Computers Math

Researchers from the University of Sussex and Universal Quantum have demonstrated for the first time that quantum bits (qubits) can directly transfer between quantum computer microchips and demonstrated this with record-breaking speed and accuracy. This breakthrough resolves a major challenge in building quantum computers large and powerful enough to tackle complex problems that are of critical importance to society.
Today, quantum computers operate on the 100-qubit scale. Experts anticipate millions of qubits are required to solve important problems that are out of reach of today’s most powerful supercomputers [1, 2]. There is a global quantum race to develop quantum computers that can help in many important societal challenges from drug discovery to making fertilizer production more energy efficient and solving important problems in nearly every industry, ranging from aeronautics to the financial sector.
In the research paper, published today in Nature Communications, the scientists demonstrate how they have used a new and powerful technique, which they dub ‘UQ Connect’, to use electric field links to enable qubits to move from one quantum computing microchip module to another with unprecedented speed and precision. This allows chips to slot together like a jigsaw puzzle to make a more powerful quantum computer.
The University of Sussex and Universal Quantum team were successful in transporting the qubits with a 99.999993% success rate and a connection rate of 2424/s, both numbers are world records and orders of magnitude better than previous solutions.
Professor Winfried Hensinger, Professor of Quantum Technologies at the University of Sussex and Chief Scientist and Co-founder at Universal Quantum said: “As quantum computers grow, we will eventually be constrained by the size of the microchip, which limits the number of quantum bits such a chip can accommodate. As such, we knew a modular approach was key to make quantum computers powerful enough to solve step-changing industry problems. In demonstrating that we can connect two quantum computing chips — a bit like a jigsaw puzzle — and, crucially, that it works so well, we unlock the potential to scale-up by connecting hundreds or even thousands of quantum computing microchips.”
While linking the modules at world-record speed, the scientists also verified that the ‘strange’ quantum nature of the qubit remains untouched during transport, for example, that the qubit can be both 0 and 1 at the same time.

Dr Sebastian Weidt, CEO and Co-founder of Universal Quantum, and Senior Lecturer in Quantum Technologies at the University of Sussex said: “Our relentless focus is on providing people with a tool that will enable them to revolutionise their field of work. The Universal Quantum and University of Sussex teams have done something truly incredible here that will help make our vision a reality. These exciting results show the remarkable potential of Universal Quantum’s quantum computers to become powerful enough to unlock the many lifechanging applications of quantum computing.”
Universal Quantum has just been awarded €67 million from the German Aerospace Center (DLR) to build two quantum computers where they will deploy this technology as part of the contract. The University of Sussex spin-out was also recently named as one of the 2022 Institute of Physics award winners in the Business Start-up category.
Weidt added: “The DLR contract was likely one of the largest government quantum computing contracts ever handed out to a single company. This is a huge validation of our technology. Universal Quantum is now working hard to deploy this technology in our upcoming commercial machines.”
Dr Mariam Akhtar led the research during her time as Research Fellow at the University of Sussex and Quantum Advisor at Universal Quantum. She said: “The team has demonstrated fast and coherent ion transfer using quantum matter links. This experiment validates the unique architecture that Universal Quantum has been developing — providing an exciting route towards truly large-scale quantum computing.”
Professor Sasha Roseneil, Vice-Chancellor of the University of Sussex, said: “It’s fantastic to see that the inspired work of the University of Sussex and Universal Quantum physicists has resulted in this phenomenal breakthrough, taking us a significant step closer to a quantum computer that will be of real societal use. These computers are set to have boundless applications — from improving the development of medicines, creating new materials, to maybe even unlocking solutions to the climate crisis. The University of Sussex is investing significantly in quantum computing to support our bold ambition to host the world’s most powerful quantum computers and create change that has the potential to positively impact so many people across the world. And with teams spanning the spectrum of quantum computing and technology research, the University of Sussex has both a breadth and a depth of expertise in this. We are still growing our research and teaching in this area, with plans for new teaching programmes, and new appointments.”
Professor Keith Jones, Interim Provost and Pro-Vice Chancellor for Research and Enterprise at the University of Sussex, said of the development: “This is a very exciting finding from our University of Sussex physicists and Universal Quantum. It proves the value and dynamism of this University of Sussex spin-out company, whose work is grounded in rigorous and world-leading academic research. Quantum computers will be pivotal in helping to solve some of the most pressing global issues. We’re delighted that Sussex academics are delivering research that offers hope in realising the positive potential of next-generation quantum technology in crucial areas such as sustainability, drug development, and cybersecurity.”
NOTES
[1] Webber, M., et. al. AVS Quantum Sci. 4, 013801 (2022)
[2] Lekitsch, B., et al., Science Advances, 3(2), 1-12 (2017) More

