Computers Math

Artificial intelligence can predict on- and off-target activity of CRISPR tools that target RNA instead of DNA, according to new research published in Nature Biotechnology.
The study by researchers at New York University, Columbia Engineering, and the New York Genome Center, combines a deep learning model with CRISPR screens to control the expression of human genes in different ways — such as flicking a light switch to shut them off completely or by using a dimmer knob to partially turn down their activity. These precise gene controls could be used to develop new CRISPR-based therapies.
CRISPR is a gene editing technology with many uses in biomedicine and beyond, from treating sickle cell anemia to engineering tastier mustard greens. It often works by targeting DNA using an enzyme called Cas9. In recent years, scientists discovered another type of CRISPR that instead targets RNA using an enzyme called Cas13.
RNA-targeting CRISPRs can be used in a wide range of applications, including RNA editing, knocking down RNA to block expression of a particular gene, and high-throughput screening to determine promising drug candidates. Researchers at NYU and the New York Genome Center created a platform for RNA-targeting CRISPR screens using Cas13 to better understand RNA regulation and to identify the function of non-coding RNAs. Because RNA is the main genetic material in viruses including SARS-CoV-2 and flu, RNA-targeting CRISPRs also hold promise for developing new methods to prevent or treat viral infections. Also, in human cells, when a gene is expressed, one of the first steps is the creation of RNA from the DNA in the genome.
A key goal of the study is to maximize the activity of RNA-targeting CRISPRs on the intended target RNA and minimize activity on other RNAs which could have detrimental side effects for the cell. Off-target activity includes both mismatches between the guide and target RNA as well as insertion and deletion mutations. Earlier studies of RNA-targeting CRISPRs focused only on on-target activity and mismatches; predicting off-target activity, particularly insertion and deletion mutations, has not been well-studied. In human populations, about one in five mutations are insertions or deletions, so these are important types of potential off-targets to consider for CRISPR design.
“Similar to DNA-targeting CRISPRs such as Cas9, we anticipate that RNA-targeting CRISPRs such as Cas13 will have an outsized impact in molecular biology and biomedical applications in the coming years,” said Neville Sanjana, associate professor of biology at NYU, associate professor of neuroscience and physiology at NYU Grossman School of Medicine, a core faculty member at New York Genome Center, and the study’s co-senior author. “Accurate guide prediction and off-target identification will be of immense value for this newly developing field and therapeutics.”
In their study in Nature Biotechnology, Sanjana and his colleagues performed a series of pooled RNA-targeting CRISPR screens in human cells. They measured the activity of 200,000 guide RNAs targeting essential genes in human cells, including both “perfect match” guide RNAs and off-target mismatches, insertions, and deletions.

Sanjana’s lab teamed up with the lab of machine learning expert David Knowles to engineer a deep learning model they named TIGER (Targeted Inhibition of Gene Expression via guide RNA design) that was trained on the data from the CRISPR screens. Comparing the predictions generated by the deep learning model and laboratory tests in human cells, TIGER was able to predict both on-target and off-target activity, outperforming previous models developed for Cas13 on-target guide design and providing the first tool for predicting off-target activity of RNA-targeting CRISPRs.
“Machine learning and deep learning are showing their strength in genomics because they can take advantage of the huge datasets that can now be generated by modern high-throughput experiments. Importantly, we were also able to use “interpretable machine learning” to understand why the model predicts that a specific guide will work well,” said Knowles, assistant professor of computer science and systems biology at Columbia Engineering, a core faculty member at New York Genome Center, and the study’s co-senior author.
“Our earlier research demonstrated how to design Cas13 guides that can knock down a particular RNA. With TIGER, we can now design Cas13 guides that strike a balance between on-target knockdown and avoiding off-target activity,” said Hans-Hermann (Harm) Wessels, the study’s co-first author and a senior scientist at the New York Genome Center, who was previously a postdoctoral fellow in Sanjana’s laboratory.
The researchers also demonstrated that TIGER’s off-target predictions can be used to precisely modulate gene dosage — the amount of a particular gene that is expressed — by enabling partial inhibition of gene expression in cells with mismatch guides. This may be useful for diseases in which there are too many copies of a gene, such as Down syndrome, certain forms of schizophrenia, Charcot-Marie-Tooth disease (a hereditary nerve disorder), or in cancers where aberrant gene expression can lead to uncontrolled tumor growth.
“Our deep learning model can tell us not only how to design a guide RNA that knocks down a transcript completely, but can also ‘tune’ it — for instance, having it produce only 70% of the transcript of a specific gene,” said Andrew Stirn, a PhD student at Columbia Engineering and the New York Genome Center, and the study’s co-first author.
By combining artificial intelligence with an RNA-targeting CRISPR screen, the researchers envision that TIGER’s predictions will help avoid undesired off-target CRISPR activity and further spur development of a new generation of RNA-targeting therapies.
“As we collect larger datasets from CRISPR screens, the opportunities to apply sophisticated machine learning models are growing rapidly. We are lucky to have David’s lab next door to ours to facilitate this wonderful, cross-disciplinary collaboration. And, with TIGER, we can predict off-targets and precisely modulate gene dosage which enables many exciting new applications for RNA-targeting CRISPRs for biomedicine,” said Sanjana.
Additional study authors include Alejandro Méndez-Mancilla and Sydney K. Hart of NYU and the New York Genome Center, and Eric J. Kim of Columbia University. The research was supported by grants from the National Institutes of Health (DP2HG010099, R01CA218668, R01GM138635), DARPA (D18AP00053), the Cancer Research Institute, and the Simons Foundation for Autism Research Initiative. More

