Science

A new theoretical result shows that strategically ‘shaking’ a quantum probe enhances its ability to measure temperature, offering a path to more precise thermal sensing.

Researchers have combined the strengths of quantum computing and classical algorithms to tackle complex integer linear programs, potentially unlocking faster and more efficient solutions.

New research details the experimental realization of confined quantum walks on a chip, offering insights into how noise and spatial constraints affect quantum evolution.

A novel approach leveraging Entropic Dynamics reconstructs the foundations of Quantum Electrodynamics, offering a fresh perspective on its underlying principles.

New research establishes theoretical performance guarantees for quantum neural estimators, paving the way for more efficient quantum machine learning algorithms.

Researchers have developed a novel machine learning framework that leverages hypercausal reasoning and quantum-inspired principles to maintain performance in constantly evolving environments.

Researchers have successfully used programmable quantum annealers to model antiferromagnetic hysteresis, opening new avenues for exploring complex magnetic phenomena.

Researchers have shown how neural networks can predict complex quantum relationships using only readily accessible local measurements, sidestepping the need for complete state reconstruction.

Researchers are rigorously comparing the performance of leading numerical methods for simulating the complex behavior of quantum systems in two dimensions.

A new study reveals how leveraging strong particle-hole entanglement in collective spin qubit systems can push quantum sensing beyond conventional limits.