Erenay Karacan • Published: 2025-11-26
Many optimally scaling quantum simulation algorithms employ controlled time evolution of the Hamiltonian, which is typically the major bottleneck for their efficient implementation. This work establishes a compression protocol for encoding the controlled time evolution operator of translationally invariant, local Hamiltonians into a quantum circuit. It achieves a near-optimal in time $t$ scaling f...
I. Samaras, K. Barr, C. Schneider, S. Höfling, K. G. Lagoudakis • Published: 2026-04-08
We demonstrate complete coherent control of a single spin qubit confined in a self-assembled InAs negatively charged quantum dot subjected to an Oblique magnetic field, and directly compare this regime with the conventional Voigt geometry. In the Oblique-field configuration, the groundstate spin eigenstates are found to be unequal superpositions of the bare electron spin, with their composition tu...
Mathias N. Lystbæk, Haley Adams, Ranjith Kagathi Ananda, Eric J Gonzalez, Luca Ballan, Qiuxuan Wu, Andrea Colaço, Peter Tan, Mar Gonzalez-Franco • Published: 2026-03-13
Audio-only walking navigation can leave users disoriented, relying on vague cardinal directions and lacking real-time environmental context, leading to frequent errors. To address this, we present a novel system that integrates a Vision Language Model (VLM) with a spatial audio cue. Our system extracts environmental landmarks to anchor navigation instructions and, crucially, provides a directional...
Giorgio Fabris, Paul Falthansl-Scheinecker, Devashish Shah, Daniel Michel Pino, Maksim Borovkov, Anton Bubis, Kevin Roux, Dina Sokolova, Alejandro Andres Juanes, Tommaso Costanzo, Inas Taha, Aziz Genç, Jordi Arbiol, Stefano Calcaterra, Afonso De Cerdeira Oliveira, Daniel Chrastina, Giovanni Isella, Ruben Seoane Souto, Maria Jose Calderon, Ramon Aguado, Jose Carlos Abadillo-Uriel, Georgios Katsaros • Published: 2026-02-24
In superconductor-semiconductor hybrid structures, superconductivity and spin polarization are competing effects because magnetic fields break Cooper pairs. They can be combined using thin films and in-plane magnetic fields, an approach that enabled the pursuit of Majorana zero modes, Kitaev chains, and Andreev spin qubits (ASQs), but remains challenging for materials with small in-plane g-factors...
Xiangyi Li, Kyoung Whan Choe, Yimin Liu, Xiaokun Chen, Chujun Tao, Bingran You, Wenbo Chen, Zonglin Di, Jiankai Sun, Shenghan Zheng, Jiajun Bao, Yuanli Wang, Weixiang Yan, Yiyuan Li, Han-chung Lee • Published: 2026-04-06
Large language model (LLM) agents are increasingly deployed to automate productivity tasks (e.g., email, scheduling, document management), but evaluating them on live services is risky due to potentially irreversible changes. Existing benchmarks rely on simplified environments and fail to capture realistic, stateful, multi-service workflows. We introduce ClawsBench, a benchmark for evaluating and ...
Mark Potts, Shu Zhang • Published: 2025-09-12
We propose using spin-qubit noise magnetometry to probe dynamical signatures of magnetic Berezinskii-Kosterlitz-Thouless (BKT) physics. For a nitrogen-vacancy (NV) center coupled to two-dimensional XY magnets, we predict distinctive features in the magnetic noise spectral density in the sub-MHz to GHz frequency range. In the quasi-long-range ordered phase, the spectrum exhibits a temperature-depen...
Fabian Ihle, Moritz Flüchter, Michael Menth • Published: 2025-11-13
Time-sensitive networking (TSN) is a set of IEEE standards that extends Ethernet with real-time capabilities. Among its mechanisms, the time-aware shaper (TAS) periodically opens and closes egress queues to protect scheduled traffic from lower-priority flows, ensuring low latency and bounded delay. Deterministic networking (DetNet), standardized by the IETF, provides similar guarantees at Layer 3 ...
Aabid Karim, Abdul Karim, Bhoomika Lohana, Matt Keon, Jaswinder Singh, Abdul Sattar • Published: 2025-03-23
We demonstrate that large language models' (LLMs) mathematical reasoning is culturally sensitive: testing 14 models from Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and Microsoft across six culturally adapted variants of the GSM8K benchmark, we find accuracy drops ranging from 0.3% (Claude 3.5 Sonnet) to 5.9% (LLaMA 3.1-8B) when math problems are embedded in unfamiliar cultural contexts--e...