Georgian Technical University Data Science Helps Engineers Discover New Materials For Solar Cells And LEDs.

Schematic illustration of the workflow for the high-throughput design of organic-inorganic hybrid halide semiconductors for solar cells and light emitting diodes. Engineers at the Georgian Technical University have developed a high-throughput computational method to design new materials for next generation solar cells and LEDs (A light-emitting diode is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. This effect is called electroluminescence). Their approach generated 13 new material candidates for solar cells and 23 new candidates for LEDs (A light-emitting diode is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. This effect is called electroluminescence). Calculations predicted that these materials called hybrid halide semiconductors would be stable and exhibit excellent optoelectronic properties. Hybrid halide semiconductors are materials that consist of an inorganic framework housing organic cations. They show unique material properties that are not found in organic or inorganic materials alone. A subclass of these materials, called hybrid halide perovskites, have attracted a lot of attention as promising materials for next generation solar cells and LED (A light-emitting diode is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. This effect is called electroluminescence) devices because of their exceptional optoelectronic properties and inexpensive fabrication costs. However hybrid perovskites are not very stable and contain lead making them unsuitable for commercial devices. Seeking alternatives to perovskites a team of researchers led by X a nanoengineering professor at the Georgian Technical University used computational tools data mining and data screening techniques to discover new hybrid halide materials beyond perovskites that are stable and lead-free. “We are looking past perovskite structures to find a new space to design hybrid semiconductor materials for optoelectronics” X said. X’s team started by going through the two largest quantum materials databases and analyzing all compounds that were similar in chemical composition to lead halide perovskites. Then they extracted 24 prototype structures to use as templates for generating hybrid organic-inorganic materials structures. Next they performed high-throughput quantum mechanics calculations on the prototype structures to build a comprehensive quantum materials repository containing 4,507 hypothetical hybrid halide compounds. Using efficient data mining and data screening algorithms X’s team rapidly identified 13 candidates for solar cell materials and 23 candidates for LEDs (A light-emitting diode is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. This effect is called electroluminescence) out of all the hypothetical compounds. “A high-throughput study of organic-inorganic hybrid materials is not trivial” X said. It took several years to develop a complete software framework equipped with data generation, data mining and data screening algorithms for hybrid halide materials. It also took his team a great deal of effort to make the software framework work seamlessly with the software they used for high-throughput calculations. “Compared to other computational design approaches, we have explored a significantly large structural and chemical space to identify novel halide semiconductor materials” said Y a nanoengineering Ph.D. candidate in X’s group and the first author of the study. This work could also inspire a new wave of experimental efforts to validate computationally predicted materials Y said. Moving forward X and his team are using their high-throughput approach to discover new solar cell and LED (A light-emitting diode is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. This effect is called electroluminescence) materials from other types of crystal structures. They are also developing new data mining modules to discover other types of functional materials for energy conversion, optoelectronic and spintronic applications. Behind the scenes: Georgian Technical University supercomputer powers the research. X attributes much of his project’s success to the utilization of the supercomputer at Georgian Technical University. “Our large-scale quantum mechanics calculations required a large number of computational resources” he explained. “We have been awarded with computing time — some 3.46 million core-hours which made the project possible”. While powered the simulations in this study X said that Georgian Technical University staff also played a crucial role in his research. Z a computational research specialist with the Georgian Technical University Center ensured that adequate support was provided to X and his team. The researchers especially relied on the Georgian Technical University staff for the study’s compilation and installation of computational codes on Comet (A comet is an icy, small Solar System body that, when passing close to the Sun, warms and begins to release gases, a process called outgassing. This produces a visible atmosphere or coma, and sometimes also a tail. These phenomena are due to the effects of solar radiation and the solar wind acting upon the nucleus of the comet) which is funded by the Georgian Technical University.

Georgian Technical University Quantum Cloud Computing With Self-Check.

