Aurelia Butler

Given the rapid pace at which technology is developing, it comes as no surprise that quantum technologies will become commonplace within decades. A big part of ushering in this new age of quantum computing requires a new understanding of both classical and quantum information and how the two can be related to each other.
Before one can send classical information across quantum channels, it needs to be encoded first. This encoding is done by means of quantum ensembles. A quantum ensemble refers to a set of quantum states, each with its own probability. To accurately receive the transmitted information, the receiver has to repeatedly ‘guess’ the state of the information being sent. This constitutes a cost function that is called ‘guesswork.’ Guesswork refers to the average number of guesses required to correctly guess the state.
The concept of guesswork has been studied at length in classical ensembles, but the subject is still new for quantum ensembles. Recently, a research team from Japan — consisting of Prof. Takeshi Koshiba of Waseda University, Michele Dall’Arno from Waseda University and Kyoto University, and Prof. Francesco Buscemi from Nagoya University — has derived analytical solutions to the guesswork problem subject to a finite set of conditions. “The guesswork problem is fundamental in many scientific areas in which machine learning techniques or artificial intelligence are used. Our results trailblaze an algorithmic aspect of the guesswork problem,” says Koshiba. Their findings are published in IEEE Transactions on Information Theory.
To begin with, the researchers considered a common formalism of quantum circuits that relates the transmitted state of a quantum ensemble ? to the quantum measurement ?. They next introduced the probability distributions for both the quantum ensemble and the numberings obtained from the quantum measurement. They then established the guesswork function. The guesswork function maps any pair of ? and ? into the expectation value of the tth guess (where t refers to the guess number), averaged over the probability distribution of the tth guess being correct. Finally, they minimized the guesswork function over the elements of ? and used this result to derive analytical solutions to the guesswork problem subject to a finite set of conditions.
These solutions included the explicit solution to a qubit ensemble with a uniform probability distribution. “Previously, results for analytical solutions have been known only for binary and symmetric ensembles. Our calculation for ensembles with a uniform probability distribution extends these,” explains Koshiba. The research team also calculated the solutions for a qubit regular polygonal ensemble, and a qubit regular polyhedral ensemble.
“Guesswork is a very basic scientific problem, but there is very little research on quantum guesswork and even less on the algorithmic implications of quantum guesswork. Our paper goes a little way towards filling that gap,” concludes Koshiba.
While the consequences of these findings may not be immediately obvious, in the future they are sure to have a major influence on quantum science, such as quantum chemistry for drug development and quantum software for quantum computing.
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Researchers have demonstrated a prototype glasses-free 3D light field display system with a significantly extended viewing distance thanks to a newly developed flat lens. The system is an important step toward compact, realistic-looking 3D displays that could be used for televisions, portable electronics and table-top devices.
Light field displays use a dense field of light rays to produce full-color real-time 3D videos that can be viewed without glasses. This approach to creating a 3D display allows several people to view the virtual scene at once, much like a real 3D object.
“Most light field 3D displays have a limited viewing range, which causes the 3D virtual image to degrade as the observer moves farther away from the device,” said research team leader Wen Qiao from Soochow University. “The nanostructured flat lens we designed is just 100 microns thick and has a very large depth of focus, which enables a high-quality virtual 3D scene to be seen from farther away.”
In Optica, an Optica Publishing Group journal, the researchers report that their prototype display exhibits high efficiency and high color fidelity over viewing distances from 24 cm to 90 cm. These characteristics all combine to create a more realistic viewing experience.
“We developed this new technology in hopes of creating displays that could allow people to feel as if they were actually together during a video conference,” said Qiao. “With the continued development of nanotechnology, we envision that glasses-free 3D displays will become a normal part of everyday life and will change the way people interact with computers.”
Creating multiple views
Light field displays create realistic images by projecting different views that allow the 3D scene to look the same when looked at from different angles. The focal length of the lenses used to create these views is the limiting factor when it comes to viewing distance. More

A recent study has revealed how future structures of MOPs can be predicted and designed at the molecular level. The discovery of new structures holds tremendous promise for accessing advanced functional materials in energy and environmental applications. Although cage-based porous materials, metal-organic polyhedra (MOPs), are attracting attention as an emerging functional platform for numerous applications, hardly predictable and seemingly uncontrollable packing structures remain an open question. There is a high demand for a roadmap for discovering and rationally designing new MOP structures.
