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“In quantum many-body theory, we are often faced with the situation that we can perform calculations using a simple approximate interaction, but realistic high-fidelity interactions cause severe computational problems,” says Dean Lee, Professor of Physics from the Facility for Rare Istope Beams and Department of Physics and Astronomy (FRIB) at Michigan State University and head of the Department of Theoretical Nuclear Sciences.

Practical Applications and Future Prospects

Wavefunction matching solves this problem by removing the short-distance part of the high-fidelity interaction and replacing it with the short-distance part of an easily calculable interaction. This transformation is done in a way that preserves all the important properties of the original realistic interaction. Since the new wavefunctions are similar to those of the easily computable interaction, the researchers can now perform calculations with the easily computable interaction and apply a standard procedure for handling small corrections – called perturbation theory.

Scientists have, for the first time ever, made light appear to move simultaneously forward and backward in time.

According to a LiveScience report, the new approach, developed by a global team of scientists, may contribute to the development of novel quantum computing methods and advance our understanding of quantum gravity.

Which factors determine how quickly a battery can be charged? This and other questions are studied by researchers of Karlsruhe Institute of Technology (KIT) with the help of computer-based simulations.

While silicon has been the go-to material for sensor applications, could polymer be used as a suitable substitute since silicon has always lacked flexibility to be used in specific applications? This is what a recent grant from the National Science Foundation hopes to address, as Dr. Elsa Reichmanis of Lehigh University was recently awarded $550,000 to investigate how polymers could potentially be used as semiconductors for sensor applications, including Internet of Things, healthcare, and environmental applications.

Illustration of an organic electrochemical transistor that could be developed as a result of this research. (Credit: Illustration by by Ella Marushchenko; Courtesy of Reichmanis Research Group)

“We’ll be creating the polymers that could be the building blocks of future sensors,” said Dr. Reichmanis, who is an Anderson Chair in Chemical Engineering in the Department of Chemical and Biomolecular Engineering at Lehigh University. “The systems we’re looking at have the ability to interact with ions and transport ionic charges, and in the right environment, conduct electronic charges.”

In a study published in Nature Materials, scientists from the University of California, Irvine describe a new method to make very thin crystals of the element bismuth—a process that may aid the manufacturing of cheap flexible electronics an everyday reality.

The FDA has allowed billionaire Elon Musk’s Neuralink to implant its brain chip in a second person after it proposed a fix for a problem that occurred in its first patient. Correspondent Brooke Shafer joins \.

Achieving the promised advantages of quantum computing relies on translating quantum operations into physical realizations. Fürrutter and colleagues use diffusion models to create quantum circuits that are based on user specifications and tailored to experimental constraints.

“Neuralink to implant 2nd human with brain chip as 85% of threads retract. Neuralink’s first patient, 29-year-old Noland Arbaugh, opened up about the roller-coaster experience. ” I was on such a high and then to be brought down that low. It was very, very hard,” Arbaugh said. ” I cried.” What a disaster!

Algorithm tweaks made up for the loss, and Neuralink thinks it has fix for next patient.

We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm³ volume with heavy metals, embedded it in resin, cut more than 5,000 slices at ∼30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online. Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each other’s initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ∼20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.

The authors have declared no competing interest.