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In groundbreaking new research, an international team of researchers led by the University of Minnesota Twin Cities has developed a unique process for producing a quantum state that is part light and part matter.

The discovery provides fundamental new insights for more efficiently developing the next generation of quantum-based optical and electronic devices. The research could also have an impact on increasing efficiency of nanoscale chemical reactions.

The research is published in Nature Photonics.

In what could be one of the significant developments in the field of quantum computing, Chinese researchers suggest having achieved quantum supremacy with the capability of performing calculations 100 trillion times faster than the world’s most advanced supercomputer. Researchers from the University of Science and Technology of China, Hefei, believe that when put into practical use, it can carry calculations in minutes which would have otherwise taken two billion years to perform. The fastest supercomputers, before this, claimed to have achieved computational efficiency easing up to 10,000 years of calculations.

Jiuzhang, as the supercomputer is called, has outperformed Google’s supercomputer, which the company had claimed last year to have achieved quantum computing supremacy. The supercomputer by Google named Sycamore is a 54-qubit processor, consisting of high-fidelity quantum logic gates that could perform the target computation in 200 seconds.

Continue reading “Quantum Computing Marks New Breakthrough, Is 100 Trillion Times More Efficient” »

Columbia team discovers 6-nanometer-long single-molecule circuit with enormous on/off ratio due to quantum interference; finding could enable faster, smaller, and more energy-efficient devices.

Researchers, led by Columbia Engineering Professor Latha Venkataraman, report today that they have discovered a new chemical design principle for exploiting destructive quantum interference. They used their approach to create a six-nanometer single-molecule switch where the on-state current is more than 10,000 times greater than the off-state current–the largest change in current achieved for a single-molecule circuit to date.

This new switch relies on a type of quantum interference that has not, up to now, been explored. The researchers used long molecules with a special central unit to enhance destructive quantum interference between different electronic energy levels. They demonstrated that their approach can be used to produce very stable and reproducible single-molecule switches at room temperature that can carry currents exceeding 0.1 microamps in the on-state. The length of the switch is similar to the size of the smallest computer chips currently on the market and its properties approach those of commercial switches. The study is published today in Nature Nanotechnology.

Photons explore quantum maze faster than possible for any classical computer.

The result highlights a fundamental tension: Either the rules of quantum mechanics don’t always apply, or at least one basic assumption about reality must be wrong.

Scientists discovered a strategy for layering dissimilar crystals with atomic precision to control the size of resulting magnetic quasi-particles called skyrmions. This approach could advance high-density data storage and quantum magnets for quantum information science.

In typical ferromagnets, magnetic spins align up or down. Yet in skyrmions, they twist and swirl, forming unique shapes like petite porcupines or tiny tornadoes.

The tiny intertwined magnetic structures could innovate high-density data storage, for which size does matter and must be small. The Oak Ridge National Laboratory-led project produced skyrmions as small as 10 nanometers – 10,000 times thinner than a human hair.

Quantum tools in python.

Quantum Toolbox in Python. QuTiP has 12 repositories available. Follow their code on GitHub.

Understanding how matter interacts with light—its optical properties—is critical in a myriad of energy and biomedical technologies, such as targeted drug delivery, quantum dots, fuel combustion, and cracking of biomass. But calculating these properties is computationally intensive, and the inverse problem—designing a structure with desired optical properties—is even harder.

Now Berkeley Lab scientists have developed a machine learning model that can be used for both problems—calculating optical properties of a known structure and, inversely, designing a structure with desired optical properties. Their study was published in Cell Reports Physical Science.

“Our model performs bi-directionally with high accuracy and its interpretation qualitatively recovers physics of how metal and dielectric materials interact with light,” said corresponding author Sean Lubner.

Google claimed quantum supremacy in October 2019 — but using a strikingly different system.