How do you build complex structures for housing cells using a material as soft as jelly? Rice University scientists have the answer, and it represents a potential leap forward for regenerative medicine and medical research in general.
Researchers in the lab of Rice’s Jeffrey Hartgerink have figured out how to 3D-print the well-defined structures using a self-assembling peptide ink. “Eventually, the goal is to print structures with cells and grow mature tissue in a petri dish. These tissues can then be transplanted to treat injuries, or used to learn about how an illness works and to test drug candidates,” said Adam Farsheed, a Rice bioengineering graduate student and lead author of the study, which appeared in Advanced Materials.
“There are 20 naturally occurring amino acids that make up proteins in the human body,” Farsheed said. “Amino acids can be linked together into larger chains, like Lego blocks. When amino acid chains are longer than 50 amino acids, they are called proteins, but when these chains are shorter than 50 amino acids they are called peptides. In this work, we used peptides as our base material in our 3D-printing inks.”
Developed by Hartgerink and collaborators, these “multidomain peptides” are designed to be hydrophobic on one side and hydrophilic on the other. When placed in water, “one of the molecules will flip itself on top of another, creating what we call a hydrophobic sandwich,” Farsheed said.
These sandwiches stack onto one another and form long fibers, which then form a hydrogel, a water-based material with a gelatinous texture that can be useful for a wide range of applications such as tissue engineering, soft robotics and wastewater treatment.
Multidomain peptides have been used for nerve regeneration, cancer treatment and wound healing, and have been shown to promote high levels of cell infiltration and tissue development when implanted in living organisms.

“We know that the multidomain peptides can safely be implanted in the body,” Farsheed said. “But what I was looking to do in this project was to go in a different direction and show that these peptides are a great 3D-printing ink.
“It might be counterintuitive since our material is so soft, but I recognized that our multidomain peptides are an ideal ink candidate because of the way they self-assemble,” he continued. “Our material can reassemble after being deformed, similar to how toothpaste forms a nice fiber when pushed out of a tube.”
Farsheed’s mechanical engineering background allowed him to take an unconventional approach when testing his hypothesis.
“I had more of a brute-force engineering approach where instead of chemically modifying the material to make it more amenable to 3D printing, I tested to see what would happen if I simply added more material,” he said. “I increased the concentration about fourfold, and it worked extremely well.
“There have been only a handful of attempts to 3D-print using other self-assembling peptides, and that work is all great, but this is the first time that any self-assembling peptide system has been used to successfully 3D-print such complex structures,” Farsheed continued.
The structures were printed with either positively charged or negatively charged multidomain peptides, and immature muscle cells placed on the structures behaved differently depending on the charge. Cells remained balled up on the substrate with a negative charge, while on the positively charged material the cells spread out and began to mature.
“It shows that we can control cell behavior using both structural and chemical complexity,” Farsheed said.
Hartgerink is a professor of chemistry and bioengineering and associate chair for undergraduate studies. Farsheed is a bioengineering graduate student and lead author on the study. Additional study co-authors are undergraduate student Adam Thomas and graduate student Brett Pogostin.
The National Institutes of Health (R01 DE021798) and the National Science Foundation Graduate Research Fellowships Program supported the research. More

