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Category: computing – Page 523

Researchers have created a scalable quantum computing platform that has been shrunk down to the size of a penny, which would serve as the basis for a quantum computer that can achieve quantum speeds while using far fewer devices than current designs.

The team hopes their research, published in Nature Communications, will help push quantum computing forward in the constant pursuit of use in real-world applications.

Over the past few years, quantum computing has gone from science fiction to a realistic technology that may see use in the next few decades. While quantum teleportation and even quantum computer chips have been demonstrated previously, the technology is still a long way off seeing real-world use.

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Analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches.

In a new study published on August 26 2021, in the journal Nature Communications Physics, researchers led by Volker Sorger, an associate professor of electrical and computer engineering at the George Washington University, reveal a new nanophotonic analog processor capable of solving partial differential equations. This nanophotonic processor can be integrated at chip-scale, processing arbitrary inputs at the speed of light.

The research team also included researchers at the University of California, Los Angeles, and City College of New York.

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Circa 2019 o.o!

Groq, a rapidly growing startup that previously hired ten executives from Google for developing chipset architecture, has announced its new architecture named Tensor Streaming Processor that can perform 1 Peta operations per second on a single chip.

Tensor Streaming Processor (TSP) is the world’s first architecture to achieve this feat of performing 1 Peta or 1 quadrillion operations or 1e15 ops/s. Groq’s new architecture can also perform up to 250 trillion floating-point operations per second (FLOPS).

TSP architecture is inspired by a software-first mindset and offers an innovative approach towards accelerated computation.

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Maryland-based IonQ has unveiled a new kind of chip in its quest to scale up its type of quantum computer technology. Its computers calculate using the quantum states of ions electromagnetically trapped in the space near a chip. Previous traps were made using silicon chipmaking processes, but the company has now switched to an evaporated glass trap technology—a way of constructing micrometer-scale features in fused silica glass often used to make microfluidic chips. Its previous trap technology, the company says, could not have supported IonQ’s new quantum architecture, which is based on multiple chains of ion-based qubits. Ultimately, IonQ executives say, the glass chip’s reconfigurable chains of ions will allow for computers with qubits that number in the triple digits.

“The purpose of an ion trap is to move ions around with precision, hold them in the environment, and get out of the way of the quantum operation,” explains Jason Amini, who led the evaporated glass trap team at IonQ. The 3D glass and metal structure Amini’s team constructed does all three better than its previous chips could, Amini says. Stray electric fields from charge on the silicon-based chip could destabilize the ions’ delicate quantum states, reducing the fidelity of quantum computation. But the evaporated glass design “hides any material that could hold charge,” he says. The effect is a more stable trap that computes better.

Another advantage, Amini says, is that the trap could be shaped to “get out of the way” of quantum operations. In an ion trap computer the ions’ quantum states are manipulated by zapping them with lasers. “We have to bring a lot of laser beams over the surface,” says Amini. The glass chip is “shaped to allow lasers to come through and address the device.”

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It’s been a truth for a long time: if you want to study the movement and behavior of single atoms, electron microscopy can give you what X-rays can’t. X-rays are good at penetrating into samples—they allow you to see what happens inside batteries as they charge and discharge, for example—but historically they have not been able to spatially image with the same precision electrons can.

But scientists are working to improve the image resolution of X-ray techniques. One such method is X-ray tomography, which enables non-invasive imaging of the inside of materials. If you want to map the intricacies of a microcircuit, for example, or trace the neurons in a brain without destroying the material you are looking at, you need X-ray tomography, and the better the resolution, the smaller the phenomena you can trace with the X-ray beam.

To that end, a group of scientists led by the U.S. Department of Energy’s (DOE) Argonne National Laboratory has created a new method for improving the resolution of hard X-ray nanotomography. (Nanotomography is X-ray imaging on the scale of nanometers. For comparison, an average human hair is 100,000 nanometers wide.) The team constructed a high-resolution X-ray microscope using the powerful X-ray beams of the Advanced Photon Source (APS) and created new computer algorithms to compensate for issues encountered at tiny scales. Using this method, the team achieved a resolution below 10 nanometers.

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Since the early days of the COVID pandemic, scientists have aggressively pursued the secrets of the mechanisms that allow severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to enter and infect healthy human cells.

Early in the pandemic, University of California San Diego’s Rommie Amaro, a computational biophysical chemist, helped develop a detailed visualization of the SARS-CoV-2 spike protein that efficiently latches onto our cell receptors.

Now, Amaro and her research colleagues from UC San Diego, University of Pittsburgh, University of Texas at Austin, Columbia University and University of Wisconsin-Milwaukee have discovered how glycans–molecules that make up a sugary residue around the edges of the spike protein–act as infection gateways.

Published August 19 in the journal Nature Chemistry, a research study led by Amaro, co-senior author Lillian Chong at the University of Pittsburgh, first author and UC San Diego graduate student Terra Sztain and co-first author and UC San Diego postdoctoral scholar Surl-Hee Ahn, describes the discovery of glycan “gates” that open to allow SARS-CoV-2 entry.

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Researchers from Skoltech, KTH Royal Institute of Technology, and Uppsala University have predicted the existence of antichiral ferromagnetism, a nontrivial property of some magnetic crystals that opens the door to a variety of new magnetic phenomena. The paper was published in the journal Physical Review B.

Chirality, or handedness, is an extremely important fundamental property of objects in many fields of physics, mathematics, chemistry and biology; a chiral object cannot be superimposed on its in any way. The simplest chiral objects are human hands, hence the term itself. The opposite of chiral is achiral: a circle or a square are simple achiral objects.

Chirality can be applied to much more complex entities; for instance, competing internal interactions in a can lead to the appearance of periodic magnetic textures in the structure that differ from their mirror images—this is called chiral ferromagnetic ordering. Chiral crystals are widely considered promising candidates for and processing device realization as information can be encoded via their nontrivial magnetic textures.

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Partition functions are ubiquitous in physics: They are important in determining the thermodynamic properties of many-body systems and in understanding their phase transitions. As shown by Lee and Yang, analytically continuing the partition function to the complex plane allows us to obtain its zeros and thus the entire function. Moreover, the scaling and nature of these zeros can elucidate phase transitions. Here, we show how to find partition function zeros on noisy intermediate-scale trapped-ion quantum computers in a scalable manner, using the XXZ spin chain model as a prototype, and observe their transition from XY-like behavior to Ising-like behavior as a function of the anisotropy. While quantum computers cannot yet scale to the thermodynamic limit, our work provides a pathway to do so as hardware improves, allowing the future calculation of critical phenomena for systems beyond classical computing limits.

Interacting quantum systems exhibit complex phenomena including phase transitions to various ordered phases. The universal nature of critical phenomena reduces their description to determining only the transition temperature and the critical exponents. However, numerically calculating these quantities for systems in new universality classes is complicated because of critical slowing down, requiring increasing resources near the critical point. An alternative approach is to analytically continue the calculation of the partition function to the complex plane and determine its zeros.

The partition function is a positive function of multiple real parameters representing physical quantities such as temperature and applied fields. When the partition function is analytically continued in one of the respective parameters, its zeros show notable structure for a variety of models of interest. Lee and Yang (1, 2) studied the partition function zeros of Ising-like systems in the complex plane of the magnetic field h and found that, at the critical temperature (and in the thermodynamic limit), the loci of zeros pinch to the real axis. Alternatively, Fisher (3) studied the partition function zeros by making the inverse temperature β complex.

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