Aurelia Butler

A research team from Osaka University, The University of Tokyo, and Tokyo Institute of Technology revealed the microscopic origin of the large magnetoelectric effect in interfacial multiferroics composed of the ferromagnetic Co2FeSi Heusler alloy and the piezoelectric material. They observed element-specific changes in the orbital magnetic moments in the interfacial multiferroic material using an X-ray Magnetic Circular Dichroism (XMCD) measurement under the application of an electric field, and they showed the change contributes to the large magnetoelectric effect.
The findings provide guidelines for designing materials with a large magnetoelectric effect, and it will be useful in developing new information writing technology that consumes less power in spintronic memory devices.
The research results will be shown in an article, “Strain-induced specific orbital control in a Heusler alloy-based interfacial multiferroics” published in NPG Asia Materials.
Controlling the direction of magnetization using low electric field is necessary for developing efficient spintronic devices. In spintronics, properties of an electron’s spin or magnetic moment are used to store information. The electron spins can be manipulated by straining orbital magnetic moments to create a high-performance magnetoelectric effect.
Japanese researchers, including Jun Okabayashi from the University of Tokyo, revealed a strain-induced orbital control mechanism in interfacial multiferroics. In multiferroic material, the magnetic property can be controlled using an electric field — potentially leading to efficient spintronic devices. The interfacial multiferroics that Okabayashi and his colleagues studied consist of a junction between a ferromagnetic material and a piezoelectric material. The direction of magnetization in the material could be controlled by applying voltage.
The team showed the microscopic origin of the large magnetoelectric effect in the material. The strain generated from the piezoelectric material could change the orbital magnetic moment of the ferromagnetic material. They revealed element-specific orbital control in the interfacial multiferroic material using reversible strain and provided guidelines for designing materials with a large magnetoelectric effect. The findings will be useful in developing new information writing technology that consumes less power. More

Moore’s Law, a fundamental scaling principle for electronic devices, forecasts that the number of transistors on a chip will double every two years, ensuring more computing power — but a limit exists.
Today’s most advanced chips house nearly 50 billion transistors within a space no larger than your thumbnail. The task of cramming even more transistors into that confined area has become more and more difficult, according to Penn State researchers.
In a study published today (Jan. 10) in the journal Nature, Saptarshi Das, an associate professor of engineering science and mechanics and co-corresponding author of the study, and his team suggest a remedy: seamlessly implementing 3D integration with 2D materials.
In the semiconductor world, 3D integration means vertically stacking multiple layers of semiconductor devices. This approach not only facilitates the packing of more silicon-based transistors onto a computer chip, commonly referred to as “More Moore,” but also permits the use of transistors made from 2D materials to incorporate diverse functionalities within various layers of the stack, a concept known as “More than Moore.”
With the work outlined in the study, Saptarshi and the team demonstrate feasible paths beyond scaling current tech to achieve both More Moore and More than Moore through monolithic 3D integration. Monolithic 3D integration is a fabrication process wherein researchers directly make the devices on the one below, as compared to the traditional process of stacking independently fabricated layers.
“Monolithic 3D integration offers the highest density of vertical connections as it does not rely on bonding of two pre-patterned chips — which would require microbumps where two chips are bonded together — so you have more space to make connections,” said Najam Sakib, graduate research assistant in engineering science and mechanics and co-author of the study.
Monolithic 3D integration faces significant challenges, though, according to Darsith Jayachandran, graduate research assistant in engineering science and mechanics and co-corresponding author of the study, since conventional silicon components would melt under the processing temperatures.

