Computers and Math from Science Daily Feed

Researchers have directly observed Floquet effects in graphene for the first time, settling a long-running scientific debate. Their ultrafast light-based technique demonstrates that graphene’s electronic properties can be tuned almost instantaneously. This paves the way for custom-engineered quantum materials and new approaches in electronics and sensing.

Princeton researchers found that the brain excels at learning because it reuses modular “cognitive blocks” across many tasks. Monkeys switching between visual categorization challenges revealed that the prefrontal cortex assembles these blocks like Legos to create new behaviors. This flexibility explains why humans learn quickly while AI models often forget old skills. The insights may help build better AI and new clinical treatments for impaired cognitive adaptability.

Researchers have discovered a way to store information using a rare class of materials called ferroaxials, which rely on swirling electric dipoles instead of magnetism or charge. These vortex-like states are naturally stable and resistant to outside interference, but until now were almost impossible to control. By using circularly polarized terahertz light, scientists were able to flip these tiny rotational patterns on command, opening the door to a new form of robust, ultrafast, and long-lasting data storage.

New research shows that light’s magnetic field is far more influential than scientists once believed. The team found that this magnetic component significantly affects how light rotates as it passes through certain materials. Their work challenges a 180-year-old understanding of the Faraday Effect and opens pathways to new optical and magnetic technologies.

Researchers created scalable quantum circuits capable of simulating fundamental nuclear physics on more than 100 qubits. These circuits efficiently prepare complex initial states that classical computers cannot handle. The achievement demonstrates a new path toward simulating particle collisions and extreme forms of matter. It may ultimately illuminate long-standing cosmic mysteries.

Researchers have found a way to make “dark excitons”—normally invisible quantum states of light—shine dramatically brighter by trapping them inside a tiny gold-nanotube optical cavity. This breakthrough boosts their emission 300,000-fold and allows scientists to switch and tune them with unprecedented precision. The work unlocks new possibilities for ultrafast photonics, on-chip quantum communication, and exploring previously unreachable quantum states in 2D materials.

A Princeton team built a new tantalum-silicon qubit that survives for over a millisecond, far surpassing today’s best devices. The design tackles surface defects and substrate losses that have limited transmon qubits for years. Easy to integrate into existing quantum chips, the approach could make processors like Google’s vastly more powerful.

Electrons can freeze into strange geometric crystals and then melt back into liquid-like motion under the right quantum conditions. Researchers identified how to tune these transitions and even discovered a bizarre “pinball” state where some electrons stay locked in place while others dart around freely. Their simulations help explain how these phases form and how they might be harnessed for advanced quantum technologies.

Researchers combined deep learning with high-resolution physics to create the first Milky Way model that tracks over 100 billion stars individually. Their AI learned how gas behaves after supernovae, removing one of the biggest computational bottlenecks in galactic modeling. The result is a simulation hundreds of times faster than current methods.

Chimps may revise their beliefs in surprisingly human-like ways. Experiments showed they switched choices when presented with stronger clues, demonstrating flexible reasoning. Computational modeling confirmed these decisions weren’t just instinct. The findings could influence how we think about learning in both children and AI.

Aalto University researchers have developed a method to execute AI tensor operations using just one pass of light. By encoding data directly into light waves, they enable calculations to occur naturally and simultaneously. The approach works passively, without electronics, and could soon be integrated into photonic chips. If adopted, it promises dramatically faster and more energy-efficient AI systems.