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A new way to calculate the interaction between a metal and its alloying material could speed the hunt for a new material that combines the hardness of ceramic with the resilience of metal.

The discovery, made by engineers at the University of Michigan, identifies two aspects of this interaction that can accurately predict how a particular alloy will behave—and with fewer demanding, from-scratch quantum mechanical calculations.

“Our findings may enable the use of machine learning algorithms for alloy design, potentially accelerating the search for better alloys that could be used in turbine engines and nuclear reactors,” said Liang Qi, assistant professor of materials science and engineering who led the research.

Metasurfaces are optically thin metamaterials that can control the wavefront of light completely, although they are primarily used to control the phase of light. In a new report, Adam C. Overvig and colleagues in the departments of Applied Physics and Applied Mathematics at the Columbia University and the Center for Functional Nanomaterials at the Brookhaven National Laboratory in New York, U.S., presented a novel study approach, now published on Light: Science & Applications. The simple concept used meta-atoms with a varying degree of form birefringence and angles of rotation to create high-efficiency dielectric metasurfaces with ability to control optical amplitude (maximum extent of a vibration) and phase at one or two frequencies. The work opened applications in computer-generated holography to faithfully reproduce the phase and amplitude of a target holographic scene without using iterative algorithms that are typically required during phase-only holography.

The team demonstrated all-dielectric metasurface holograms with independent and complete control of the amplitude and phase. They used two simultaneous optical frequencies to generate two-dimensional (2-D) and 3D holograms in the study. The phase-amplitude metasurfaces allowed additional features that could not be attained with phase-only holography. The features included artifact-free 2-D holograms, the ability to encode separate phase and amplitude profiles at the object plane and encode intensity profiles at the metasurface and object planes separately. Using the method, the scientists also controlled the surface textures of 3D holographic objects.

Light waves possess four key properties including amplitude, phase, polarization and optical impedance. Materials scientists use metamaterials or “metasurfaces” to tune these properties at specific frequencies with subwavelength, spatial resolution. Researchers can also engineer individual structures or “meta-atoms” to facilitate a variety of optical functionalities. Device functionality is presently limited by the ability to control and integrate all four properties of light independently in the lab. Setbacks include challenges of developing individual meta-atoms with varying responses at a desired frequency with a single fabrication protocol. Research studies previously used metallic scatterers due to their strong light-matter interactions to eliminate inherent optical losses relative to metals while using lossless dielectric platforms for high-efficiency phase control—the single most important property for wavefront control.

Researchers at the Kavli Institute for the Physics and Mathematics of the Universe (WPI) and Tohoku University in Japan have recently identified an anomaly in the electromagnetic duality of Maxwell Theory. This anomaly, outlined in a paper published in Physical Review Letters, could play an important role in the consistency of string theory.

The recent study is a collaboration between Yuji Tachikawa and Kazuya Yonekura, two string theorists, and Chang-Tse Hsieh, a condensed matter theorist. Although the study started off as an investigation into string theory, it also has implications for other areas of physics.

In current physics theory, classical electromagnetism is described by Maxwell’s equations, which were first introduced by physicist James Clerk Maxwell around 1865. Objects governed by these equations include electric and magnetic fields, electrically charged particles (e.g., electrons and protons), and magnetic monopoles (i.e. hypothetical particles carrying single magnetic poles).

Physicists can explore tailored physical systems to rapidly solve challenging computational tasks by developing spin simulators, combinatorial optimization and focusing light through scattering media. In a new report on Science Advances, C. Tradonsky and a group of researchers in the Departments of Physics in Israel and India addressed the phase retrieval problem by reconstructing an object from its scattered intensity distribution. The experimental process addressed an existing problem in disciplines ranging from X-ray imaging to astrophysics that lack techniques to reconstruct an object of interest, where scientists typically use indirect iterative algorithms that are inherently slow.

In the new optical approach, Tradonsky et al conversely used a digital degenerate cavity laser (DDCL) mode to rapidly and efficiently reconstruct the object of interest. The experimental results suggested that the gain competition between the many lasing modes acted as a highly parallel computer to rapidly dissolve the phase retrieval problem. The approach applies to two-dimensional (2-D) objects with known compact support and complex-valued objects, to generalize imaging through scattering media, while accomplishing other challenging computational tasks.