“One challenge is the process temperature ceiling of 450 degrees Celsius (C) for back-end integration for silicon-based chips — our monolithic 3D integration approach drops that temperate significantly to less than 200 C,” Jayachandran said, explaining that the process temperature ceiling is the maximum temperature allowed before damaging the prefabricated structures. “Incompatible process temperature budgets make monolithic 3D integration challenging with silicon chips, but 2D materials can withstand temperatures needed for the process.”
The researchers used existing techniques for their approach, but they are the first to successfully achieve monolithic 3D integration at this scale using 2D transistors made with 2D semiconductors called transition metal dichalcogenides.
The ability to vertically stack the devices in 3D integration also enabled more energy-efficient computing because it solved a surprising problem for such tiny things as transistors on a computer chip: distance.
“By stacking devices vertically on top of each other, you’re decreasing the distance between devices, and therefore, you’re decreasing the lag and also the power consumption,” said Rahul Pendurthi, graduate research assistant in engineering science and mechanics and co-corresponding author of the study.
By decreasing the distance between devices, the researchers achieved “More Moore.” By incorporating transistors made with 2D materials, the researchers met the “More than Moore” criterion as well. The 2D materials are known for their unique electronic and optical properties, including sensitivity to light, which makes these materials ideal as sensors. This is useful, the researchers said, as the number of connected devices and edge devices — things like smartphones or wireless home weather stations that gather data on the ‘edge’ of a network — continue to increase.
“‘More Than Moore’ refers to a concept in the tech world where we are not just making computer chips smaller and faster, but also with more functionalities,” said Muhtasim Ul Karim Sadaf, graduate research assistant in engineering science and mechanics and co-author of the study. “It is about adding new and useful features to our electronic devices, like better sensors, improved battery management or other special functions, to make our gadgets smarter and more versatile.”
Using 2D devices for 3D integration has several other advantages, the researchers said. One is superior carrier mobility, which refers to how an electrical charge is carried in semiconductor materials. Another is being ultra-thin, enabling the researchers to fit more transistors on each tier of the 3D integration and enable more computing power.

While most academic research involves small-scale prototypes, this study demonstrated 3D integration at a massive scale, characterizing tens of thousands of devices. According to Das, this achievement bridges the gap between academia and industry and could lead to future partnerships where industry leverages Penn State’s 2D materials expertise and facilities. The advance in scaling was enabled by the availability of high-quality, wafer-scale transition metal dichalcogenides developed by researchers at Penn State’s Two-Dimensional Crystal Consortium (2DCC-MIP), a U.S. National Science Foundation (NSF) Materials Innovation Platform and national user facility.
“This breakthrough demonstrates yet again the essential role of materials research as the foundation of the semiconductor industry and U.S. competitiveness,” said Charles Ying, program director for NSF’s Materials Innovation Platforms. “Years of effort by Penn State’s Two-Dimensional Crystal Consortium to improve the quality and size of 2D materials have made it possible to achieve 3D integration of semiconductors at a size that can be transformative for electronics.”
According to Das, this technological advancement is only the first step.
“Our ability to demonstrate, at wafer scale, a huge number of devices shows that we have been able to translate this research to a scale which can be appreciated by the semiconductor industry,” Das said. “We have put 30,000 transistors in each tier, which may be a record number. This puts Penn State in a very unique position to lead some of the work and partner with the U.S. semiconductor industry in advancing this research.”
Along with Das, Jayachandran, Pendurthi, Sadaf and Sakib, other authors include Andrew Pannone, doctoral student in engineering science and mechanics; Chen Chen, assistant research professor in 2DCC-MIP; Ying Han, postdoctoral researcher in mechanical engineering; Nicholas Trainor, doctoral student in materials science and engineering; Shalini Kumari, postdoctoral scholar; Thomas McKnight, doctoral student in materials science and engineering; Joan Redwing, director of the 2DCC-MIP and distinguished professor of materials science and engineering and of electrical engineering; and Yang Yang, assistant professor of engineering science and mechanics.
The U.S. National Science Foundation and Army Research Office supported this research. More