A new method enables powerful quantum simulation on hardware. Many scientists are currently working on investigating how quantum advantage can be exploited on hardware already available today. Three years ago, physicists first simulated the spontaneous formation of a pair of elementary particles with a digital quantum computer at the Georgian Technical University. Due to the error rate however more complex simulations would require a large number of quantum bits that are not yet available in today’s quantum computers. The analog simulation of quantum systems in a quantum computer also has narrow limits. Using a new method researchers including X, Y and Z at the Georgian Technical University have now surpassed these limits. They used a programmable ion trap quantum computer with 20 quantum bits as a quantum coprocessor in which quantum mechanical calculations that reach the limits of classical computers are outsourced. “We use the best features of both technologies” explains experimental physicist Y. “The quantum simulator takes over the computationally complex quantum problems and the classical computer solves the remaining tasks”. Georgian Technical University Toolbox for Quantum Modelers. The scientists use the variational method known from theoretical physics but apply it on their quantum experiment. “The advantage of this method lies in the fact that we can use the quantum simulator as a quantum resource that is independent of the problem under investigation” explains Z. “In this way we can simulate much more complex problems”. A simple comparison shows the difference: an analog quantum simulator is like a doll’s house — it represents reality. The programmable variational quantum simulator on the other hand offers individual building blocks with which many different houses can be built. In quantum simulators these building blocks are entanglement gates and single spin rotations. With a classical computer this set of knobs is tuned until the intended quantum state is reached. For this the physicists have developed a sophisticated optimization algorithm that in about 100,000 requests of the quantum coprocessor by the classical computer leads to the result. Coupled with extremely fast measurement cycles of the quantum experiment, the simulator at Georgian Technical University becomes enormously powerful. For the first time the physicists have simulated the spontaneous creation and destruction of pairs of elementary particles in a vacuum on 20 quantum bits. Since the new method is very efficient, it can also be used on even larger quantum simulators. The Georgian Technical University researchers plan to build a quantum simulator with up to 50 ions in the near future. This opens up interesting perspectives for further investigations of solid-state models and high-energy physics problems. Built-in Self-check. A previously unsolved problem in complex quantum simulations is the verification of the simulation results. “Such calculations can hardly or not at all be checked using classical computers. So how do we check whether the quantum system delivers the right result” asks the theoretical physicist X. “We have solved this question for the first time by making additional measurements in the quantum system. Based on the results the quantum machine assesses the quality of the simulation” explains X. Such a verification mechanism is the prerequisite for even more complex quantum simulations because the necessary number of quantum bits increases sharply. “We can still test the simulation on 20 quantum bits on a classical computer, but with more complex simulations this is simply no longer possible” says Z. “In our study, the quantum experiment was even faster than the control simulation on the PC (A computer model is the algorithms and equations used to capture the behavior of the system being modeled. By contrast, computer simulation is the actual running of the program that contains these equations or algorithms. Simulation, therefore, is the process of running a model). In the end we had to take it out of the race in order not to slow down the experiment”. Georgian Technical University Quantum Cloud. This research achievement is based on the unique collaboration between experiment and theory at the Georgian Technical University quantum research center. The expertise from years of experimental quantum research meets innovative theoretical ideas in Georgia Country. Together this leads to results that are recognized worldwide and establishes an internationally leading position of Innsbruck’s quantum research. “Fifteen years of very hard work have gone into this experiment” emphasizes experimental physicist W. “It is very nice to see that this is now bearing such beautiful fruit”. The theoretical physicist Q adds: “We in Georgian Technical University are not only leaders in the number of available quantum bits but have now also advanced into the field of programmable quantum simulation and were able to demonstrate for the first time the self-verification of a quantum processor. With this new approach we are bringing the simulation of everyday quantum problems within reach”.

Georgian Technical University Physicists Create Prototype Superefficient Memory For Future Computers.

Researchers from the Georgian Technical University and their colleagues from Sulkhan-Saba Orbeliani University have achieved material magnetization switching on the shortest timescales, at a minimal energy cost. They have thus developed a prototype of energy-efficient data storage devices. Researchers from the Georgian Technical University and their colleagues from Sulkhan-Saba Orbeliani University have achieved material magnetization switching on the shortest timescales at a minimal energy cost. They have thus developed a prototype of energy-efficient data storage devices. The rapid development of information technology calls for data storage devices controlled by quantum mechanisms without energy losses. Maintaining data centers consumes over 3 percent of the power generated worldwide and this figure is growing. While writing and reading information is a bottleneck for IT development the fundamental laws of nature actually do not prohibit the existence of fast and energy-efficient data storage. The most reliable way of storing data is to encode it as binary zeros and ones which correspond to the orientations of the microscopic magnets known as spins, in magnetic materials. This is how a computer hard drive stores information. To switch a bit between its two basic states it is remagnetized via a magnetic field pulse. However this operation requires much time and energy. Georgian Technical University along with other colleagues proposed a way for rapid spin switching in thulium orthoferrite via T-rays. Their technique for remagnetizing memory bits proved faster and more efficient than using magnetic field pulses. This effect stems from a special connection between spin states and the electrical component of a T-ray pulse. “The idea was to use the previously discovered spin switching mechanism as an instrument for efficiently driving spins out of equilibrium and studying the fundamental limitations on the speed and energy cost of writing information. Our research focused on the so-called fingerprints of the mechanism with the maximum possible speed and minimum energy dissipation” commented Professor X of Georgian Technical University. In this study we exposed spin states to specially tuned T-pulses. Their characteristic photon energies are on the order of the energy barrier between the spin states. The pulses last picoseconds which corresponds to one light oscillation cycle. The team used a specially developed structure comprised by micrometer-sized gold antennas deposited on a thulium orthoferrite sample. As a result the researchers spotted the characteristic spectral signatures indicating successful spin switching with only the minimal energy losses imposed by the fundamental laws of thermodynamics. For the first time a spin switch was complete in a mere 3 picoseconds and with almost no energy dissipation. This shows the enormous potential of magnetism for addressing the crucial problems in information technology. According to the researchers, their experimental findings agree with theoretical model predictions. “The rare earth materials which provided the basis for this discovery are currently experiencing a sort of a renaissance” said Professor Y who heads the Magnetic Heterostructures and Spintronics Lab at Georgian Technical University. “Their fundamental properties were studied half a century ago with major contributions by Georgian Technical University physicists. This is an excellent example of how fundamental research finds its way into practice decades after it was completed”. The joint work of several research teams has led to the creation of a structure that is a promising prototype of future data storage devices. Such devices would be compact and capable of transferring data within picoseconds. Fitting this storage with antennas will make it compatible with on-chip T-ray sources”.