“Large Language Models are neural network language models with remarkable conversational skills. They generate human-like responses and engage in interactive conversations. However, they often generate highly convincing statements that are verifiably wrong or provide inappropriate responses. Today there is no way to be certain about the quality, evidence level, or consistency of clinical information or supporting evidence for any response. These chatbots are unsafe tools when it comes to medical advice and it is necessary to develop new frameworks that ensure patient safety,” said Prof. Stephen Gilbert, Professor for Medical Device Regulatory Science at Else Kröner Fresenius Center for Digital Health at TU Dresden.
Challenges in the regulatory approval of large language models
Most people research their symptoms online before seeking medical advice. Search engines play a role in decision-making process. The forthcoming integration of LLM-chatbots into search engines may increase users’ confidence in the answers given by a chatbot that mimics conversation. It has been demonstrated that LLMs can provide profoundly dangerous information when prompted with medical questions.
LLM’s underlying approach has no model of medical “ground truth,” which is inherently dangerous. Chat interfaced LLMs have already provided harmful medical responses and have already been used unethically in ‘experiments’ on patients without consent. Almost every medical LLM use case requires regulatory control in the EU and US. In the US their lack of explainability disqualifies them from being ‘non devices’. LLMs with explainability, low bias, predictability, correctness, and verifiable outputs do not currently exist and they are not exempted from current (or future) governance approaches.
In this paper the authors describe the limited scenarios in which LLMs could find application under current frameworks, they describe how developers can seek to create LLM-based tools that could be approved as medical devices, and they explore the development of new frameworks that preserve patient safety. “Current LLM-chatbots do not meet key principles for AI in healthcare, like bias control, explainability, systems of oversight, validation and transparency. To earn their place in medical armamentarium, chatbots must be designed for better accuracy, with safety and clinical efficacy demonstrated and approved by regulators,” concludes Prof. Gilbert. More

Transistors that can change properties are important elements in the development of tomorrow’s semiconductors. With standard transistors approaching the limit for how small they can be, having more functions on the same number of units becomes increasingly important in enabling the development of small, energy-efficient circuits for improved memory and more powerful computers. Researchers at Lund University in Sweden have shown how to create new configurable transistors and exert control on a new, more precise level.
In view of the constantly increasing need for better, more powerful and efficient circuits, there is a great interest in reconfigurable transistors. The advantage of these is that, in contrast to standard semiconductors, it is possible to change the transistor’s properties after they have been manufactured.
Historically, computers’ computational power and efficiency have been improved by scaling down the silicon transistor’s size (also known as Moore’s Law). But now a stage has been reached where the costs for continuing development along those lines have become much higher, and quantum mechanics problems have arisen that have slowed development.
Instead, the search is on for new materials, components and circuits. Lund University is among the world leaders in III-V materials, which are an alternative to silicon. These are materials with considerable potential in the development of high-frequency technology (such as parts for future 6G and 7G networks), optical applications and increasingly energy-efficient electronic components.
Ferroelectric materials are used in order to realise this potential. These are special materials that can change their inner polarisation when exposed to an electric field. It can be compared to an ordinary magnet, but instead of a magnetic north and south pole, electric poles are formed with a positive and a negative charge on each side of the material. By changing the polarisation, it is possible to control the transistor. Another advantage is that the material “remembers” its polarisation, even if the current is turned off.
Through a new combination of materials, the researchers have created ferroelectric “grains” that control a tunnel junction — an electrical bridging effect — in the transistor. This is on an extremely small scale — a grain is 10 nanometres in size. By measuring fluctuations in the voltage or current, it has been possible to identify when polarisation changes in the individual grains and thus understand how this affects the transistor’s behaviour.