Researchers have developed a new technology that uses meta-optical devices to perform thermal imaging. The approach provides richer information about imaged objects, which could broaden the use of thermal imaging in fields such as autonomous navigation, security, thermography, medical imaging and remote sensing.
“Our method overcomes the challenges of traditional spectral thermal imagers, which are often bulky and delicate due to their reliance on large filter wheels or interferometers,” said research team leader Zubin Jacob from Purdue University. “We combined meta-optical devices and cutting-edge computational imaging algorithms to create a system that is both compact and robust while also having a large field of view.”
In Optica, Optica Publishing Group’s journal for high-impact research, the authors describe their new spectro-polarimetric decomposition system, which uses a stack of spinning metasurfaces to break down thermal light into its spectral and polarimetric components. This allows the imaging system to capture the spectral and polarization details of thermal radiation in addition to the intensity information that is acquired with traditional thermal imaging.
The researchers showed that the new system can be used with a commercial thermal camera to successfully classify various materials, a task that is typically challenging for conventional thermal cameras. The method’s ability to distinguish temperature variations and identify materials based on spectro-polarimetric signatures could help boost safety and efficiency for a variety of applications, including autonomous navigation.
“Traditional autonomous navigation approaches rely heavily on RGB cameras, which struggle in challenging conditions like low light or bad weather,” said the paper’s first author Xueji Wang, a postdoctoral researcher at Purdue University. “When integrated with heat-assisted detection and ranging technology, our spectro-polarimetric thermal camera can provide vital information in these difficult scenarios, offering clearer images than RGB or conventional thermal cameras. Once we achieve real-time video capture, the technology could significantly enhance scene perception and overall safety.”
Doing more with a smaller imager
Spectro-polarimetric imaging in the long-wave infrared is crucial for applications such as night vision, machine vision, trace gas sensing and thermography. However, today’s spectro-polarimetric long-wave infrared imagers are bulky and limited in spectral resolution and field of view.

To overcome these limitations the researchers turned to large-area metasurfaces — ultra-thin structured surfaces that can manipulate light in complex ways. After engineering spinning dispersive metasurfaces with tailored infrared responses, they developed a fabrication process that allowed these metasurfaces to be used to create large-area (2.5-cm diameter) spinning devices suitable for imaging applications. The resulting spinning stack measures less than 10 x 10 x 10 cm and can be used with a traditional infrared camera.
“Integrating these large-area meta-optical devices with computational imaging algorithms facilitated the efficient reconstruction of the thermal radiation spectrum,” said Wang. “This enabled a more compact, robust and effective spectro-polarimetric thermal imaging system than was previously achievable.”
Classifying materials with thermal imaging
To evaluate their new system, the researchers spelled out “Purdue” using various materials and microstructures, each with unique spectro-polarimetric properties. Using the spectro-polarimetric information acquired with the system, they accurately distinguished the different materials and objects. They also demonstrated a three-fold increase in material classification accuracy compared to traditional thermal imaging methods, highlighting the system’s effectiveness and versatility.
The researchers say that the new method could be especially useful for applications that require detailed thermal imaging. “In security, for example, it could revolutionize airport systems by detecting concealed items or substances on people,” said Wang. “Moreover, its compact and robust design enhances its suitability for diverse environmental conditions, making it particularly beneficial for applications such as autonomous navigation.”
In addition to working to achieve video capture with the system, the researchers are trying to enhance the technique’s spectral resolution, transmission efficiency and speed of image capture and processing. They also plan to improve the metasurface design to enable more complex light manipulation for higher spectral resolution. Additionally, they want to extend the method to room-temperature imaging since the use of metasurface stacks restricted the method to high-temperature objects. They plan to do this using improved materials, metasurface designs and techniques like anti-reflection coatings. More