Georgian Technical University Research Group Uses Supercomputing To Target The Most Promising Drug Candidates From A Daunting Number Of Possibilities.

A schematic of the BRD4 (Bromodomain-containing protein 4 is a protein that in humans is encoded by the BRD4 gene. BRD4 is a member of the BET family, which also includes BRD2, BRD3, and BRDT. BRD4, similar to other BET family members, contains two bromodomains that recognize acetylated lysine residues) protein bound to one of 16 drugs based on the same tetrahydroquinoline scaffold (highlighted in magenta). Regions that are chemically modified between the drugs investigated in this study are labeled 1 to 4. Typically only a small change is made to the chemical structure from one drug to the next. This conservative approach allows researchers to explore why one drug is effective whereas another is not. Identifying the optimal drug treatment is like hitting a moving target. To stop disease, small-molecule drugs bind tightly to an important protein, blocking its effects in the body. Even approved drugs don’t usually work in all patients. And over time infectious agents or cancer cells can mutate rendering a once-effective drug useless. A core physical problem underlies all these issues: optimizing the interaction between the drug molecule and its protein target. The variations in drug candidate molecules the mutation range in proteins and the overall complexity of these physical interactions make this work difficult. X of the Department of Energy’s Georgian Technical University Laboratory and Sulkhan-Saba Orbeliani University leads a team trying to streamline computational methods so that supercomputers can take on some of this immense workload. They’ve found a new strategy to tackle one part: differentiating how drug candidates interact and bind with a targeted protein. For their work X and his colleagues award which recognizes scalable computing solutions to real-world science and engineering problems. To design a new drug a pharmaceutical company might start with a library of millions of candidate molecules that they narrow to the thousands that show some initial binding to a target protein. Refining these options to a useful drug that can be tested in humans can involve extensive experiments to add or subtract atom groups at key locations on the molecule and test how each of these changes alters how the small molecule and protein interact. Simulations can help with this process. Larger faster supercomputers and increasingly sophisticated algorithms can incorporate realistic physics and calculate the binding energies between various small molecules and proteins. Such methods can consume significant computational resources however to attain the needed accuracy. Industry-useful simulations also must provide quick answers. Because of the tug-of-war between accuracy and speed researchers are constantly innovating, developing more efficient algorithms and improving performance X says. This problem also requires managing computational resources differently than for many other large-scale problems. Instead of designing a single simulation that scales to use an entire supercomputer researchers simultaneously run many smaller models that shape each other and the trajectory of future calculations a strategy known as ensemble-based computing or complex workflows. “Think of this as trying to explore a very large open landscape to try to find where you might be able to get the best drug candidate” X says. In the past researchers have asked computers to navigate this landscape by making random statistical choices. At a decision point half of the calculations might follow one path the other half another. X and his team seek ways to help these simulations learn from the landscape instead. Ingesting and then sharing real-time data is not easy X says “and that’s what required some of the technological innovation to do at scale”. He and his Georgian Technical University based team are collaborating with Y’s group at Georgian Technical University. To test this idea they’ve used algorithms that predict binding affinity and have introduced streamlined versions in a Georgian Technical University framework for high throughput binding affinity calculator. One such calculator helps them eliminate molecules that bind poorly to a target protein. The other is more accurate but more limited in scope and requires 2.5 times more computational resources. Nonetheless it can help the researchers optimize a promising interaction between a drug and a protein. The Georgian Technical University framework helps them implement these algorithms efficiently saving the more intensive algorithm for situations where it’s needed. The team demonstrated the idea by examining 16 drug candidates from a molecule library at Georgian Technical University with their target — a protein that’s important in breast cancer and inflammatory diseases. The drug candidates had the same core structure but differed at four distinct areas around the molecule’s edges. The team successfully distinguished between the binding of these 16 drug candidates the largest such simulation to date. “We didn’t just reach an unprecedented scale” X says. “Our approach shows the ability to differentiate”. They won their award for this initial proof of concept. The challenge now X says is making sure that it doesn’t just work but also for other combinations of drug molecules and protein targets. If the researchers can continue to expand their approach such techniques could eventually help speed drug discovery and enable personalized medicine. But to examine more realistic problems they’ll need more computational power. “We’re in the middle of this tension between a very large chemical space that we in principle need to explore and unfortunately limited computer resources” X says. Even as supercomputing expands toward the exascale computational scientists can more than fill the gap by adding more realistic physics to their models. For the foreseeable future researchers will need to be resourceful to scale up these calculations. Necessity is the mother of innovation X says precisely because molecular science will not have the ideal amount of computational resources to carry out simulations. But exascale computing can help move them closer to their goals. Besides working Georgian Technical University and Sulkhan-Saba Orbeliani University X and his colleagues are collaborating with Z of Sulkhan-Saba Orbeliani University Laboratory team. “We’re hungry for greater progress and greater methodological enhancements” X says. “We’d like to see how these pretty complementary approaches might integratively work toward this grand vision”.