The newly published research has examined new ferroelectric memory in the form of transistors with tunnel barriers in order to create new circuit architectures.
“The aim is to create neuromorphic circuits, i.e. circuits that are adapted for artificial intelligence in that their structure is similar to the human brain with its synapses and neurons,” says Anton Eriksson, who recently completed his doctoral degree in nanoelectronics.
What is special about the new results is that it has been possible to create tunnel junctions using ferroelectric grains that are located directly adjacent to the junction. These nanograins can then be controlled on an individual level, when previously it was only possible to keep track of entire groups of grains, known as ensembles. In this way, it is possible to identify and control separate parts of the material.
“In order to create advanced applications, you must first understand the dynamics in individual grains down to the atomic level, as well as the defects that exist. The increased understanding of the material can be used to optimise the functions. By controlling these ferroelectric grains, you can then create new semiconductors in which you can alter properties. By changing the voltage, you can thus produce different functions in one and the same component,” says Lars-Erik Wernersson, professor of nanoelectronics.
The researchers have also examined how this knowledge can be used to create different reconfigurable applications by manipulating in various ways the signal that goes through the transistor. It could, for example, be used for new memory cells or more energy-efficient transistors.
This new type of transistor is called ferro-TFET and can be used in both digital and analogue circuits.
“What’s interesting is that it’s possible to modulate the input signal in various ways, for example by the transistor shifting phase, frequency doubling, and signal mixing. As the transistor remembers its properties, even when the current is turned off, there is no need to reset it every time the circuit is used,” says Zhongyunshen Zho, doctoral student in nanoelectronics.
Another advantage of these transistors is that they can function at low voltage. This makes them energy-efficient, which will be required, for example, in tomorrow’s wireless communication, Internet of Things and quantum computers.
“I consider this to be leading-edge research of international standing. It’s good that in Lund and Sweden we are at the forefront regarding semiconductors, especially in view of the EU’s recently enacted Chips Act, which aims to strengthen Europe’s position regarding semiconductors,” says Lars-Erik Wernersson. More

Flexible displays that can change color, convey information and even send veiled messages via infrared radiation are now possible, thanks to new research from the University of Illinois Urbana-Champaign. Engineers inspired by the morphing skins of animals like chameleons and octopuses have developed capillary-controlled robotic flapping fins to create switchable optical and infrared light multipixel displays that are 1,000 times more energy efficient than light-emitting devices.
The new study led by mechanical science and engineering professor Sameh Tawfick demonstrates how bendable fins and fluids can simultaneously switch between straight or bent and hot and cold by controlling the volume and temperature of tiny fluid-filled pixels. Varying the volume of fluids within the pixels can change the directions in which the flaps flip — similar to old-fashioned flip clocks — and varying the temperature allows the pixels to communicate via infrared energy.
The study findings are published in the journal Science Advances.
Tawfick’s interest in the interaction of elastic and capillary forces — or elasto-capillarity — started as a graduate student, spanned the basic science of hair wetting and led to his research in soft robotic displays at Illinois.
“An everyday example of elasto-capillarity is what happens to our hair when we get in the shower,” Tawfick said. “When our hair gets wet, it sticks together and bends or bundles as capillary forces are applied and released when it dries out.”
In the lab, the team created small boxes, or pixels, a few millimeters in size, that contain fins made of a flexible polymer that bend when the pixels are filled with fluid and drained using a system of tiny pumps. The pixels can have single or multiple fins and are arranged into arrays that form a display to convey information, Tawfick said.

