AI and politics 😳 Artificial though it may be, the concept of “intelligence” doesn’t seem to jibe with a computer-generated image of uniformed cats toting assault rifles.
Yet that visual slur, which supports a debunked story about immigrants in Ohio eating pets, has become a signature image from…
UMD experts explain the emotional pulls and cognitive pitfalls—and how to avoid them.
In a world powered by artificial intelligence applications, data is king, but it’s also the crown’s biggest burden.
As described in the article, quantum memory stores data in ways that classical memory systems cannot match. In quantum systems, information is stored in quantum states, using the principles of superposition and entanglement to represent data more efficiently. This ability allows quantum systems to process and store vastly more information, potentially impacting data-heavy industries like AI.
In a 2021 study from the California Institute of Technology, researchers showed that quantum memory could dramatically reduce the number of steps needed to model complex systems. Their method proved that quantum algorithms using memory could require exponentially fewer steps, cutting down on both time and energy. However, this early work required vast amounts of quantum memory—an obstacle that could have limited its practical application.
Now, two independent teams have derived additional insights, demonstrating how these exponential advantages can be achieved with much less quantum memory. Sitan Chen from Harvard University, along with his team, found that just two quantum copies of a system were enough to provide the same computational efficiency previously thought to require many more.
The challenge of observing and controlling complex phases of matter has long intrigued researchers, particularly when it comes to non-equilibrium systems like time crystals.
Researchers successfully observed topological time-crystalline order using superconducting qubits on a programmable quantum processor.
Have you ever wanted to travel through time to see what your future self might be like?
The user engages with the tool in two ways: through introspection, when they consider their life and goals as they construct their future selves, and retrospection, when they contemplate whether the simulation reflects who they see themselves becoming, says Yin.
“You can imagine Future You as a story search space. You have a chance to hear how some of your experiences, which may still be emotionally charged for you now, could be metabolized over the course of time,” she says.
To help people visualize their future selves, the system generates an age-progressed photo of the user. The chatbot is also designed to provide vivid answers using phrases like “when I was your age,” so the simulation feels more like an actual future version of the individual.
Kidneys are injured more often than any of the organs along the urinary tract from external trauma.
Tech group signs deal with Kairos Power to build up to 7 small facilities to meet its energy needs.
Laboratory testing has revealed that some negatively-doped, ‘n-type’ tunnel oxide passivated contact (TOPCon) and heterojunction (HJT) solar modules are susceptible to ultraviolet (UV) light-related damage and degradation. That could mean trouble down the line, if modules in the field begin to show UV-related performance loss. Manufacturers are implementing solutions at cell and module level.
Predicting the behavior of many interacting quantum particles is a complex task, but it’s essential for unlocking the potential of quantum computing in real-world applications. A team of researchers, led by EPFL, has developed a new method to compare quantum algorithms and identify the most challenging quantum problems to solve.
Quantum systems, from subatomic particles to complex molecules, hold the key to understanding the workings of the universe. However, modeling these systems quickly becomes overwhelming due to their immense complexity. It’s like trying to predict the behavior of a massive crowd where everyone constantly influences everyone else. When you replace the crowd with quantum particles, you encounter what’s known as the “quantum many-body problem.”
Quantum many-body problems involve predicting the behavior of numerous interacting quantum particles. Solving these problems could lead to major breakthroughs in fields like chemistry and materials science, and even accelerate the development of technologies like quantum computers.
Researchers have achieved a breakthrough in observing intrinsic magnetic structures in kagome lattices, which may significantly influence future quantum computing and superconductivity applications.
A research team led by Prof. Qingyou Lu from the Hefei Institutes of Physical Science at the Chinese Academy of Sciences, in collaboration with Prof. Yimin Xiong from Anhui University, has achieved a groundbreaking discovery. Using advanced techniques such as magnetic force microscopy (MFM), electron paramagnetic resonance spectroscopy, and micromagnetic simulations, they have made the first-ever observation of intrinsic magnetic structures within a kagome lattice.
These findings, published recently in Advanced Science, shed new light on the behavior of materials, which is largely determined by the interaction between their internal electrons and lattice structure. Kagome lattices, known for their unique properties like Dirac points and flat bands, display extraordinary phenomena such as topological magnetism and unconventional superconductivity. These lattices are of great interest because of their potential to unlock new insights into high-temperature superconductivity and quantum computing. Despite this, the intrinsic spin patterns that define these materials have remained elusive—until now.
