons from an electric charge flow, a quantum optical circuit uses light sources that generate individual light particles, or photons, on-demand, one-at-a-time, acting as information carrying bits (quantum bits or qubits). These light sources are nano-sized semiconductor “quantum dots”-tiny manufactured collections of tens of thousands to a million atoms packed within a volume of linear size less than a thousandth of the thickness of typical human hair buried in a matrix of another suitable semiconductor.
They have so far been proven to be the most versatile on-demand single photon generators. The optical circuit requires these single photon sources to be arranged on a semiconductor chip in a regular pattern. Photons with nearly identical wavelength from the sources must then be released in a guided direction. This allows them to be manipulated to form interactions with other photons and particles to transmit and process information.
Until now, there has been a significant barrier to the development of such circuits. For example, in current manufacturing techniques quantum dots have different sizes and shapes and assemble on the chip in random locations. The fact that the dots have different sizes and shapes mean that the photons they release do not have uniform wavelengths. This and the lack of positional order make them unsuitable for use in the development of optical circuits.
In recently published work, researchers at USC have shown that single photons can indeed be emitted in a uniform way from quantum dots arranged in a precise pattern. It should be noted that the method of aligning quantum dots was first developed at USC by the lead PI, Professor Anupam Madhukar, and his team nearly thirty years ago, well before the current explosive research activity in quantum information and interest in on-chip single-photon sources. In this latest work, the USC team has used such methods to create single-quantum dots, with their remarkable single-photon emission characteristics. It is expected that the ability to precisely align uniformly-emitting quantum dots will enable the production of optical circuits, potentially leading to novel advancements in quantum computing and communications technologies.

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The work, published in APL Photonics, was led by Jiefei Zhang, currently a research assistant professor in the Mork Family Department of Chemical Engineering and Materials Science, with corresponding author Anupam Madhukar, Kenneth T. Norris Professor in Engineering and Professor of Chemical Engineering, Electrical Engineering, Materials Science, and Physics.
“The breakthrough paves the way to the next steps required to move from lab demonstration of single photon physics to chip-scale fabrication of quantum photonic circuits,” Zhang said. “This has potential applications in quantum (secure) communication, imaging, sensing and quantum simulations and computation.”
Madhukar said that it is essential that quantum dots be ordered in a precise way so that photons released from any two or more dots can be manipulated to connect with each other on the chip. This will form the basis of building unit for quantum optical circuits.
“If the source where the photons come from is randomly located, this can’t be made to happen.” Madhukar said.
“The current technology that is allowing us to communicate online, for instance using a technological platform such as Zoom, is based on the silicon integrated electronic chip. If the transistors on that chip are not placed in exact designed locations, there would be no integrated electrical circuit,” Madhukar said. “It is the same requirement for photon sources such as quantum dots to create quantum optical circuits.”
The research is supported by the Air Force Office of Scientific Research (AFOSR) and the U.S. Army Research Office (ARO).

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“This advance is an important example of how solving fundamental materials science challenges, like how to create quantum dots with precise position and composition, can have big downstream implications for technologies like quantum computing,” said Evan Runnerstrom, program manager, Army Research Office, an element of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory. “This shows how ARO’s targeted investments in basic research support the Army’s enduring modernization efforts in areas like networking.”
To create the precise layout of quantum dots for the circuits, the team used a method called SESRE (substrate-encoded size-reducing epitaxy) developed in the Madhukar group in the early 1990s. In the current work, the team fabricated regular arrays of nanometer-sized mesas with a defined edge orientation, shape (sidewalls) and depth on a flat semiconductor substrate, composed of gallium arsenide (GaAs). Quantum dots are then created on top of the mesas by adding appropriate atoms using the following technique.
First, incoming gallium (Ga) atoms gather on the top of the nanoscale mesas attracted by surface energy forces, where they deposit GaAs. Then, the incoming flux is switched to indium (In) atoms, to in turn deposit indium arsenide (InAs) followed back by Ga atoms to form GaAs and hence create the desired individual quantum dots that end up releasing single photons. To be useful for creating optical circuits, the space between the pyramid-shaped nano-mesas needs to be filled by material that flattens the surface. The final chip where opaque GaAs is depicted as a translucent overlayer under which the quantum dots are located.
“This work also sets a new world-record of ordered and scalable quantum dots in terms of the simultaneous purity of single-photon emission greater than 99.5%, and in terms of the uniformity of the wavelength of the emitted photons, which can be as narrow as 1.8nm, which is a factor of 20 to 40 better than typical quantum dots,” Zhang said.
Zhang said that with this uniformity, it becomes feasible to apply established methods such as local heating or electric fields to fine-tune the photon wavelengths of the quantum dots to exactly match each other, which is necessary for creating the required interconnections between different quantum dots for circuits.
This means that for the first time researchers can create scalable quantum photonic chips using well-established semiconductor processing techniques. In addition, the team’s efforts are now focused on establishing how identical the emitted photons are from the same and/or from different quantum dots. The degree of indistinguishability is central to quantum effects of interference and entanglement, that underpin quantum information processing -communication, sensing, imaging, or computing.
Zhang concluded: “We now have an approach and a material platform to provide scalable and ordered sources generating potentially indistinguishable single-photons for quantum information applications. The approach is general and can be used for other suitable material combinations to create quantum dots emitting over a wide range of wavelengths preferred for different applications, for example fiber-based optical communication or the mid-infrared regime, suited for environmental monitoring and medical diagnostics,” Zhang said.
Gernot S. Pomrenke, AFOSR Program Officer, Optoelectronics and Photonics said that reliable arrays of on-demand single photon sources on-chip were a major step forward.
“This impressive growth and material science work stretches over three decades of dedicated effort before research activities in quantum information were in the mainstream,” Pomrenke said. “Initial AFOSR funding and resources from other DoD agencies have been critical in realizing the challenging work and vision by Madhukar, his students, and collaborators. There is a great likelihood that the work will revolutionize the capabilities of data centers, medical diagnostics, defense and related technologies.” More