“We are not limited to cubic pixel boxes, either,” Tawfick said. “The fins can be arranged in various orientations to create different images, even along curved surfaces. The control is precise enough to achieve complex motions, like simulating the opening of a flower bloom.”
The study reports that another feature of the new displays is the ability to send two simultaneous signals — one that can be seen with the human eye and another that can only be seen with an infrared camera.
“Because we can control the temperature of these individual droplets, we can display messages that can only be seen using an infrared device,” Tawfick said, “Or we can send two different messages at the same time.”
However, there are a few limitations to the new displays, Tawfick said.
While building the new devices, the team found that the tiny pumps needed to control the pixel fluids were not commercially available, and the entire device is sensitive to gravity — meaning that it only works while in a horizontal position.

“Once we turn the display by 90 degrees, the performance is greatly degraded, which is detrimental to applications like billboards and other signs intended for the public,” Tawfick said. “The good news is, we know that when liquid droplets become small enough, they become insensitive to gravity, like when you see a rain droplet sticking on your window and it doesn’t fall. We have found that if we use fluid droplets that are five times smaller, gravity will no longer be an issue.”
The team said that because the science behind gravity’s effect on droplets is well understood, it will provide the focal point for their next application of the emerging technology.
Tawfick said he is very excited to see where this technology is headed because it brings a fresh idea to a big market space of large reflective displays. “We have developed a whole new breed of displays that require minimal energy, are scaleable and even flexible enough to be placed onto curved surfaces.”
Illinois researchers Jonghyun Ha, Yun Seong Kim, Chengzhang Li, Jonghyun Hwang, Sze Chai Leung and Ryan Siu also participated in this research.
The Airforce Office of Scientific Research and the National Science Foundation supported this research. More

For people who have suffered neurotrauma such as a stroke, everyday tasks can be extremely challenging because of decreased coordination and strength in one or both upper limbs. These problems have spurred the development of robotic devices to help enhance their abilities. However, the rigid nature of these assistive devices can be problematic, especially for more complex tasks like playing a musical instrument.
A first-of-its-kind robotic glove is lending a “hand” and providing hope to piano players who have suffered a disabling stroke. Developed by researchers from Florida Atlantic University’s College of Engineering and Computer Science, the soft robotic hand exoskeleton uses artificial intelligence to improve hand dexterity.
Combining flexible tactile sensors, soft actuators and AI, this robotic glove is the first to “feel” the difference between correct and incorrect versions of the same song and to combine these features into a single hand exoskeleton.
“Playing the piano requires complex and highly skilled movements, and relearning tasks involves the restoration and retraining of specific movements or skills,” said Erik Engeberg, Ph.D., senior author, a professor in FAU’s Department of Ocean and Mechanical Engineering within the College of Engineering and Computer Science, and a member of the FAU Center for Complex Systems and Brain Sciences and the FAU Stiles-Nicholson Brain Institute. “Our robotic glove is composed of soft, flexible materials and sensors that provide gentle support and assistance to individuals to relearn and regain their motor abilities.”
Researchers integrated special sensor arrays into each fingertip of the robotic glove. Unlike prior exoskeletons, this new technology provides precise force and guidance in recovering the fine finger movements required for piano playing. By monitoring and responding to users’ movements, the robotic glove offers real-time feedback and adjustments, making it easier for them to grasp the correct movement techniques.
To demonstrate the robotic glove’s capabilities, researchers programmed it to feel the difference between correct and incorrect versions of the well-known tune, “Mary Had a Little Lamb,” played on the piano. To introduce variations in the performance, they created a pool of 12 different types of errors that could occur at the beginning or end of a note, or due to timing errors that were either premature or delayed, and that persisted for 0.1, 0.2 or 0.3 seconds. Ten different song variations consisted of three groups of three variations each, plus the correct song played with no errors.