A synthetic biosensor that mimics properties found in cell membranes and provides an electronic readout of activity could lead to a better understanding of cell biology, development of new drugs, and the creation of sensory organs on a chip capable of detecting chemicals, similar to how noses and tongues work.
A study, “Cell-Free Synthesis Goes Electric: Dual Optical and Electronic Biosensor vie Direct Channel Integration into a Supported Membrane Electrode,” was published Jan. 18 in the Synthetic Biology journal of the American Chemical Society.
The bioengineering feat described in the paper uses synthetic biology to re-create a cell membrane and its embedded proteins, which are gatekeepers of cellular functions. A conducting sensing platform allows for an electronic readout when a protein is activated. Being able to test if and how a molecule reacts with proteins in a cell membrane could generate a great many applications.
But embedding membrane proteins into sensors had been notoriously difficult until the study’s authors combined bioelectronic sensors with a new approach to synthesize proteins.
“This technology really allows us to study these proteins in ways that would be incredibly challenging, if not impossible, with current technology,” said first author Zachary Manzer, a doctoral student in the lab of senior author Susan Daniel, the Fred H. Rhodes Professor and director of the Robert Frederick Smith School of Chemical and Biomolecular Engineering at Cornell Engineering.
Proteins within cell membranes serve many important functions, including communicating with the environment, catalyzing chemical reactions, and moving compounds and ions across the membranes. When a membrane protein receptor is activated, charged ions move across a membrane channel, triggering a function in the cell. For example, brain neurons or muscle cells fire when cues from nerves signal charged calcium-ion channels to open.

The researchers have created a biosensor that starts with a conducting polymer, which is soft and easy to work with, on top of a support that together act as an electric circuit that is monitored by a computer. A layer of lipid (fat) molecules, which forms the membrane, lies on top of the polymer, and the proteins of interest are placed within the lipids.
In this proof of concept, the researchers have created a cell-free platform that allowed them to synthesize a model protein directly into this artificial membrane. The system has a dual readout technology built in. Since the components of the sensor are transparent, researchers can use optical techniques, such as engineering proteins that fluoresce when activated, which allows scientists to study the fundamentals via microscope, and observe what happens to the protein itself during a cellular process. They can also record electronic activity to see how the protein is functioning through clever circuit design.
“This really is the first demonstration of leveraging cell-free synthesis of transmembrane proteins into biosensors,” Daniel said. “There’s no reason why we wouldn’t be able to express many different kinds of proteins into this general platform.”
Currently, researchers have used proteins grown and extracted from living cells for similar applications, but given this advance, users won’t have to grow proteins in cells and then harvest and embed them in the membrane platform. Instead, they can synthesize them directly from DNA, the basic template for proteins.
“We can bypass the whole process of the cell as the factory that produces the protein,” Daniel said, “and biomanufacture the proteins ourselves.”
With such a system, a drug chemist interested in a particular protein implicated in a disease might flow potentially therapeutic molecules across that protein to see how it responds. Or a scientist looking to create an environmental sensor could place on the platform a particular protein that is sensitive to a chemical or pollutant, such as those found in lake water.

“If you think of your nose, or your tongue, every time you smell or taste something, ion channels are firing,” Manzer said. Scientists may now take the proteins being activated when we smell something and translate the results into this electronic system to sense things that might be undetectable with a chemical sensor.”
The new sensor opens the door for pharmacologists to research how to create non-opioid pain medicines, or drugs to treat Alzheimer’s or Parkinson’s disease, which interact with cell membrane proteins.
Surajit Ghosh, a postdoctoral researcher in Daniel’s lab, is a co-first author. Neha Kamat, assistant professor of biomedical engineering at Northwestern University, is a senior co-author of the paper.
The study was funded by the National Science Foundation, the Air Force Office of Scientific Research, the American Heart Association, the National Institute of General Medical Sciences and the Defense Advanced Research Projects Agency. More