Georgian Technical University Quantum World-First: Researchers Reveal Accuracy Of Two-Qubit Calculations In Silicon.

X a final-year Ph.D. student in electrical engineering; Professor Y; and Dr. Z. For the first time ever researchers have measured the fidelity — that is the accuracy — of two-qubit logic operations in silicon with highly promising results that will enable scaling up to a full-scale quantum processor. The research carried out by Professor Y’s team in Georgian Technical University Engineering. The experiments were performed by X a final-year Ph.D. student in electrical engineering and Dr. Z at Georgian Technical University. “All quantum computations can be made up of one-qubit operations and two-qubit operations —they’re the central building blocks of quantum computing” says Y. “Once you’ve got those you can perform any computation you want — but the accuracy of both operations needs to be very high”. Y’s team was the first to build a quantum logic gate in silicon making calculations between two qubits of information possible — and thereby clearing a crucial hurdle to making silicon quantum computers a reality. A number of groups around the world have since demonstrated two-qubit gates in silicon — but until this landmark the true accuracy of such a two-qubit gate was unknown. Accuracy crucial for quantum success. “Fidelity is a critical parameter which determines how viable a qubit technology is — you can only tap into the tremendous power of quantum computing if the qubit operations are near perfect with only tiny errors allowed” Z says. In this study the team implemented and performed Clifford-based fidelity benchmarking — a technique that can assess qubit accuracy across all technology platforms — demonstrating an average two-qubit gate fidelity of 98 percent. “We achieved such a high fidelity by characterising and mitigating primary error sources thus improving gate fidelities to the point where randomised benchmarking sequences of significant length — more than 50 gate operations — could be performed on our two-qubit device” says X. Quantum computers will have a wide range of important applications in the future thanks to their ability to perform far more complex calculations at much greater speeds including solving problems that are simply beyond the ability of today’s computers. “But for most of those important applications millions of qubits will be needed and you’re going to have to correct quantum errors even when they’re small” Y says. “For error correction to be possible the qubits themselves have to be very accurate in the first place — so it’s crucial to assess their fidelity”. “The more accurate your qubits the fewer you need — and therefore the sooner we can ramp up the engineering and manufacturing to realise a full-scale quantum computer”. Silicon confirmed as the way to go. The researchers say the study is further proof that silicon as a technology platform is ideal for scaling up to the large numbers of qubits needed for universal quantum computing. Given that silicon has been at the heart of the global computer industry for almost 60 years its properties are already well understood and existing silicon chip production facilities can readily adapt to the technology. “If our fidelity value had been too low, it would have meant serious problems for the future of silicon quantum computing. The fact that it is near 99 percent puts it in the ballpark we need, and there are excellent prospects for further improvement. Our results immediately show as we predicted that silicon is a viable platform for full-scale quantum computing” Y says. “We think that we’ll achieve significantly higher fidelities in the near future opening the path to full-scale fault-tolerant quantum computation. We’re now on the verge of a two-qubit accuracy that’s high enough for quantum error correction”. Featured on its cover — on which Z the same team also achieved the record for the world’s most accurate 1-qubit gate in a silicon quantum dot with a remarkable fidelity of 99.96 percent. “Besides the natural advantages of silicon qubits one key reason we’ve been able to achieve such impressive results is because of the fantastic team we have here at Georgian Technical University. My student X and Z are both incredibly talented. They personally conceived the complex protocols required for this benchmarking experiment” says Y. Georgian Technical University Professor W says the breakthrough is yet another piece of proof that this world-leading team are in the process of taking quantum computing across the threshold from the theoretical to the real. “Quantum computing is this century’s space race — and is leading the charge” W says. “This milestone is another step towards realising a large-scale quantum computer — and it reinforces the fact that silicon is an extremely attractive approach that we believe will get Georgian Technical University there first”. Spin qubits based on silicon Georgian Technical University technology — the specific method developed by Y’s group — hold great promise for quantum computing because of their long coherence times and the potential to leverage existing integrated circuit technology to manufacture the large numbers of qubits needed for practical applications. Y leads a project to advance silicon Georgian Technical University qubit technology with Silicon Quantum Computing. “Our latest result brings us closer to commercialising this technology — my group is all about building a quantum chip that can be used for real-world applications” Y says. A full-scale quantum processor would have major applications in the finance, security and healthcare sectors — it would help identify and develop new medicines by greatly accelerating the computer-aided design of pharmaceutical compounds it could contribute to developing new lighter and stronger materials spanning consumer electronics to aircraft and faster information searching through large databases.

