Lifeboat Foundation

Recently, researchers have been incorporating graphene-based materials into superconducting quantum computing devices, which promise faster, more efficient computing, among other perks. Until now, however, there’s been no recorded coherence for these advanced qubits, so there’s no knowing if they’re feasible for practical quantum computing.

In a paper published today in Nature Nanotechnology, the researchers demonstrate, for the first time, a coherent qubit made from graphene and exotic materials. These materials enable the qubit to change states through voltage, much like transistors in today’s traditional computer chips — and unlike most other types of superconducting qubits. Moreover, the researchers put a number to that coherence, clocking it at 55 nanoseconds, before the qubit returns to its ground state.

The work combined expertise from co-authors William D. Oliver, a physics professor of the practice and Lincoln Laboratory Fellow whose work focuses on quantum computing systems, and Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT who researches innovations in graphene.

Meet the 10-foot-tall (3 meters) Mars Ascent Vehicle.

The 10-foot-tall (3 meters) Mars Ascent Vehicle will blast rock, sediment and atmospheric samples off Mars in the early 2030s, in the first-ever rocket launch from the surface of another planet.

System uses tiny magnetic beads to rapidly measure the position of muscles and relay that information to a bionic prosthesis.

Large language models are widely adopted in a range of natural language tasks, such as question-answering, common sense reasoning, and summarization. These models, however, have had difficulty with tasks requiring quantitative reasoning, such as resolving issues in mathematics, physics, and engineering.

Researchers find quantitative reasoning an intriguing application for language models as they put language models to the test in various ways. The ability to accurately parse a query with normal language and mathematical notation, remember pertinent formulas and constants and produce step-by-step answers requiring numerical computations and symbolic manipulation are necessary for solving mathematical and scientific problems. Therefore, scientists have believed that machine learning models will require significant improvements in model architecture and training methods to solve such reasoning problems.

A new Google research introduces Minerva, a language model that uses sequential reasoning to answer mathematical and scientific problems. Minerva resolves such problems by providing solutions incorporating numerical computations and symbolic manipulation.

The Space Force has assumed command of a new unit that will be focused on keeping an eye out for foreign threats in space, but it comes as Congress is warning the small service branch that it has to prepare to slow its growth.

Delta 18 and the brand-new National Space Intelligence Center were officially commissioned late last month at Wright-Patterson Air Force Base in Dayton, Ohio. It will be staffed by nearly 350 civilian and military personnel.

Delta 18’s mission is to “deliver critical intelligence on threat systems, foreign intentions, and activities in the space domain to support national leaders, allies, partners and joint war fighters,” according to a press release.

North Korean-backed hackers are targeting hospitals and healthcare organizations in the U.S. with ransomware, a trio of government agencies that includes the Federal Bureau of Investigation warn in a cybersecurity alert.

Modern technology allows us to measure magnetic fields created by electric signals in our brains. Now, a breakthrough makes detection 10 times better.

After multiple interview rounds, hackers posing as recruiters sent an Axie Infinity engineer an offer letter containing spyware, The Block reported.

While you read this sentence, the neurons in your brain are communicating with one another by firing off quick electrical signals. They communicate with one another via synapses, which are tiny, specialized junctions.

There are many various kinds of synapses that develop between neurons, including “excitatory” and “inhibitory,” and scientists are still unsure of the specific methods by which these structures are formed. A biochemistry team has provided significant insight into this topic by demonstrating that the types of chemicals produced from synapses ultimately determine which types of synapses occur between neurons.