A research team, led by Professor Wonyoung Choe in the Department of Chemistry at Ulsan National Institute of Science and Technology (UNIST), South Korea, has made a major leap forward in revealing how future structures of MOPs can be predicted and designed at the molecular level. Their findings are expected to create a new paradigm for accelerating materials development and application of MOPs.
Prior to MOPs, metal-organic frameworks (MOFs), another well-known class of porous material, have developed rapidly. MOFs share compositional similarities (i.e., metal clusters and organic ligands) to MOPs. However, the molecular building blocks of MOFs are connected in an extended manner, while discrete cages consisting of metal clusters and organic ligands are packed by weak interactions in MOPs. Unlike MOPs, thousands of MOFs have been synthesized since their first discovery and now they are becoming increasingly important materials in academia and industries alike. A major driving force behind the phenomenal success of MOFs is their predictable and designable structures with a rich choice of molecular building blocks. By considering the molecular geometry of building blocks, the possible structures can be predicted and designed.
So far, it was believed that strong bonds to connect building blocks are necessary to construct structures in a predictable way. Since weak or non-directional interactions have often resulted in unpredictable structures, the rational design of MOPs has been less illuminated. In this study, the research team discovered a special type of MOPs where the design principle can be applied to molecular packing systems, despite the absence of strong bonds. The zirconium (Zr)-based MOPs are notable examples. The authors unveiled multiple weaker bonds can do a similar role to strong bonds.
Zr-based MOPs are an emerging class of MOPs with their excellent chemical stability. While the Zr-MOPs are essentially cage-based compounds, features mainly found in MOFs, such as robust framework and permanent porosity, also appear in Zr-MOPs. The authors say that such extraordinary dual features motivated them to further investigate the solid-state packing of Zr-MOPs. In this study, the authors not only provided a comprehensive study of the existing structures but also discovered future structures that have not been observed but are potentially accessible. A fundamental understanding of the nanoscale self-assembly of cages provides opportunities to control the packing structure, porosity, and properties. The authors expected that these unique dual features of Zr-MOPs can lead to many intriguing applications that are not accessible by typical MOPs or MOFs. They also encouraged to find other interesting classes of cage-based frameworks.
“The emergence of new structures would provide a new opportunity to control their properties,” said Professor Wonyoung Choe. “Taking a different perspective on cage-based frameworks can lead to a new stage of functional porous materials.”
The findings of this research have been published as a Perspective in Chem, a sister journal to Cell, on March 10, 2022. This study has been supported by the National Research Foundation (NRF) of Korea via the Mid-Career Researcher Program, Hydrogen Energy Innovation Technology Development Project, Science Research Center (SRC), and Global Ph.D. Fellowship (GPF), as well as Korea Environment Industry & Technology Institute (KEITI) through Public Technology Program based on Environmental Policy Program, funded by Korea Ministry of Environment (MOE).
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Materials provided by Ulsan National Institute of Science and Technology(UNIST). Original written by JooHyeon Heo. Note: Content may be edited for style and length. More

Scientists from the Faculty of Engineering, Information and Systems at the University of Tsukuba devised a text message mediation robot that can help users control their anger when receiving upsetting news. This device may help improve social interactions as we move towards a world with increasingly digital communications.
While a quick text message apology is a fast and easy way for friends to let us know they are going to be late for a planned meet up, it is often missing the human element that would accompany an explanation face-to-face, or even over the phone. It is likely to be more upsetting when we are not able to perceive the emotional weight behind our friends’ regret at making us wait.
Now, researchers at the University of Tsukuba have built a handheld robot they called OMOY, which was equipped with a movable weight actuated by mechanical components inside its body. By shifting the internal weight, the robot could express simulated emotions. The robot was deployed as a mediator for reading text messages. A text with unwelcome or frustrating news could be followed by an exhortation by OMOY to not get upset, or even sympathy for the user. “With the medium of written digital communication, the lack of social feedback redirect focus from the sender and onto the content of the message itself,” author Professor Fumihide Tanaka says. The mediator robot was designed so that it can suppress the user’s anger and other negative interpersonal motivations, such as thoughts of revenge, and instead fostered forgiveness.
The researchers tested 94 people with a message like “I’m sorry, I am late. The appointment slipped my mind. Can you wait another hour?” The team found that OMOY was able to reduce negative emotions. “The mediator robot can relay a frustrating message followed by giving its own opinion. When this speech is accompanied by the appropriate weight shifts, we saw that that the user would perceive the ‘intention’ of the robot to help them calm down,” Professor Tanaka says.