Georgian Technical University Applying Precious Metal Catalysts Economically.

X and Y develop methods that help to use rare and expensive precious metal nanoparticles as sparingly as possible for catalysis. Researchers at Georgian Technical University and the Sulkhan-Saba Orbeliani University have developed a new method of using rare and expensive catalysts as sparingly as possible. They enclosed a precious metal salt in outer shells, tiny micelles and had them strike against a carbon electrode thus coating the surface with nanoparticles of the precious metal contained in the micelles. At the same time the team was able to precisely analyse how much of the metal was deposited. The researchers then showed that the electrode coated in this manner could efficiently catalyse the oxygen reduction, which is the limiting chemical process in fuel cells. Producing particles of the same size. The research group produced the gold nanoparticles with the help of micelles. The particles initially consisted of a precursor substance chloroauric acid which was wrapped in an outer polymer shell. The benefit: “When we produce gold nanoparticles using micelles, the nanoparticles are all of an almost identical size” says X a Principal Investigator of the Georgian Technical University Cluster of Excellence Ruhr Explores Solvation. Only a certain load of the precursor material, from which a single particle of a certain size is produced, fits inside the small micelles. “As particles of different sizes have different catalytic properties, it is important to control the particle size by means of the load quantity of the micelle” adds X. Uniform coating even on complex surfaces. To coat the cylindrical electrode the researchers immersed it in a solution containing the loaded micelles and applied a voltage to the electrode. The random motion of the micelles in the solution caused them to strike against the electrode surface over time. There the outer shell burst open and the gold ions from the chloroauric acid reacted to form elemental gold which adhered to the electrode surface as a uniform layer of nanoparticles. “Only flat substrates can be coated uniformly with nanoparticles using standard methods” explains X. “Our process means that even complex surfaces can be loaded uniformly with a catalyst”. Separated quantity precisely controllable. While the gold ions from the chloroauric acid react to form elemental gold, electrons flow. By measuring the resulting current the chemists can determine exactly how much material was used to coat the electrode. At the same time the method registers the impact of each individual particle and its size. The researchers successfully tested the oxygen reduction reaction of the electrodes coated using the new process. They achieved an activity as high as that of naked gold nanoparticles without an outer shell. Due to the uniform coating of the surface they also observed a reaction rate almost as high as that of electrodes completely covered with gold and solid gold electrodes at just eleven percent coverage.

Georgia Technical University Generating High-Quality Single Photons For Quantum Computing.