Parents: It might be time to rethink your family’s video-gaming rules.
New research findings challenge the fears parents have been hearing for years that children who spend hour after hour playing video games, or choose games of certain genres, would manifest unhealthy results in their cognitive ability.
“Our studies turned up no such links, regardless of how long the children played and what types of games they chose,” said Jie Zhang, associate professor of curriculum and instruction at the University of Houston College of Education and a member of the research team. The work is published in the Journal of Media Psychology.
In reaching the conclusions, researchers examined the video gaming habits of 160 diverse urban public-school preteen students (70% from lower income households), which represents an age group less studied in previous research. Participating students reported playing video games an average of 2.5 hours daily, with the group’s heaviest gamers putting in as much as 4.5 hours each day.
The team looked for association between the students’ video game play and their performance on the standardized Cognitive Ability Test 7, known as CogAT, which evaluates verbal, quantitative and nonverbal/spatial skills. CogAT was chosen as a standard measure, in contrast to the teacher-reported grades or self-reported learning assessments that previous research projects have relied on.
“Overall, neither duration of play nor choice of video game genres had significant correlations with the CogAT measures. That result shows no direct linkage between video game playing and cognitive performance, despite what had been assumed,” said May Jadalla, professor in the School of Teaching and Learning at Illinois State University and the study’s principal investigator.
But the study revealed another side of the issue, too. Certain types of games described as helping children build healthy cognitive skills also presented no measurable effects, in spite of the games’ marketing messages.
“The current study found results that are consistent with previous research showing that types of gameplay that seem to augment cognitive functions in young adults don’t have the same impact in much younger children,” said C. Shawn Green, professor in the Department of Psychology at the University of Wisconsin-Madison.
Does this mean the world can play on? Maybe, the research suggests. But the experts also caution that gaming time took the heaviest players’ away from other, more productive activities — homework, to be specific — in a process psychologists call displacement. But even in those cases, the differences were slight between those participants and their peers’ CogAT measures of cognitive abilities.
“The study results show parents probably don’t have to worry so much about cognitive setbacks among video game-loving children, up to fifth grade. Reasonable amounts of video gaming should be OK, which will be delightful news for the kids. Just keep an eye out for obsessive behavior,” said Zhang. “When it comes to video games, finding common ground between parents and young kids is tricky enough. At least now we understand that finding balance in childhood development is the key, and there’s no need for us to over-worry about video gaming.”
The study was funded by the National Science Foundation. More

Engineers at the University of Waterloo have developed artificial intelligence (AI) technology to predict if women with breast cancer would benefit from chemotherapy prior to surgery.
The new AI algorithm, part of the open-source Cancer-Net initiative led by Dr. Alexander Wong, could help unsuitable candidates avoid the serious side effects of chemotherapy and pave the way for better surgical outcomes for those who are suitable.
“Determining the right treatment for a given breast cancer patient is very difficult right now, and it is crucial to avoid unnecessary side effects from using treatments that are unlikely to have real benefit for that patient,” said Wong, a professor of systems design engineering.
“An AI system that can help predict if a patient is likely to respond well to a given treatment gives doctors the tool needed to prescribe the best personalized treatment for a patient to improve recovery and survival.”
In a project led by Amy Tai, a graduate student with the Vision and Image Processing (VIP) Lab, the AI software was trained with images of breast cancer made with a new magnetic image resonance modality, invented by Wong and his team, called synthetic correlated diffusion imaging (CDI).
With knowledge gleaned from CDI images of old breast cancer cases and information on their outcomes, the AI can predict if pre-operative chemotherapy treatment would benefit new patients based on their CDI images.
Known as neoadjuvant chemotherapy, the pre-surgical treatment can shrink tumours to make surgery possible or easier and reduce the need for major surgery such as mastectomies.
“I’m quite optimistic about this technology as deep-learning AI has the potential to see and discover patterns that relate to whether a patient will benefit from a given treatment,” said Wong, a director of the VIP Lab and the Canada Research Chair in Artificial Intelligence and Medical Imaging.
A paper on the project, Cancer-Net BCa: Breast Cancer Pathologic Complete Response Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging, was recently presented at Med-NeurIPS as part of NeurIPS 2022, a major international conference on AI.
The new AI algorithm and the complete dataset of CDI images of breast cancer have been made publicly available through the Cancer-Net initiative so other researchers can help advance the field. More