The robot’s body expression produced by weight shifts did not require any specific external components, such as arms or legs, which implied that the internal weight movements could reduce a user’s anger or other negative emotions without the use of rich body gestures or facial expressions.
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After a stroke, patients may lose feeling in an arm or experience weakness and reduced movement that limits their ability to complete basic daily activities. Traditional rehabilitation therapy is very intensive, time-consuming and can be both expensive and inconvenient, especially for rural patients travelling long distances to in-person therapy appointments.
That’s why a team of researchers, including one at the University of Missouri, utilized a motion-sensor video game, Recovery Rapids, to allow patients recovering from a stroke to improve their motor skills and affected arm movements at home while checking in periodically with a therapist via telehealth.
The researchers found the game-based therapy led to improved outcomes similar to a highly regarded form of in-person therapy, known as constraint-induced therapy, while only requiring one-fifth of the therapist hours. This approach saves time and money while increasing convenience and safety as telehealth has boomed in popularity during the COVID-19 pandemic.
“As an occupational therapist, I have seen patients from rural areas drive more than an hour to come to an in-person clinic three to four days a week, where the rehab is very intensive, taking three to four hours per session, and the therapist must be there the whole time,” said Rachel Proffitt, assistant professor in the MU School of Health Professions. “With this new at-home gaming approach, we are cutting costs for the patient and reducing time for the therapist while still improving convenience and overall health outcomes, so it’s a win-win. By saving time for the therapists, we can also now serve more patients and make a broader impact on our communities.”
Traditional rehab home exercises tend to be very repetitive and monotonous, and patients rarely adhere to them. The Recovery Rapids game helps patients look forward to rehabilitation by completing various challenges in a fun, interactive environment, and the researchers found that the patients adhered well to their prescribed exercises.
“The patient is virtually placed in a kayak, and as they go down the river, they perform arm motions simulating paddling, rowing, scooping up trash, swaying from side to side to steer, and reaching overhead to clear out spider webs and bats, so it’s making the exercises fun,” said Rachel Proffitt, assistant professor in the MU School of Health Professions. “As they progress, the challenges get harder, and we conduct check-ins with the participants via telehealth to adjust goals, provide feedback and discuss the daily activities they want to resume as they improve.”
Nearly 800,000 Americans have a stroke each year according to the CDC, and two-thirds of stroke survivors report they cannot use their affected limbs to do normal daily activities, including making a cup of coffee, cooking a meal or playing with one’s grandchildren.
“I am passionate about helping patients get back to all the activities they love to do in their daily life,” Proffitt said. “Anything we can do as therapists to help in a creative way while saving time and money is the ultimate goal.”
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In the simplest terms, superconductivity between two or more objects means zero wasted electricity. It means electricity is being transferred between these objects with no loss of energy.
Many naturally occurring elements and minerals like lead and mercury have superconducting properties. And there are modern applications that currently use materials with superconducting properties, including MRI machines, maglev trains, electric motors and generators. Usually, superconductivity in materials happens at low-temperature environments or at high temperatures at very high pressures. The holy grail of superconductivity today is to find or create materials that can transfer energy between each other in a non-pressurized room-temperature environment.
If the efficiency of superconductors at room temperature could be applied at scale to create highly efficient electric power transmission systems for industry, commerce, and transportation, it would be revolutionary. The deployment of the technology of room temperature superconductors at atmospheric pressure would accelerate the electrification of our world for its sustainable development. The technology allows us to do more work and use less natural resources with lower waste to preserve the environment.
There are a few superconducting material systems for electric transmission in various stages of development. In the meantime, researchers at the University of Houston are conducting experiments to look for superconductivity in a room-temperature and atmospheric pressure environment.
Paul Chu, founding director and chief scientist at the Texas Center for Superconductivity at UH and Liangzi Deng, research assistant professor, chose FeSe (Iron (II) Selenide) for their experiments because it has a simple structure and also great Tc (superconducting critical temperature) enhancement under pressure.
Chu and Deng have developed a pressure-quench process (PQP), in which they first apply pressure to their samples at room-temperature to enhance superconductivity, cool them to a chosen lower temperature, and then completely release the applied pressure, while still retaining the enhanced superconducting properties.