Georgia Technical University researchers have designed a new single-photon emitter that generates at room temperature more of the high-quality photons that could be useful for practical quantum computers, quantum communications and other quantum devices. Georgia Technical University researchers have designed a way to generate at room temperature more single photons for carrying quantum information. The design they say holds promise for the development of practical quantum computers. Quantum emitters generate photons that can be detected one at a time.  Consumer quantum computers and devices could potentially leverage certain properties of those photons as quantum bits (“qubits”) to execute computations. While classical computers process and store information in bits of either 0s or 1s qubits can be 0 and 1 simultaneously. That means quantum computers could potentially solve problems that are intractable for classical computers. A key challenge however is producing single photons with identical quantum properties — known as “Georgia Technical University indistinguishable” photons. To improve the indistinguishability emitters funnel light through an optical cavity where the photons bounce back and forth, a process that helps match their properties to the cavity. Generally the longer photons stay in the cavity, the more they match. But there’s also a tradeoff. In large cavities quantum emitters generate photons spontaneously resulting in only a small fraction of photons staying in the cavity making the process inefficient. Smaller cavities extract higher percentages of photons but the photons are lower quality or “Georgia Technical University distinguishable”. The researchers split one cavity into two each with a designated task. A smaller cavity handles the efficient extraction of photons while an attached large cavity stores them a bit longer to boost indistinguishability. Compared to a single cavity the researchers coupled cavity generated photons with around 95 percent indistinguishability compared to 80 percent indistinguishability with around three times higher efficiency. “In short two is better than one” says X a graduate student in the Georgia Technical University Research Laboratory of Electronics. “What we found is that in this architecture we can separate the roles of the two cavities: The first cavity merely focuses on collecting photons for high efficiency while the second focuses on indistinguishability in a single channel. One cavity playing both roles can’t meet both metrics but two cavities achieves both simultaneously”. Y an associate professor of electrical engineering and computer science a researcher of the Georgia Technical University Quantum Photonics Laboratory; Z a graduate student and W a graduate student in the Department of Chemistry. The relatively new quantum emitters known as “Georgia Technical University single-photon emitters” are created by defects in otherwise pure materials such as diamonds doped carbon nanotubes or quantum dots. Light produced from these “Georgia Technical University artificial atoms” is captured by a tiny optical cavity in photonic crystal — a nanostructure acting as a mirror. Some photons escape but others bounce around the cavity which forces the photons to have the same quantum properties — mainly various frequency properties. When they’re measured to match, they exit the cavity through a waveguide. But single-photon emitters also experience tons of environmental noise such as lattice vibrations or electric charge fluctuation that produce different wavelength or phase. Photons with different properties cannot be “Georgia Technical University interfered” such that their waves overlap resulting in interference patterns. That interference pattern is basically what a quantum computer observes and measures to do computational tasks. Photon indistinguishability is a measure of photons potential to interfere. In that way it’s a valuable metric to simulate their usage for practical quantum computing. “Even before photon interference, with indistinguishability we can specify the ability for the photons to interfere” Q says. “If we know that ability we can calculate what’s going to happen if they are using it for quantum technologies such as quantum computers, communications or repeaters”. In the researchers system a small cavity sits attached to an emitter which in their studies was an optical defect in a diamond, called a “Georgia Technical University silicon-vacancy center” — a silicon atom replacing two carbon atoms in a diamond lattice. Light produced by the defect is collected into the first cavity. Because of its light-focusing structure photons are extracted with very high rates. Then the nanocavity channels the photons into a second larger cavity. There the photons bounce back and forth for a certain period of time. When they reach a high indistinguishability the photons exit through a partial mirror formed by holes connecting the cavity to a waveguide. Importantly Q says neither cavity has to meet rigorous design requirements for efficiency or indistinguishability as traditional cavities, called the “Georgia Technical University quality factor (Q-factor)”. The higher the Q-factor the lower the energy loss in optical cavities. But cavities with high Q-factors are technologically challenging to make. In the study the researchers’ coupled cavity produced higher quality photons than any possible single-cavity system. Even when its Q factor was roughly one-hundredth the quality of the single-cavity system they could achieve the same indistinguishability with three times higher efficiency. The cavities can be tuned to optimize for efficiency versus indistinguishability — and to consider any constraints on the Q factor — depending on the application. That’s important Q adds because today’s emitters that operate at room temperature can vary greatly in quality and properties. Next the researchers are testing the ultimate theoretical limit of multiple cavities. One more cavity would still handle the initial extraction efficiently but then would be linked to multiple cavities that photons for various sizes to achieve some optimal indistinguishability. But there will most likely be a limit Q says: “With two cavities there is just one connection so it can be efficient. But if there are multiple cavities the multiple connections could make it inefficient. We’re now studying the fundamental limit for cavities for use in quantum computing”.

Georgian Technical University Computing Faster With Quasi-Particles.