Carnegie Mellon University’s Robotics Institute has a new artist-in-residence.
FRIDA, a robotic arm with a paintbrush taped to it, uses artificial intelligence to collaborate with humans on works of art. Ask FRIDA to paint a picture, and it gets to work putting brush to canvas.
“There’s this one painting of a frog ballerina that I think turned out really nicely,” said Peter Schaldenbrand, a School of Computer Science Ph.D. student in the Robotics Institute working with FRIDA and exploring AI and creativity. “It is really silly and fun, and I think the surprise of what FRIDA generated based on my input was really fun to see.”
FRIDA, named after Frida Kahlo, stands for Framework and Robotics Initiative for Developing Arts. The project is led by Schaldenbrand with RI faculty members Jean Oh and Jim McCann, and has attracted students and researchers across CMU.
Users can direct FRIDA by inputting a text description, submitting other works of art to inspire its style, or uploading a photograph and asking it to paint a representation of it. The team is experimenting with other inputs as well, including audio. They played ABBA’s “Dancing Queen” and asked FRIDA to paint it.
“FRIDA is a robotic painting system, but FRIDA is not an artist,” Schaldenbrand said. “FRIDA is not generating the ideas to communicate. FRIDA is a system that an artist could collaborate with. The artist can specify high-level goals for FRIDA and then FRIDA can execute them.”
The robot uses AI models similar to those powering tools like OpenAI’s ChatGPT and DALL-E 2, which generate text or an image, respectively, in response to a prompt. FRIDA simulates how it would paint an image with brush strokes and uses machine learning to evaluate its progress as it works.

FRIDA’s final products are impressionistic and whimsical. The brushstrokes are bold. They lack the precision sought so often in robotic endeavors. If FRIDA makes a mistake, it riffs on it, incorporating the errant splotch of paint into the end result.
“FRIDA is a project exploring the intersection of human and robotic creativity,” McCann said. “FRIDA is using the kind of AI models that have been developed to do things like caption images and understand scene content and applying it to this artistic generative problem.”
FRIDA taps into AI and machine learning several times during its artistic process. First, it spends an hour or more learning how to use its paintbrush. Then, it uses large vision-language models trained on massive datasets that pair text and images scraped from the internet, such as OpenAI’s Contrastive Language-Image Pre-Training (CLIP), to understand the input. AI systems use these models to generate new text or images based on a prompt.
Other image-generating tools such as OpenAI’s DALL-E 2, use large vision-language models to produce digital images. FRIDA takes that a step further and uses its embodied robotic system to produce physical paintings. One of the biggest technical challenges in producing a physical image is reducing the simulation-to-real gap, the difference between what FRIDA composes in simulation and what it paints on the canvas. FRIDA uses an idea known as real2sim2real. The robot’s actual brush strokes are used to train the simulator to reflect and mimic the physical capabilities of the robot and painting materials.
FRIDA’s team also seeks to address some of the limitations in current large vision-language models by continually refining the ones they use. The team fed the models the headlines from news articles to give it a sense of what was happening in the world and further trained them on images and text more representative of diverse cultures to avoid an American or Western bias. This multicultural collaboration effort is led by Zhixuan Liu and Beverley-Claire Okogwu, first-year RI master’s students, and Youeun Shin and Youngsik Yun, visiting master’s students from Dongguk University in Korea. Their efforts include training data contributions from China, Japan, Korea, Mexico, Nigeria, Norway, Vietnam and other countries.

Once FRIDA’s human user has specified a high-level concept of the painting they want to create, the robot uses machine learning to create its simulation and develop a plan to make a painting to achieve the user’s goals. FRIDA displays a color pallet on a computer screen for a human to mix and provide to the robot. Automatic paint mixing is currently being developed, led by Jiaying Wei, a master’s student in the School of Architecture, with Eunsu Kang, faculty in the Machine Learning Department.
Armed with a brush and paint, FRIDA will make its first strokes. Every so often, the robot uses an overhead camera to capture an image of the painting. The image helps FRIDA evaluate its progress and refine its plan, if needed. The whole process takes hours.
“People wonder if FRIDA is going to take artists’ jobs, but the main goal of the FRIDA project is quite the opposite. We want to really promote human creativity through FRIDA,” Oh said. “For instance, I personally wanted to be an artist. Now, I can actually collaborate with FRIDA to express my ideas in painting.”
More information about FRIDA is available on its website. The team will present its latest research from the project, “FRIDA: A Collaborative Robot Painter With a Differentiable, Real2Sim2Real Planning Environment” at the 2023 IEEE International Conference on Robotics and Automation this May in London. FRIDA resides in the RI’s Bot Intelligence Group (BIG) lab in the Squirrel Hill neighborhood of Pittsburgh. More