From “Law and Order” to “CSI,” not to mention real life, investigators have used fingerprints as the gold standard for linking criminals to a crime. But if a perpetrator leaves prints from different fingers in two different crime scenes, these scenes are very difficult to link, and the trace can go cold.
It’s a well-accepted fact in the forensics community that fingerprints of different fingers of the same person — “intra-person fingerprints” — are unique, and therefore unmatchable.
Research led by Columbia Engineering undergraduate
A team led by Columbia Engineering undergraduate senior Gabe Guo challenged this widely held presumption. Guo, who had no prior knowledge of forensics, found a public U.S. government database of some 60,000 fingerprints and fed them in pairs into an artificial intelligence-based system known as a deep contrastive network. Sometimes the pairs belonged to the same person (but different fingers), and sometimes they belonged to different people.
AI has potential to greatly improve forensic accuracy
Over time, the AI system, which the team designed by modifying a state-of-the-art framework, got better at telling when seemingly unique fingerprints belonged to the same person and when they didn’t. The accuracy for a single pair reached 77%. When multiple pairs were presented, the accuracy shot significantly higher, potentially increasing current forensic efficiency by more than tenfold. The project, a collaboration between Hod Lipson’s Creative Machines lab at Columbia Engineering and Wenyao Xu’s Embedded Sensors and Computing lab at University at Buffalo, SUNY, was published today in Science Advances.
Study findings challenge-and surprise-forensics community
Once the team verified their results, they quickly sent the findings to a well-established forensics journal, only to receive a rejection a few months later. The anonymous expert reviewer and editor concluded that “It is well known that every fingerprint is unique,” and therefore it would not be possible to detect similarities even if the fingerprints came from the same person.

The team did not give up. They doubled down on the lead, fed their AI system even more data, and the system kept improving. Aware of the forensics community’s skepticism, the team opted to submit their manuscript to a more general audience. The paper was rejected again, but Lipson, who is the James and Sally Scapa Professor of Innovation in the Department of Mechanical Engineering and co-director of the Makerspace Facility, appealed. “I don’t normally argue editorial decisions, but this finding was too important to ignore,” he said. “If this information tips the balance, then I imagine that cold cases could be revived, and even that innocent people could be acquitted.”
While the system’s accuracy is not sufficient to officially decide a case, it can help prioritize leads in ambiguous situations. After more back and forth, the paper was finally accepted for publication by Science Advances.
Unveiled: a new kind of forensic marker to precisely capture fingerprints
One of the sticking points was the following question: What alternative information was the AI actually using that has evaded decades of forensic analysis? After careful visualizations of the AI system’s decision process, the team concluded that the AI was using a new kind of forensic marker.
“The AI was not using ‘minutiae,’ which are the branchings and endpoints in fingerprint ridges — the patterns used in traditional fingerprint comparison,” said Guo, who began the study as a first-year student at Columbia Engineering in 2021. “Instead, it was using something else, related to the angles and curvatures of the swirls and loops in the center of the fingerprint.”
Columbia Engineering senior Aniv Ray and PhD student Judah Goldfeder, who helped analyze the data, noted that their results are just the beginning. “Just imagine how well this will perform once it’s trained on millions, instead of thousands of fingerprints,” said Ray.

VIDEO: https://youtu.be/s5esfRbBc18
A need for broader datasets
The team is aware of potential biases in the data. The authors present evidence that indicates that the AI performs similarly across genders and races, where samples were available. However, they note, more careful validation needs to be done using datasets with broader coverage if this technique is to be used in practice.
Transformative potential of AI in a well-established field
This discovery is an example of more surprising things to come from AI, notes Lipson, . “Many people think that AI cannot really make new discoveries-that it just regurgitates knowledge,” he said. “But this research is an example of how even a fairly simple AI, given a fairly plain dataset that the research community has had lying around for years, can provide insights that have eluded experts for decades.”
He added, “Even more exciting is the fact that an undergraduate student, with no background in forensics whatsoever, can use AI to successfully challenge a widely held belief of an entire field. We are about to experience an explosion of AI-led scientific discovery by non-experts, and the expert community, including academia, needs to get ready.” More