The concept of the PQP is not new, but Chu and Deng’s PQP is the first time it’s been used to retain the high-pressure-enhanced superconductivity in a high-temperature superconductor (HTS) at atmospheric pressure. The findings are published in the Journal of Superconductivity and Novel Magnetism.
“We waste about 10% of our electricity during transmission, that’s a huge number. If we had superconductors to transmit electricity with zero energy wasted, we would basically change the world, transportation and electricity transmission would be revolutionized, “Chu said. “If this process can be used, we can create materials that could transmit electricity from the place where you produce it all the way to places thousands of miles away without the loss of energy.”
Their process was inspired by the late Pol Duwez, a prominent material scientist, engineer and metallurgist at the California Institute of Technology who pointed out that most of the alloys used in industrial applications are metastable or chemically unstable at atmospheric pressure and room temperature, and these metastable phases possess desired and/or enhanced properties that their stable counterparts lack, Chu and Deng noted in their study.
Examples of these materials include diamonds, high-temperature 3D-printing materials, black phosphorus and even beryllium copper, which is notably used to make tools for use in high explosive environments like oil rigs and grain elevators.
“The ultimate goal of this experiment was to raise the temperature to above room temperature while keeping the material’s superconducting properties,” Chu said. “If that can be achieved, cryogenics will no longer be needed to operate machines that used superconducting material like an MRI machine and that’s why we’re excited about this.”
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Omar Maddouri, a doctoral student in the Department of Electrical and Computer Engineering at Texas A&M University, is working with Dr. Byung-Jun Yoon, professor, and Dr. Edward Dougherty, Robert M. Kennedy ’26 Chair Professor, to evaluate machine-learning models using transfer learning principles. Dr. Francis “Frank” Alexander with Brookhaven National Labs and Dr. Xiaoning Qian from the Department of Electrical and Computer Engineering at Texas A&M University are also involved with the project.
In data-driven machine learning, models are built to make predictions and estimations for what’s to come in any given data set. One important field within machine learning is classification, which allows a data set to be assessed by an algorithm and then classified or broken down into classes or categories. When the data sets provided are very small, it can be very challenging to not only build a classification model based on this data but also to evaluate the performance of this model, ensuring its accuracy. This is where transfer learning comes into play.
“In transfer learning, we try to transfer knowledge or bring data from another domain to see whether we can enhance the task that we are doing in the domain of interest, or target domain,” Maddouri explained.
The target domain is where the models are built, and their performance is evaluated. The source domain is a separate domain that is still relevant to the target domain from which knowledge is transferred to make the analysis within the target domain easier.
Maddouri’s project utilizes a joint prior density to model the relatedness between the source and target domains and offers a Bayesian approach to apply the transfer learning principles to provide an overall error estimator of the models. An error estimator will deliver an estimate of how accurate these machine-learning models are at classifying the data sets at hand.
What this means is that before any data is observed, the team creates a model using their initial inferences about the model parameters in the target and source domains and then updates this model with enhanced accuracy as more evidence or information about the data sets becomes available.
This technique of transfer learning has been used to build models in previous works; however, no one has ever before used this transfer learning technique to propose novel error estimators to evaluate the performance of these models. For an efficient utilization, the devised estimator has been implemented using advanced statistical methods that enabled a fast screening of source data sets which enhances the computational complexity of the transfer learning process by 10 to 20 times.
This technique can help serve as a benchmark for future research within academia to build upon. In addition, it can help with identifying or classifying different medical issues that would otherwise be very difficult. For example, Maddouri utilized this technique to classify patients with schizophrenia using transcriptomic data from brain tissue samples originally acquired by invasive brain biopsies. Because of the nature and the location of the brain region that can be analyzed for this disorder, the data collected is very limited. However, using a stringent feature selection procedure that comprises differential gene expression analysis and statistical testing for assumptions validity, the research team identified transcriptomic profiles of three genes from an additional brain region found to be highly relevant to the desired brain tissue as reported by independent research studies from other literature.
This knowledge allowed them to utilize the transfer learning technique to leverage samples collected from the second brain region (source domain) to help with the analysis and significantly boost the accuracy of diagnosis within the original brain region (target domain). The data gathered from the source domain can be exploratory in the absence of information from the target domain, allowing the research team to enhance the quality of their conclusion.
This research has been funded by the Department of Energy and the National Science Foundation.
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Materials provided by Texas A&M University. Original written by Rachel Rose. Note: Content may be edited for style and length. More