![The computational scaling of the WISE algorithm reveals a performance trade-off: while total solution time and the identification of divergent Weinberg eigenvalues scale quadratically with the number of scattering channels [latex] \propto N^{2} [/latex], convergence of the Born series for both the regularized source vector and eigenvectors achieves linear scaling [latex] \propto N [/latex], suggesting a fundamental limit to efficiency as system complexity increases.](https://arxiv.org/html/2601.01159v1/x4.png)
Researchers have developed a new algorithm that dramatically improves the efficiency of quantum scattering calculations, potentially unlocking simulations of more complex molecular collisions.
Researchers are exploring a surprising new application for SHA-256 ASICs – harnessing their inherent timing variations to build energy-efficient physical reservoir computing systems.
Researchers have developed a new QEMU plugin, NQC2, that significantly accelerates code coverage analysis for embedded systems without compromising performance.
A new wave of research explores how quantum machine learning can bolster the security of distributed energy systems against increasingly sophisticated coordinated stealth attacks.
![Classical syndrome codes-specifically [latex]CSXC_{\mathrm{S}}^{X}[/latex]-demonstrate performance characteristics that implicitly forecast eventual failure modes within the system they attempt to stabilize.](https://arxiv.org/html/2601.01137v1/x1.png)
Researchers have uncovered a fundamental link between the mathematical structure of Bivariate Bicycle codes and their ability to correct errors in quantum data, even with limited measurements.
A new system architecture tackles the challenges of running multiple quantum programs concurrently, paving the way for more efficient use of scarce quantum resources.
A new framework combines federated learning with advanced cryptography to safeguard critical infrastructure from evolving cyber threats.
As the threat of quantum computing looms, this review explores the vital steps needed to safeguard Intelligent Transportation Systems from emerging cryptographic vulnerabilities.
A new framework, Rogue Variable Theory, explores how quantum-inspired mathematics can represent ambiguous pre-event cognitive states, offering a path towards more interpretable and aligned artificial intelligence.
![The system optimizes semantic communication by assigning repetition counts to quantized embeddings-guided by a policy [latex]\pi_{\theta}[/latex]-to minimize semantic distortion [latex]D_{S}[/latex] while adhering to a predetermined channel-use budget [latex]B[/latex].](https://arxiv.org/html/2601.00186v1/x1.png)
A new reinforcement learning approach intelligently prioritizes the most vital parts of data embeddings, ensuring reliable communication even with limited bandwidth.