Quantum machines, new materials, and the universe at every scale.
arXiv
Verdict: NotableSupervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics.
The distance of a quantum code is a fundamental metric that dictates its error-correction capability.
Altermagnetism is a rapidly emerging paradigm in condensed matter magnetism, yet the number of experimentally confirmed altermagnetic candidates remains remarkably limited.
Knowledge bases are essential to agents for high energy physics (HEP), supplying few-shot evidence for previously unseen tasks.
We investigate quantum criticality in the transverse-field Ising model on finite Cayley trees and demonstrate that their extensive boundary fundamentally changes the nature of the quantum ....
We present cosmological constraints from 11 strongly lensed quasars with measured time delays (hereafter, the TDCOSMO-2026 sample).
Steering candidate generation toward user-defined objectives is central to inverse materials design, yet remains difficult when objectives are black-box, changing, or non-differentiable, ....