Decoding Quantum Learning: A New Metric for Smarter Algorithms

Researchers have developed a novel method to evaluate how effectively quantum algorithms learn, paving the way for more efficient reinforcement learning systems.

Researchers have developed a novel method to evaluate how effectively quantum algorithms learn, paving the way for more efficient reinforcement learning systems.

New research explores how the interplay between dark matter and dark energy influences the observed acceleration of the universe.

A new analysis reveals how quantum systems transition to predictable classical behavior as quantum effects fade.

Researchers have developed a computationally efficient method for modeling the complex interplay between light and matter, opening doors to understanding and designing novel quantum materials.

A new framework, Wasserstein Evolution, draws parallels between evolutionary algorithms and the physics of phase transitions to achieve more robust and diverse optimization.

A new framework details how to analyze continuous measurements on quantum systems that retain memory of their past interactions.

New research shows how strong interactions between light and matter can manipulate magnetic materials, potentially unlocking exotic quantum states of matter.

Researchers have developed a new framework to model the energy loss and hadronization of heavy quarks using near-term quantum devices.

A new framework clarifies how to systematically understand and connect quantum resources in bosonic systems, paving the way for more powerful quantum technologies.
New research demonstrates a real-time quantum control strategy enabling atomic magnetometers to achieve quantum-limited sensitivity and track weak, fluctuating fields with unprecedented precision.