Science

As autonomous AI systems become increasingly integrated into software supply chains, a new range of vulnerabilities emerges, demanding a proactive shift in security paradigms.

A new theoretical approach significantly enhances the accuracy of g-tensor calculations, crucial for understanding the magnetic behavior of molecules.

New research challenges the assumptions behind vector quantization, offering solutions to a common problem that hinders generative model performance.

Researchers have developed a new generative model that replaces discrete quantization with a continuous Principal Component Analysis layer, offering improved performance and interpretability.

A new approach to constructing quantum kernels using spectral phase encoding demonstrates increased resilience to noise, offering a pathway to more reliable quantum machine learning.

New research explores how quantum algorithms could pose a threat to the Learning Parity with Noise problem, a cornerstone of modern code-based cryptography.

As quantum computing looms, this review explores the practical challenges of integrating post-quantum cryptography into the low-power wireless networks that power our everyday devices.

A new study explores how readily available artificial intelligence tools can assist researchers in analyzing complex qualitative datasets.

A new framework leverages decentralized learning and privacy-enhancing technologies to build more resilient intrusion detection systems.

A new theoretical approach investigates the exotic states of matter predicted to exist at the cores of neutron stars and in other environments with extreme densities.