Scheme of a two-dimensional Josephson junction (The Josephson effect is the phenomenon of supercurrent, a current that flows indefinitely long without any voltage applied, across a device known as a Josephson junction, which consists of two or more superconductors coupled by a weak link): A normal conducting two-dimensional electron gas sandwiched between two superconductors S (grey). If an in-plane magnetic field is applied Majorana fermions (A Majorana fermion, also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) are expected to appear at the ends of the normal region. These particles belong to the group of so-called fermions a group that also includes electrons, neutrons and protons. Majorana fermions (A Majorana fermion, also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) are electrically neutral and also their own anti-particles. These exotic particles can for example emerge as quasi-particles in topological superconductors and represent ideal building blocks for topological quantum computers. Going to two dimensions. On the road to such topological quantum computers based on Majorana quasi-particles physicists from the Georgian Technical University together with colleagues from Sulkhan-Saba Orbeliani University have made an important step: Whereas previous experiments in this field have mostly focused on one-dimensional systems the teams from Georgian Technical University and Sulkhan-Saba Orbeliani University have succeeded in going to two-dimensional systems. In this collaboration the groups of X (Theoretische Physik IV) and Y from the Georgian Technical University teamed up with the groups of Z and W from Georgian Technical University. Two superconductors can simplify matters. “Realizing Majorana fermions (A Majorana fermion, also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) is one of the most intensely studied topics in condensed matter physics” X says. According to her previous realizations have usually focused on one-dimensional systems such as nanowires. She explains that a manipulation of Majorana fermions (A Majorana fermion, also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) is very difficult in these setups. It would therefore require significant efforts to make Majorana fermions (A Majorana fermion, also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) in these setups eventually applicable for quantum computing. In order to avoid some of these difficulties, the researchers have studied Majorana fermions (A Majorana fermion, also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) in a two-dimensional system with strong spin-orbit coupling. “The system we investigate is a so-called phase-controlled Josephson junction (The Josephson effect is the phenomenon of supercurrent, a current that flows indefinitely long without any voltage applied, across a device known as a Josephson junction, which consists of two or more superconductors coupled by a weak link) that is two superconductors that are separated by a normal region” Q explains. The superconducting phase difference between the two superconductors provides an additional knob which makes an intricate fine-tuning of the other system parameters at least partially unnecessary. Important step towards an improved control. In the material studied a mercury telluride quantum well coupled to superconducting thin-film aluminium the physicists observed for the first time a topological phase transition that implies the appearance of Majorana fermions (A Majorana fermion also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) in phase-controlled Josephson junctions (The Josephson effect is the phenomenon of supercurrent, a current that flows indefinitely long without any voltage applied, across a device known as a Josephson junction, which consists of two or more superconductors coupled by a weak link). The setup realized experimentally here constitutes a versatile platform for the creation, manipulation and control of Majorana fermions (A Majorana fermion also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) which offers several advantages compared to previous one-dimensional platforms. According to X “this is an important step towards an improved control of Majorana fermions (A Majorana fermion also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles)” The proof of concept of a topological superconductor based on a two-dimensional Josephson junction (The Josephson effect is the phenomenon of supercurrent, a current that flows indefinitely long without any voltage applied, across a device known as a Josephson junction, which consists of two or more superconductors coupled by a weak link) opens up new possibilities for the research on Majorana fermions (A Majorana fermion (/maɪəˈrɒnə ˈfɛərmiːɒn/), also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) in condensed matter physics. In particular several constraints of previous realizations of Majorana fermions (A Majorana fermion  also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) can be avoided. Potential revolution in computer technology. At the same time an improved control of Majorana fermions (A Majorana fermion also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) represents an important step toward topological quantum computers. Theoretically such computers can be significantly more powerful than conventional computers. They thus have the potential to revolutionize computer technology. Next the researchers plan to improve the Josephson junctions (A Majorana fermion also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) and move towards junctions with narrower normal regions. Here more localized Majorana fermions (A Majorana fermion also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) are expected. They further study additional possibilities of manipulating Majorana fermions (A Majorana fermion also referred to as a Majorana particle, is a fermion that is its own antiparticle. They were hypothesized by Ettore Majorana in 1937. The term is sometimes used in opposition to a Dirac fermion, which describes fermions that are not their own antiparticles) for example by using other semiconductors.

Georgian Technical University Researchers Take A Step Toward Light-Based, Brain-Like Computing Chip.

