An electrically driven on-chip light source of entangled photon pairs is developed by combining an InP gain section and Si3N4 microrings. A pair generation rate of 8,200 counts s−1 and a coincidence-to-accidental ratio of more than 80 are achieved around the wavelength of 1,550 nm.
We introduce quantum circuit learning (QCL) as an emerging regression algorithm for chemo-and materials-informatics. The supervised model, functioning on the rule of quantum mechanics, can process linear and smooth non-linear functions from small datasets (100 records). Compared with conventional algorithms, such as random forest, support vector machine, and linear regressions, the QCL can offer better predictions with some one-dimensional functions and experimental chemical databases. QCL will potentially help the virtual exploration of new molecules and materials more efficiently through its superior prediction performances.
Fellow scientists labeled him a crackpot. Now Stuart Hameroff’s quantum consciousness theories are getting support from unlikely places.
The “spooky action at a distance” that once unnerved Einstein may be on its way to being as pedestrian as the gyroscopes that currently measure acceleration in smartphones.
Quantum entanglement significantly improves the precision of sensors that can be used to navigate without GPS, according to a new study in Nature Photonics.
“By exploiting entanglement, we improve both measurement sensitivity and how quickly we can make the measurement,” said Zheshen Zhang, associate professor of electrical and computer engineering at the University of Michigan and co-corresponding author of the study. The experiments were done at the University of Arizona, where Zhang was working at the time.
There are high expectations that quantum computers may deliver revolutionary new possibilities for simulating chemical processes. This could have a major impact on everything from the development of new pharmaceuticals to new materials. Researchers at Chalmers University have now, for the first time in Sweden, used a quantum computer to undertake calculations within a real-life case in chemistry.
“Quantum computers could in theory be used to handle cases where electrons and atomic nuclei move in more complicated ways. If we can learn to utilize their full potential, we should be able to advance the boundaries of what is possible to calculate and understand,” says Martin Rahm, Associate Professor in Theoretical Chemistry at the Department of Chemistry and Chemical Engineering, who has led the study.
Within the field of quantum chemistry, the laws of quantum mechanics are used to understand which chemical reactions are possible, which structures and materials can be developed, and what characteristics they have. Such studies are normally undertaken with the help of super computers, built with conventional logical circuits. There is however a limit for which calculations conventional computers can handle. Because the laws of quantum mechanics describe the behavior of nature on a subatomic level, many researchers believe that a quantum computer should be better equipped to perform molecular calculations than a conventional computer.
Hydrogen, the most abundant element in the universe, is found everywhere from the dust filling most of outer space to the cores of stars to many substances here on Earth. This would be reason enough to study hydrogen, but its individual atoms are also the simplest of any element with just one proton and one electron. For David Ceperley, a professor of physics at the University of Illinois Urbana-Champaign, this makes hydrogen the natural starting point for formulating and testing theories of matter.
Ceperley, also a member of the Illinois Quantum Information Science and Technology Center, uses computer simulations to study how hydrogen atoms interact and combine to form different phases of matter like solids, liquids, and gases. However, a true understanding of these phenomena requires quantum mechanics, and quantum mechanical simulations are costly. To simplify the task, Ceperley and his collaborators developed a machine learning technique that allows quantum mechanical simulations to be performed with an unprecedented number of atoms. They reported in Physical Review Letters that their method found a new kind of high-pressure solid hydrogen that past theory and experiments missed.
“Machine learning turned out to teach us a great deal,” Ceperley said. “We had been seeing signs of new behavior in our previous simulations, but we didn’t trust them because we could only accommodate small numbers of atoms. With our machine learning model, we could take full advantage of the most accurate methods and see what’s really going on.”
Providing increased resistance to outside interference, topological qubits create a more stable foundation than conventional qubits. This increased stability allows the quantum computer to perform computations that can uncover solutions to some of the world’s toughest problems.
While qubits can be developed in a variety of ways, the topological qubit will be the first of its kind, requiring innovative approaches from design through development. Materials containing the properties needed for this new technology cannot be found in nature—they must be created. Microsoft brought together experts from condensed matter physics, mathematics, and materials science to develop a unique approach producing specialized crystals with the properties needed to make the topological qubit a reality.
Donald Hoffman interview on spacetime, consciousness, and how biological fitness conceals reality. We discuss Nima Arkani-Hamed’s Amplituhedron, decorated permutations, evolution, and the unlimited intelligence.
The Amplituhedron is a static, monolithic, geometric object with many dimensions. Its volume codes for amplitudes of particle interactions & its structure codes for locality and unitarity. Decorated permutations are the deepest core from which the Amplituhedron gets its structure. There are no dynamics, they are monoliths as in 2001: A Space Odyssey.
Continue reading “Exposing the Strange Blueprint Behind ‘Reality’ (Donald Hoffman Interview)” »
Interfacial superconductivity and the quantum anomalous Hall effect have been developed by layer-by-layer material fabrication.
A new method created by Pritzker School of Molecular Engineering (PME) researchers can help determine the origin of electronic states in designed materials.
Assistant Professor Shuolong Yang and his colleagues created a method for better understanding magnetic topological insulators, which have unique surface properties that could make them useful in quantum information science technologies.
A giant orbital magnetic moment exists in graphene quantum dots, according to new work by physicists at the University of California Santa Cruz in the US. As well as being of fundamental interest for studying systems with relativistic electrons – that is those travelling at near-light speeds – the work could be important for quantum information science since these moments could encode information.
Graphene, a sheet of carbon just one atom thick, has a number of unique electronic properties, many of which arise from the fact that it is a semiconductor with a zero-energy gap between its valence and conduction bands. Near where the two bands meet, the relationship between the energy and momentum of charge carriers (electrons and holes) in the material is described by the Dirac equation and resembles that of a photon, which is massless.
These bands, called Dirac cones, enable the charge carriers to travel through graphene at extremely high, “ultra-relativistic” speeds approaching that of light. This extremely high mobility means that graphene-based electronic devices such as transistors could be faster than any that exist today.