Large language models like OpenAI’s GPT-3 are massive neural networks that can generate human-like text, from poetry to programming code. Trained using troves of internet data, these machine-learning models take a small bit of input text and then predict the text that is likely to come next.
But that’s not all these models can do. Researchers are exploring a curious phenomenon known as in-context learning, in which a large language model learns to accomplish a task after seeing only a few examples — despite the fact that it wasn’t trained for that task. For instance, someone could feed the model several example sentences and their sentiments (positive or negative), then prompt it with a new sentence, and the model can give the correct sentiment.
Typically, a machine-learning model like GPT-3 would need to be retrained with new data for this new task. During this training process, the model updates its parameters as it processes new information to learn the task. But with in-context learning, the model’s parameters aren’t updated, so it seems like the model learns a new task without learning anything at all.
Scientists from MIT, Google Research, and Stanford University are striving to unravel this mystery. They studied models that are very similar to large language models to see how they can learn without updating parameters.
The researchers’ theoretical results show that these massive neural network models are capable of containing smaller, simpler linear models buried inside them. The large model could then implement a simple learning algorithm to train this smaller, linear model to complete a new task, using only information already contained within the larger model. Its parameters remain fixed.
An important step toward understanding the mechanisms behind in-context learning, this research opens the door to more exploration around the learning algorithms these large models can implement, says Ekin Akyürek, a computer science graduate student and lead author of a paper exploring this phenomenon. With a better understanding of in-context learning, researchers could enable models to complete new tasks without the need for costly retraining.

“Usually, if you want to fine-tune these models, you need to collect domain-specific data and do some complex engineering. But now we can just feed it an input, five examples, and it accomplishes what we want. So in-context learning is a pretty exciting phenomenon,” Akyürek says.
Joining Akyürek on the paper are Dale Schuurmans, a research scientist at Google Brain and professor of computing science at the University of Alberta; as well as senior authors Jacob Andreas, the X Consortium Assistant Professor in the MIT Department of Electrical Engineering and Computer Science and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); Tengyu Ma, an assistant professor of computer science and statistics at Stanford; and Danny Zhou, principal scientist and research director at Google Brain. The research will be presented at the International Conference on Learning Representations.
A model within a model
In the machine-learning research community, many scientists have come to believe that large language models can perform in-context learning because of how they are trained, Akyürek says.
For instance, GPT-3 has hundreds of billions of parameters and was trained by reading huge swaths of text on the internet, from Wikipedia articles to Reddit posts. So, when someone shows the model examples of a new task, it has likely already seen something very similar because its training dataset included text from billions of websites. It repeats patterns it has seen during training, rather than learning to perform new tasks.

Akyürek hypothesized that in-context learners aren’t just matching previously seen patterns, but instead are actually learning to perform new tasks. He and others had experimented by giving these models prompts using synthetic data, which they could not have seen anywhere before, and found that the models could still learn from just a few examples. Akyürek and his colleagues thought that perhaps these neural network models have smaller machine-learning models inside them that the models can train to complete a new task.
“That could explain almost all of the learning phenomena that we have seen with these large models,” he says.
To test this hypothesis, the researchers used a neural network model called a transformer, which has the same architecture as GPT-3, but had been specifically trained for in-context learning.
By exploring this transformer’s architecture, they theoretically proved that it can write a linear model within its hidden states. A neural network is composed of many layers of interconnected nodes that process data. The hidden states are the layers between the input and output layers.
Their mathematical evaluations show that this linear model is written somewhere in the earliest layers of the transformer. The transformer can then update the linear model by implementing simple learning algorithms.
In essence, the model simulates and trains a smaller version of itself.
Probing hidden layers
The researchers explored this hypothesis using probing experiments, where they looked in the transformer’s hidden layers to try and recover a certain quantity.
“In this case, we tried to recover the actual solution to the linear model, and we could show that the parameter is written in the hidden states. This means the linear model is in there somewhere,” he says.
Building off this theoretical work, the researchers may be able to enable a transformer to perform in-context learning by adding just two layers to the neural network. There are still many technical details to work out before that would be possible, Akyürek cautions, but it could help engineers create models that can complete new tasks without the need for retraining with new data.
Moving forward, Akyürek plans to continue exploring in-context learning with functions that are more complex than the linear models they studied in this work. They could also apply these experiments to large language models to see whether their behaviors are also described by simple learning algorithms. In addition, he wants to dig deeper into the types of pretraining data that can enable in-context learning.
“With this work, people can now visualize how these models can learn from exemplars. So, my hope is that it changes some people’s views about in-context learning,” Akyürek says. “These models are not as dumb as people think. They don’t just memorize these tasks. They can learn new tasks, and we have shown how that can be done.” More