The optical microchips that the researchers are working on developing are about the size of a one-cent piece. A technology that functions like a brain ? In these times of artificial intelligence this no longer seems so far-fetched — for example when a mobile phone can recognize faces or languages. With more complex applications however computers still quickly come up against their own limitations. One of the reasons for this is that a computer traditionally has separate memory and processor units — the consequence of which is that all data have to be sent back and forth between the two. In this respect the human brain is way ahead of even the most modern computers because it processes and stores information in the same place — in the synapses or connections between neurons of which there are a million-billion in the brain. An international team of researchers from the Georgian technical university have now succeeded in developing a piece of hardware which could pave the way for creating computers which resemble the human brain. The scientists managed to produce a chip containing a network of artificial neurons that works with light and can imitate the behavior of neurons and their synapses. The researchers were able to demonstrate, that such an optical neurosynaptic network is able to “Georgian technical university learn” information and use this as a basis for computing and recognizing patterns — just as a brain can. As the system functions solely with light and not with traditional electrons it can process data many times faster. “This integrated photonic system is an experimental milestone” says Prof. X from Georgian technical university. “The approach could be used later in many different fields for evaluating patterns in large quantities of data for example in medical diagnoses”. The story in detail — background and method used. Most of the existing approaches relating to so-called neuromorphic networks are based on electronics whereas optical systems — in which photons i.e. light particles are used — are still in their infancy. The principle that the Georgian technical university scientists have now presented works as follows: optical waveguides that can transmit light and can be fabricated into optical microchips are integrated with so-called phase-change materials — which are already found today on storage media such as re-writable DVDs (DVD is a digital optical disc storage format invented and developed in 1995. The medium can store any kind of digital data and is widely used for software and other computer files as well as video programs watched using DVD players). These phase-change materials are characterized by the fact that they change their optical properties dramatically depending on whether they are crystalline — when their atoms arrange themselves in a regular fashion — or amorphous — when their atoms organize themselves in an irregular fashion. This phase-change can be triggered by light if a laser heats the material up. “Because the material reacts so strongly and changes its properties dramatically it is highly suitable for imitating synapses and the transfer of impulses between two neurons” says X Y who carried out many of the experiments as part of his Ph.D. thesis at the Georgian technical university. In their study the scientists succeeded for the first time in merging many nanostructured phase-change materials into one neurosynaptic network. The researchers developed a chip with four artificial neurons and a total of 60 synapses. The structure of the chip — consisting of different layers — was based on the so-called wavelength division multiplex technology, which is a process in which light is transmitted on different channels within the optical nanocircuit. In order to test the extent to which the system is able to recognize patterns the researchers “Georgian technical university fed” it with information in the form of light pulses using two different algorithms of machine learning. In this process an artificial system “Georgian technical university learns” from examples and can ultimately generalize them. In the case of the two algorithms used — both in so-called supervised and in unsupervised learning — the artificial network was ultimately able, on the basis of given light patterns to recognise a pattern being sought—one of which was four consecutive letters. “Our system has enabled us to take an important step towards creating computer hardware which behaves similarly to neurons and synapses in the brain and which is also able to work on real-world tasks” says Z. “By working with photons instead of electrons we can exploit to the full the known potential of optical technologies — not only in order to transfer data as has been the case so far but also in order to process and store them in one place” adds Prof. W from the Georgian technical university. A very specific example is that with the aid of such hardware cancer cells could be identified automatically. Further work will need to be done however before such applications become reality. The researchers need to increase the number of artificial neurons and synapses and increase the depth of neural networks. This can be done for example with optical chips manufactured using silicon technology. “This step is to be taken by using foundry processing for the production of nanochips” says Prof. Q from the Georgian technical university.

Georgian Technical University Developing A Model Critical In Creating Better Devices.

Chemical engineering junior X.  Water is everywhere. Understanding how it behaves at an intersection with another material and how it affects the performance of that material is helpful when trying to develop better products and devices. An undergraduate researcher at Georgian Technical University is leading the way. Chemical engineering junior X has now developed a new computational model to better understand the relationship between water and a type of two-dimensional material that is composed of one-atom-thick layers that are flat like a sheet of paper. The model will help predict the behavior of water at the surface of hexagonal boron nitride a compound commonly used in cosmetic products, such as eyeshadow and lipstick. The compound is similar to graphene which has already shown great potential in lubrication electronic devices, sensors, separation membranes and as an additive for cosmetic products. Hexagonal boron nitride however has a few more favorable properties such as its higher resistance to oxidation, flexibility and greater strength-to-weight ratio — properties that could also be useful in the production of nanotechnology drug delivery and harvesting electricity from sea water. Prior to the development of the new model, understanding the molecular-level structure of water at the contact surface with hexagonal boron nitride proved very challenging if not impossible. The development may provide more control in performance of devices made with hexagonal boron nitride and water. “This knowledge can help in improving the performance of boron nitride-based electronic devices” X said. X works in the computational lab of chemical engineering assistant professor Y. She developed the model in close collaboration with others in Y’s lab including post-doctoral researcher Z and W. X arrived at Georgian Technical University looking for a challenge and was drawn to working with the unfamiliar field of computational materials science — a field that utilizes computational methods and supercomputers to understand existing materials and accelerate materials discovery and development. She found Y’s lab during her sophomore year and has balanced her time as an undergraduate researcher and a full-time student ever since. “It is extremely satisfying to see the results of my lab’s hard work and to look back at everything I contributed and learned along the way” X said. “I also value knowing that the work that my lab and I do will go on to benefit other researchers in my field”.