To classify the song variations, Random Forest (RF), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) algorithms were trained with data from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the robotic glove independently and while worn by a person. The accuracy of these algorithms was compared to classify the correct and incorrect song variations with and without the human subject.
Results of the study, published in the journal Frontiers in Robotics and AI, demonstrated that the ANN algorithm had the highest classification accuracy of 97.13 percent with the human subject and 94.60 percent without the human subject. The algorithm successfully determined the percentage error of a certain song as well as identified key presses that were out of time. These findings highlight the potential of the smart robotic glove to aid individuals who are disabled to relearn dexterous tasks like playing musical instruments.
Researchers designed the robotic glove using 3D printed polyvinyl acid stents and hydrogel casting to integrate five actuators into a single wearable device that conforms to the user’s hand. The fabrication process is new, and the form factor could be customized to the unique anatomy of individual patients with the use of 3D scanning technology or CT scans.
“Our design is significantly simpler than most designs as all the actuators and sensors are combined into a single molding process,” said Engeberg. “Importantly, although this study’s application was for playing a song, the approach could be applied to myriad tasks of daily life and the device could facilitate intricate rehabilitation programs customized for each patient.”
Clinicians could use the data to develop personalized action plans to pinpoint patient weaknesses, which may present themselves as sections of the song that are consistently played erroneously and can be used to determine which motor functions require improvement. As patients progress, more challenging songs could be prescribed by the rehabilitation team in a game-like progression to provide a customizable path to improvement.
“The technology developed by professor Engeberg and the research team is truly a gamechanger for individuals with neuromuscular disorders and reduced limb functionality,” said Stella Batalama, Ph.D., dean of the FAU College of Engineering and Computer Science. “Although other soft robotic actuators have been used to play the piano; our robotic glove is the only one that has demonstrated the capability to ‘feel’ the difference between correct and incorrect versions of the same song.”
Study co-authors are Maohua Lin, first author and a Ph.D. student; Rudy Paul, a graduate student; and Moaed Abd, Ph.D., a recent graduate; all from the FAU College of Engineering and Computer Science; James Jones, Boise State University; Darryl Dieujuste, a graduate research assistant, FAU College of Engineering and Computer Science; and Harvey Chim, M.D., a professor in the Division of Plastic and Reconstructive Surgery at the University of Florida.
This research was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIH), the National Institute of Aging of the NIH and the National Science Foundation. This research was supported in part by a seed grant from the FAU College of Engineering and Computer Science and the FAU Institute for Sensing and Embedded Network Systems Engineering (I-SENSE). More

Researchers have demonstrated a new material for single-molecule electronic switches, which can effectively vary current at the nanoscale in response to external stimuli. The material for this molecular switch has a unique structure created by locking a linear molecular backbone into a ladder-type structure. A new study finds that the ladder-type molecular structure greatly enhances the stability of the material, making it highly promising for use in single-molecule electronics applications.
Reported in the journal Chem, the study shows that the ladder-type molecule serves as a robust and reversible molecular switch over a wide range of conductivity levels and different molecular states.
“Our work provides a significant step forward towards the development of functional molecular electronic devices,” says Charles Schroeder, who is the James Economy Professor of Materials Science and Engineering and Professor of Chemical and Biomolecular Engineering at the University of Illinois Urbana-Champaign.
To enhance the chemical and mechanical stability of the molecule, the team used new strategies in chemical synthesis to lock the molecular backbone to prevent the molecule from rotating, like converting a rope ladder into something more stable like metal or wood.
“Imagine a light switch that we turn on and off every day, but instead of flipping an actual switch, we add chemical or electrochemical stimuli to turn the electrical signal from the material on and off,” says lead author and former graduate student Jialing (Caroline) Li. Compared to bulk inorganic materials, organic single molecules can be made into basic electrical components, like wires and transistors, and will help enable the ultimate goal of shrinking electrical circuits.
Single-molecule electronic devices are constructed as junctions with a single molecule bridge that is generally anchored to two terminal groups connected to metal electrodes. These devices can be made programmable by using a stimuli-responsive element in the bridge that can be switched on and off by using an array of stimuli such as pH, optical fields, electric fields, magnetic fields, mechanical forces and electrochemical control.
“The molecular scale switch has been a very popular subject in studies of single molecule electronics,” Li explains. “But realizing a multi-state switch on a molecular scale is challenging because we require a material that is conductive and has several different molecular charge states, and we require the material to be very stable so it can be switched on and off for many cycles.”
Though Li explored many other organic materials, the drawback of those materials was that they were not stable in ambient conditions and could break down easily when exposed to oxygen. After searching for the ideal material for a long time, Li struck gold when she stumbled upon a material from a research group at Texas A&M University (collaborators on this project) and immediately identified it as ideal for her purposes.
Modifying the structure by locking the backbone of the molecule prevents hydrolysis, chemical breakdown due to reaction with water, and other degradation reactions from occurring, and makes characterization of the material easier since it cannot rotate and change forms. This rigid, coplanar form enhances the electronic properties of the molecule, making the flow of electrons through the material easier. The ladder-type structure allows for stable molecular charge states when external stimuli are applied that give rise to significantly different levels of conductivity- making multi-state switching possible.
This material meets almost all of the requirements needed to serve in single-molecule electronic devices: it is stable in ambient conditions, can be cycled on/off many times, is conductive (although not as conductive as metal) and has different molecular states accessible to be utilized.
“Researchers have been struggling to minimize the size of the transistor to fit as many as possible on chips for semiconductors, usually using inorganic materials like silicon,” Li says. “An alternative way of doing that is using organic materials like a single-molecule material to conduct the electrons and replace the inorganic counterparts.” The ladder-type structure used in this research shows promise to be used as functional materials for single-molecule transistors.
For now, only one unit of the molecule is used for single-molecule electronics, but it is possible to extend the length to include many repeating units to make a longer molecular wire. The team believes that the material will still be highly conductive, even over a longer distance. More