In an effort to make the internet more accessible for people with disabilities, researchers at The Ohio State University have begun developing an artificial intelligence agent that could complete complex tasks on any website using simple language commands.
In the three decades since it was first released into the public domain, the world wide web has become an incredibly intricate, dynamic system. Yet because internet function is now so integral to society’s well-being, its complexity also makes it considerably harder to navigate.
Today there are billions of websites available to help access information or communicate with others, and many tasks on the internet can take more than a dozen steps to complete. That’s why Yu Su, co-author of the study and an assistant professor of computer science and engineering at Ohio State, said their work, which uses information taken from live sites to create web agents — online AI helpers — is a step toward making the digital world a less confusing place.
“For some people, especially those with disabilities, it’s not easy for them to browse the internet,” said Su. “We rely more and more on the computing world in our daily life and work, but there are increasingly a lot of barriers to that access, which, to some degree, widens the disparity.”
The study was presented in December at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), a flagship conference for AI and machine learning research.
By taking advantage of the power of large language models, the agent works similarly to how humans behave when browsing the web, said Su. The Ohio State team showed that their model was able to understand the layout and functionality of different websites using only its ability to process and predict language.
Researchers started the process by creating Mind2Web, the first dataset for generalist web agents. Though previous efforts to build web agents focused on toy simulated websites, Mind2Web fully embraces the complex and dynamic nature of real-world websites and emphasizes an agent’s ability of generalizing to entirely new websites it has never seen before. Su said that much of their success is due to their agent’s ability to handle the internet’s ever-evolving learning curve. The team lifted over 2,000 open-ended tasks from 137 different real-world websites, which they then used to train the agent.

Some of the tasks included booking one-way and round-trip international flights, following celebrity accounts on Twitter, browsing comedy films from 1992 to 2017 streaming on Netflix, and even scheduling car knowledge tests at the DMV. Many of the tasks were very complex — for example, booking one of the international flights used in the model would take 14 actions. Such effortless versatility allows for diverse coverage on a number of websites, and opens up a new landscape for future models to explore and learn in an autonomous fashion, said Su.
“It’s only become possible to do something like this because of the recent development of large language models like ChatGPT,” said Su. Since the chatbot became public in November 2022, millions of users have used it to automatically generate content, from poetry and jokes to cooking advice and medical diagnoses.
Still, because one website could contain thousands of raw HTML elements, it would be too costly to feed so much information to a single large language model. To address this gap, the study also introduces a framework called MindAct, a two-pronged agent that uses both small and large language models to carry out these tasks. The team found that by using this strategy, MindAct significantly outperforms other common modeling strategies and is able to understand various concepts at a decent level.
With more fine-tuning, the study points out, the model could likely be used in tandem with both open-and closed-source large language models such as Flan-T5 or GPT-4. However, their work does highlight an increasingly relevant ethical problem in creating flexible artificial intelligence, said Su. While it could certainly serve as a helpful agent to humans surfing the web, the model could also be used to enhance systems like ChatGPT and turn the entire internet into an unprecedentedly powerful tool, said Su.
“On the one hand, we have great potential to improve our efficiency and to allow us to focus on the most creative part of our work,” he said. “But on the other hand, there’s tremendous potential for harm.” For instance, autonomous agents able to translate online steps into the real world could influence society by taking potentially dangerous actions, such as misusing financial information or spreading misinformation.
“We should be extremely cautious about these factors and make a concerted effort to try to mitigate them,” said Su. But as AI research continues to evolve, he notes that it’s likely society will experience major growth in the commercial use and performance of generalist web agents in the years to come, especially as the technology has already gained so much popularity in the public eye.
“Throughout my career, my goal has always been trying to bridge the gap between human users and the computing world,” said Su. “That said, the real value of this tool is that it will really save people time and make the impossible possible.”
The research was supported by the National Science Foundation, the U.S. Army Research Lab and the Ohio Supercomputer Center. Other co-authors were Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang and Huan Sun, all of Ohio State. More