New research from the University of Georgia reveals that artificial intelligence can be used to find planets outside of our solar system. The recent study demonstrated that machine learning can be used to find exoplanets, information that could reshape how scientists detect and identify new planets very far from Earth.
“One of the novel things about this is analyzing environments where planets are still forming,” said Jason Terry, doctoral student in the UGA Franklin College of Arts and Sciences department of physics and astronomy and lead author on the study. “Machine learning has rarely been applied to the type of data we’re using before, specifically for looking at systems that are still actively forming planets.”
The first exoplanet was found in 1992, and though more than 5,000 are known to exist, those have been among the easiest for scientists to find. Exoplanets at the formation stage are difficult to see for two primary reasons. They are too far away, often hundreds of lights years from Earth, and the discs where they form are very thick, thicker than the distance of the Earth to the sun. Data suggests the planets tend to be in the middle of these discs, conveying a signature of dust and gases kicked up by the planet.
The research showed that artificial intelligence can help scientists overcome these difficulties.
“This is a very exciting proof of concept,” said Cassandra Hall, assistant professor of astrophysics, principal investigator of the Exoplanet and Planet Formation Research Group, and co-author on the study. “The power here is that we used exclusively synthetic telescope data generated by computer simulations to train this AI, and then applied it to real telescope data. This has never been done before in our field, and paves the way for a deluge of discoveries as James Webb Telescope data rolls in.”
The James Webb Space Telescope, launched by NASA in 2021, has inaugurated a new level of infrared astronomy, bringing stunning new images and reams of data for scientists to analyze. It’s just the latest iteration of the agency’s quest to find exoplanets, scattered unevenly across the galaxy. The Nancy Grace Roman Observatory, a 2.4-meter survey telescope scheduled to launch in 2027 that will look for dark energy and exoplanets, will be the next major expansion in capability — and delivery of information and data — to comb through the universe for life.

The Webb telescope supplies the ability for scientists to look at exoplanetary systems in an extremely bright, high resolution, with the forming environments themselves a subject of great interest as they determine the resulting solar system.
“The potential for good data is exploding, so it’s a very exciting time for the field,” Terry said.
New analytical tools are essential
Next-generation analytical tools are urgently needed to greet this high-quality data, so scientists can spend more time on theoretical interpretations rather than meticulously combing through the data and trying to find tiny little signatures.
“In a sense, we’ve sort of just made a better person,” Terry said. “To a large extent the way we analyze this data is you have dozens, hundreds of images for a specific disc and you just look through and ask ‘is that a wiggle?’ then run a dozen simulations to see if that’s a wiggle and … it’s easy to overlook them — they’re really tiny, and it depends on the cleaning, and so this method is one, really fast, and two, its accuracy gets planets that humans would miss.”
Terry says this is what machine learning can already accomplish — improve on human capacity to save time and money as well as efficiently guide scientific time, investments and new proposals.
“There remains, within science and particularly astronomy in general, skepticism about machine learning and of AI, a valid criticism of it being this black box — where you have hundreds of millions of parameters and somehow you get out an answer. But we think we’ve demonstrated pretty strongly in this work that machine learning is up to the task. You can argue about interpretation. But in this case, we have very concrete results that demonstrate the power of this method.”
The research team’s work is designed to develop a concrete foundation for future applications on observational data, demonstrating the method’s effectiveness by using simulational observations. More