In recent years, scientists have been studying special materials called topological materials, with special attention paid to the shape, i.e., topology, of their electronic structures (electronic bands). Although it is not visible in real space, their unusual shape in topological materials produces various unique properties that can be suitable for making next-generation devices.
It was thought that in order to exploit topological physical properties, crystalline materials, where atoms are highly ordered and arranged in repeating patterns, were needed. Materials in the amorphous state, i.e., where atoms are disordered and only periodically arranged over short distances, were considered unsuitable for hosting the outstanding physical properties of topological materials.
Now, a collaborative research group has verified that even amorphous materials can have these special properties. The group was led by Associate Professor Kohei Fujiwara and Professor Atsushi Tsukazaki from Tohoku University’s Institute for Materials Research (IMR); Lecturer Yasuyuki Kato and Professor Yukitoshi Motome from the University of Tokyo’s Graduate School of Engineering and Associate Professor Hitoshi Abe at the High Energy Accelerator Research Organization’s Institute for Materials Structure Science.
Details of their findings were reported in the journal Nature Communications on June 13, 2023.
“We discovered that the concept of band topology, which has been discussed mainly in crystals, is also valid and technologically useful in amorphous states,” stated Fujiwara.
To make their discovery, the team performed experiments and model calculations on iron-tin amorphous thin films. They demonstrated that despite a short-range atom arrangement, the amorphous material still showed the same special effects as in the crystalline materials, notably the anomalous Hall effect and the Nernst effect.
“Amorphous materials are easier and cheaper to make compared to crystals, so this opens up new possibilities for developing devices using these materials. This could lead to advancements in sensing technology, which is important for creating the Internet of Things (IoT) where many devices are connected and communicate with each other,” adds Fujiwara.
Looking ahead, the group is eager to unearth more amorphous materials and develop innovative devices using them. More

Scientists have made an important step toward developing computers advanced enough to simulate complex natural phenomena at the quantum level. While these types of simulations are too cumbersome or outright impossible for classical computers to handle, photonics-based quantum computing systems could provide a solution.
A team of researchers from the University of Rochester’s Hajim School of Engineering & Applied Sciences developed a new chip-scale optical quantum simulation system that could help make such a system feasible. The team, led by Qiang Lin, a professor of electrical and computer engineering and optics, published their findings in Nature Photonics.
Lin’s team ran the simulations in a synthetic space that mimics the physical world by controlling the frequency, or color, of quantum entangled photons as time elapses. This approach differs from the traditional photonics-based computing methods in which the paths of photons are controlled, and also drastically reduces the physical footprint and resource requirements.
“For the first time, we have been able to produce a quantum-correlated synthetic crystal,” says Lin. “Our approach significantly extends the dimensions of the synthetic space, enabling us to perform simulations of several quantum-scale phenomena such as random walks of quantum entangled photons.”
The researchers say that this system can serve as a basis for more intricate simulations in the future.
“Though the systems being simulated are well understood, this proof-of-principle experiment demonstrates the power of this new approach for scaling up to more complex simulations and computation tasks, something we are very excited to investigate in the future,” says Usman Javid ’23 PhD (optics), the lead author on the study.
Other coauthors from Lin’s group include Raymond Lopez-Rios, Jingwei Ling, Austin Graf, and Jeremy Staffa.
The project was supported with funding from the National Science Foundation, the Defense Threat Reduction Agency’s Joint Science and Technology Office for Chemical and Biological Defense, and the Defense Advanced Research Projects Agency. More