For the first time, researchers from the Structured Light Laboratory (School of Physics) at the University of the Witwatersrand in South Africa, led by Professor Andrew Forbes, in collaboration with string theorist Robert de Mello Koch from Huzhou University in China (previously from Wits University), have demonstrated the remarkable ability to perturb pairs of spatially separated yet interconnected quantum entangled particles without altering their shared properties.
“We achieved this experimental milestone by entangling two identical photons and customising their shared wave-function in such a way that their topology or structure becomes apparent only when the photons are treated as a unified entity,” explains lead author, Pedro Ornelas, an MSc student in the structured light laboratory.
This connection between the photons was established through quantum entanglement, often referred to as ‘spooky action at a distance’, enabling particles to influence each other’s measurement outcomes even when separated by significant distances. The research was published in Nature Photonics on 8 January 2024.
The role of topology and its ability to preserve properties, in this work, can be likened to how a coffee mug can be reshaped into the form of a doughnut; despite the changes in appearance and shape during the transformation, a singular hole — a topological characteristic — remains constant and unaltered. In this way, the two objects are topologically equivalent. “The entanglement between our photons is malleable, like clay in a potter’s hands, but during the moulding process, some features are retained,” explains Forbes.
The nature of the topology investigated here, termed Skyrmion topology, was initially explored by Tony Skyrme in the 1980s as field configurations displaying particle-like characteristics. In this context, topology refers to a global property of the fields, akin to a piece of fabric (the wave-function) whose texture (the topology) remains unchanged regardless of the direction in which it is pushed.
These concepts have since been realised in modern magnetic materials, liquid crystals, and even as optical analogues using classical laser beams. In the realm of condensed matter physics, skyrmions are highly regarded for their stability and noise resistance, leading to groundbreaking advancements in high-density data storage devices. “We aspire to see a similar transformative impact with our quantum-entangled skyrmions,” says Forbes.
Previous research depicted these Skyrmions as localised at a single location. “Our work presents a paradigm shift: the topology that has traditionally been thought to exist in a single and local configuration is now nonlocal or shared between spatially separated entities,” says Ornelas.

Expanding on this concept, the researchers utilise topology as a framework to classify or distinguish entangled states. They envisage that “this fresh perspective can serve as a labelling system for entangled states, akin to an alphabet!” says Dr Isaac Nape, a co-investigator.
“Similar to how spheres, doughnuts, and handcuffs are distinguished by the number of holes they contain, our quantum skyrmions can be differentiated by their topological aspects in the same fashion,” says Nape. The team hopes that this might become a powerful tool that paves the way for new quantum communication protocols that use topology as an alphabet for quantum information processing across entanglement based channels.
The findings reported in the article are crucial because researchers have grappled for decades with developing techniques to preserve entangled states. The fact that topology remains intact even as entanglement decays suggests a potentially new encoding mechanism that utilises entanglement, even in scenarios with minimal entanglement where traditional encoding protocols would fail.
“We will focus our research efforts on defining these new protocols and expanding the landscape of topological nonlocal quantum states,” says Forbes. More

A combination of only 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals. The identified proteins could be used to tailor treatments to the individual based on the expected severity of the disease. The study, led by researchers at Linköping University in Sweden, has been published in the journal Nature Communications.
“A combination of 11 proteins predicted both short and long-term disease activity and disability outcomes. We also concluded that it’s important to measure these proteins in cerebrospinal fluid, which better reflects what’s going on in the central nervous system, compared with measuring in the blood,” says Julia Åkesson, doctoral student at Linköping University and the University of Skövde.
In multiple sclerosis, the immune system attacks the person’s own body, damaging nerves in the brain and in the spinal cord. What is attacked primarily is a fatty compound called myelin, which surrounds and insulates the nerve axons so that signals can be transmitted. When myelin is damaged, transmission becomes less efficient.
Disease progression in multiple sclerosis varies considerably from person to person. To those for whom a more severe disease is predicted, it is important not to lose valuable time at the onset of the disease but to get the right treatment quickly. The researchers behind the current study, which is a collaboration between Linköping University, the Karolinska Institute and the University of Skövde, wanted to find out whether it was possible to detect at an early stage of disease which patients would require a more powerful treatment. Being able to do so would be relevant both to physicians and those living with MS.
“I think we’ve come one step closer to an analysis tool for selecting which patients would need more effective treatment in an early stage of the disease. But such a treatment may have side effects and be relatively expensive, and some patients don’t need it,” says Mika Gustafsson, professor of bioinformatics at the Department of Physics, Chemistry and Biology at Linköping University, who led the study.
Finding markers linked to disease severity many years ahead is a complicated challenge. In their study, the researchers analysed nearly 1,500 proteins in samples from 92 people with suspected or recently diagnosed MS. Data from the protein analyses were combined with a large amount of information from the patients’ journals, such as disability, results from MRI scans of the nervous system, and treatments received. Using machine learning, the researchers found a number of proteins that could predict disease progression.
“Having a panel consisting of only 11 proteins makes it easy should anyone want to develop analysis for this. It won’t be as costly as measuring 1,500 proteins, so we’ve really narrowed it down to make it useful for others wanting to take this further,” says Sara Hojjati, doctoral student at the Department of Biomedical and Clinical Sciences at Linköping University.

