Lifeboat Foundation

A brain-computer interface from the startup Inbrain could be used to treat Parkinson’s disease.

Here, the authors demonstrate a combined atom array-nanophotonic chip platform for quantum networking and distributed quantum computing, enabled by a high-fidelity background-free imaging technique, a semi-open photonic chip geometry, and free-space coupling to the nanophotonic cavities.

Quantum information systems offer faster, more powerful computing methods than standard computers to help solve many of the world’s toughest problems. Yet fulfilling this ultimate promise will require bigger and more interconnected quantum computers than scientists have yet built. Scaling quantum systems up to larger sizes, and connecting multiple systems, has proved challenging.

Though not a new technology, new advancements have progressed this field significantly.

An ordered pattern of atomic spins with possible uses in computing can become more ordered if shaken at the right frequency.

New research has established a reversible framework for quantum entanglement, aligning it with the principles of thermodynamics and paving the way for improved manipulation and understanding of quantum resources.

Bartosz Regula from the RIKEN Center for Quantum Computing and Ludovico Lami from the University of Amsterdam have demonstrated through probabilistic calculations the existence of an “entropy” rule for quantum entanglement. This discovery could enhance our understanding of quantum entanglement, a crucial resource underpinning the potential of future quantum computers. Although quantum entanglement has been a research focus in quantum information science for decades, optimal methods for its effective utilization remain largely unknown.

The second law of thermodynamics, which says that a system can never move to a state with lower “entropy”, or order, is one of the most fundamental laws of nature and lies at the very heart of physics. It is what creates the “arrow of time,” and tells us the remarkable fact that the dynamics of general physical systems, even extremely complex ones such as gases or black holes, are encapsulated by a single function, its “entropy.”

New research has revealed that the lag observed in organic electrochemical transistors (OECTs) when switched on is due to a two-step activation process, providing crucial insights for designing more effective and customizable OECTs for various technological and biological applications.

Researchers who want to bridge the divide between biology and technology spend a lot of time thinking about translating between the two different “languages” of those realms.

“Our digital technology operates through a series of electronic on-off switches that control the flow of current and voltage,” said Rajiv Giridharagopal, a research scientist at the University of Washington. “But our bodies operate on chemistry. In our brains, neurons propagate signals electrochemically, by moving ions — charged atoms or molecules — not electrons.”

Nanotransfection is very useful and could be used as a way to heal oneself on a smartphone in one touch with cell reprogramming and much more like gene transfer.

Tissue nanotransfection (TNT), a cutting-edge technique of in vivo gene therapy, has gained substantial attention in various applications ranging from in vivo tissue reprogramming in regenerative medicine, and wound healing to cancer treatment. This technique harnesses the advancements in the semiconductor processes, facilitating the integration of conventional transdermal gene delivery methods—nanoelectroporation and microneedle technologies. TNT silicon chips have demonstrated considerable promise in reprogramming fibroblast cells of skin in vivo into vascular or neural cells in preclinical studies to assist in the recovery of injured limbs and damaged brain tissue. More recently, the application of TNT chips has been extended to the area of exosomes, which are vital for intracellular communication to track their functionality during the wound healing process.

Trained on 1.5 million whole-slide images from 100,000 patients, a pathology foundation model is shown to improve performance of specialized models in detection of rare cancers.