To calculate the intensity distribution of light scattered far from an unknown object relatively easily, researchers can compute the source of the absolute value of an object’s Fourier transform. The reconstruction of an object from its scattered intensity distribution is, however, ill-posed, since phase information can be lost and diverse phase distributions in the work can result in different reconstructions. Scientists must therefore obtain prior information about an object’s shape, positivity, spatial symmetry or sparsity for more precise object reconstructions. Such examples are found in astronomy, short-pulse characterization studies, X-ray diffraction, radar detection, speech recognition and when imaging across turbid media. During the reconstruction of objects with a finite extent (compact support), researchers offer a unique solution to the phase retrieval problem, as long as they model the same scattered intensity at a sufficiently higher resolution.

A newly developed algorithm opens a window into understanding the transition from quantum to classical objects.

The universe is kind of an impossible object. It has an inside but no outside; it’s a one-sided coin. This Möbius architecture presents a unique challenge for cosmologists, who find themselves in the awkward position of being stuck inside the very system they’re trying to comprehend.

It’s a situation that Lee Smolin has been thinking about for most of his career. A physicist at the Perimeter Institute for Theoretical Physics in Waterloo, Canada, Smolin works at the knotty intersection of quantum mechanics, relativity and cosmology. Don’t let his soft voice and quiet demeanor fool you — he’s known as a rebellious thinker and has always followed his own path. In the 1960s Smolin dropped out of high school, played in a rock band called Ideoplastos, and published an underground newspaper. Wanting to build geodesic domes like R. Buckminster Fuller, Smolin taught himself advanced mathematics — the same kind of math, it turned out, that you need to play with Einstein’s equations of general relativity. The moment he realized this was the moment he became a physicist. He studied at Harvard University and took a position at the Institute for Advanced Study in Princeton, New Jersey, eventually becoming a founding faculty member at the Perimeter Institute.

Continue reading “We’re Stuck Inside the Universe. Lee Smolin Has an Idea for How to Study It Anyway” »

A new paper from researchers at the University of Chicago introduces a technique for compiling highly optimized quantum instructions that can be executed on near-term hardware. This technique is particularly well suited to a new class of variational quantum algorithms, which are promising candidates for demonstrating useful quantum speedups. The new work was enabled by uniting ideas across the stack, spanning quantum algorithms, machine learning, compilers, and device physics. The interdisciplinary research was carried out by members of the EPiQC (Enabling Practical-scale Quantum Computation) collaboration, an NSF Expedition in Computing.

Adapting to a New Paradigm for Quantum Algorithms

The original vision for quantum algorithms dates to the early 1980s, when physicist Richard Feynman proposed performing molecular simulations using just thousands of noise-less qubits (quantum bits), a practically impossible task for traditional computers. Other algorithms developed in the 1990s and 2000s demonstrated that thousands of noise-less qubits would also offer dramatic speedups for problems such as database search, integer factoring, and matrix algebra. However, despite recent advances in quantum hardware, these algorithms are still decades away from scalable realizations, because current hardware features noisy qubits.

Something called the fast Fourier transform is running on your cell phone right now. The FFT, as it is known, is a signal-processing algorithm that you use more than you realize. It is, according to the title of one research paper, “an algorithm the whole family can use.”

Alexander Stoytchev – an associate professor of electrical and computer engineering at Iowa State University who’s also affiliated with the university’s Virtual Reality Applications Center, its Human Computer Interaction graduate program and the department of computer science – says the FFT algorithm and its inverse (known as the IFFT) are at the heart of signal processing.

And, as such, “These are algorithms that made the digital revolution possible,” he said.

Something called the fast Fourier transform is running on your cell phone right now. The FFT, as it is known, is a signal-processing algorithm that you use more than you realize. It is, according to the title of one research paper, “an algorithm the whole family can use.”

Alexander Stoytchev—an associate professor of electrical and computer engineering at Iowa State University who’s also affiliated with the university’s Virtual Reality Applications Center, its Human Computer Interaction graduate program and the department of computer science—says the FFT algorithm and its inverse (known as the IFFT) are at the heart of signal processing.

And, as such, “These are algorithms that made the digital revolution possible,” he said.