The research team also found that a specific protein, leaking from damaged nerve axons, is a reliable biomarker for disease activity in the short term. This protein is called neurofilament light chain, NfL. These findings confirm earlier research on the use of NfL to identify nerve damage and also suggest that the protein indicates how active the disease is.
One of the main strengths of the study is that the combination of proteins found in the patient group from which samples were taken at Linköping University Hospital was later confirmed in a separate group consisting of 51 MS patients sampled at the Karolinska University Hospital in Stockholm.
This study is the first to measure such a large amount of proteins with a highly sensitive method, proximity extension assay, combined with next-generation sequencing, PEA-NGS. This technology allows for high-accuracy measuring also of very small amounts, which is important as these proteins are often present in very low levels.
The study was funded by the Swedish Foundation for Strategic Research, the Swedish Brain Foundation, Knut and Alice Wallenberg Foundation, Margaretha af Ugglas Foundation, the Swedish Research Council, NEURO Sweden and the Swedish Foundation for MS research, and others. More

A study led by the University of Oxford has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the ‘reality gap’: the difference between predicted and observed behaviour from quantum devices. The results have been published in Physical Review X.
Quantum computing could supercharge a wealth of applications, from climate modelling and financial forecasting, to drug discovery and artificial intelligence. But this will require effective ways to scale and combine individual quantum devices (also called qubits). A major barrier against this is inherent variability: where even apparently identical units exhibit different behaviours.
Functional variability is presumed to be caused by nanoscale imperfections in the materials that quantum devices are made from. Since there is no way to measure these directly, this internal disorder cannot be captured in simulations, leading to the gap in predicted and observed outcomes.
To address this, the research group used a “physics-informed” machine learning approach to infer these disorder characteristics indirectly. This was based on how the internal disorder affected the flow of electrons through the device.
Lead researcher Associate Professor Natalia Ares (Department of Engineering Science, University of Oxford) said: ‘As an analogy, when we play “crazy golf” the ball may enter a tunnel and exit with a speed or direction that doesn’t match our predictions. But with a few more shots, a crazy golf simulator, and some machine learning, we might get better at predicting the ball’s movements and narrow the reality gap.’
The researchers measured the output current for different voltage settings across an individual quantum dot device. The data was input into a simulation which calculated the difference between the measured current with the theoretical current if no internal disorder was present. By measuring the current at many different voltage settings, the simulation was constrained to find an arrangement of internal disorder that could explain the measurements at all voltage settings. This approach used a combination of mathematical and statistical approaches coupled with deep learning.
Associate Professor Ares added: ‘In the crazy golf analogy, it would be equivalent to placing a series of sensors along the tunnel, so that we could take measurements of the ball’s speed at different points. Although we still can’t see inside the tunnel, we can use the data to inform better predictions of how the ball will behave when we take the shot.’
Not only did the new model find suitable internal disorder profiles to describe the measured current values, it was also able to accurately predict voltage settings required for specific device operating regimes.
Crucially, the model provides a new method to quantify the variability between quantum devices. This could enable more accurate predictions of how devices will perform, and also help to engineer optimum materials for quantum devices. It could inform compensation approaches to mitigate the unwanted effects of material imperfections in quantum devices.
Co-author David Craig, a PhD student at the Department of Materials, University of Oxford, added, ‘Similar to how we cannot observe black holes directly but we infer their presence from their effect on surrounding matter, we have used simple measurements as a proxy for the internal variability of nanoscale quantum devices. Although the real device still has greater complexity than the model can capture, our study has demonstrated the utility of using physics-aware machine learning to narrow the reality gap.’ More