cyberintel.kalymoon.com · 48827 articles · updated every 4 hours · grows forever
arXiv:2603.26102v1 Announce Type: new Abstract: We consider a non-contextual inequality in the sequential measurement scenario and derive the optimal quantum violation of it without assuming the dimen…
arXiv:2603.26075v1 Announce Type: new Abstract: An elementary prediction of the quantization of the gravitational field is that the Newtonian interaction can entangle pairs of massive objects. Convers…
arXiv:2603.26063v1 Announce Type: new Abstract: Quantum error mitigation is essential for extracting trustworthy results from noisy intermediate-scale quantum (NISQ) processors. Yet, current approache…
arXiv:2603.26039v1 Announce Type: new Abstract: Grover's algorithm is a fundamental quantum algorithm that achieves a quadratic speedup for unstructured search problems of size $N$. Recent studies hav…
arXiv:2603.25952v1 Announce Type: new Abstract: This work proposes a scalable framework for topological quantum computing using Matryoshka-type Sine-Cosine chains. These chains support high-dimensiona…
arXiv:2603.25920v1 Announce Type: new Abstract: Quantum networks will rely on entanglement distribution to enable multi-user applications such as distributed quantum computing and cryptography. While …
arXiv:2603.25876v1 Announce Type: new Abstract: We propose two-gate extensions of the sequential single-qubit optimizers, Free Axis Selection (Fraxis) and Free Quaternion Selection (FQS), termed Two-G…
arXiv:2603.25831v1 Announce Type: new Abstract: With recent breakthroughs in the construction of good qLDPC codes and nearly good qLTCs, the study of (co)homological invariants of quantum code complex…
arXiv:2603.25789v1 Announce Type: new Abstract: We study bipartite entanglement statistics in one-dimensional anyon chains, whose Hilbert spaces are constrained by fusion rules of unitary pre-modular …
arXiv:2603.25774v1 Announce Type: new Abstract: We present Catalytic Quantum Error Correction (CQEC), a quantum state recovery protocol based on the arbitrary amplification of coherence in catalytic c…
arXiv:2603.25757v1 Announce Type: new Abstract: We quantify decoder dependence in surface-code threshold studies under two matched regimes: Pauli noise and native GKP-style Gaussian displacement digit…
arXiv:2603.25863v1 Announce Type: cross Abstract: This paper proposes a method for dynamic hand gesture recognition based on the composition of two models: the MediaPipe Hand Landmarker, responsible f…
arXiv:2603.25862v1 Announce Type: cross Abstract: Virtually every sector of society is experiencing a dramatic growth in the volume of unstructured textual data that is generated and published, from n…
arXiv:2603.25841v1 Announce Type: cross Abstract: Current multimodal large language models (MLLMs) cannot effectively utilize eye-gaze information for video understanding, even when gaze cues are supp…
arXiv:2603.25839v1 Announce Type: cross Abstract: Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new ligh…
arXiv:2603.25823v1 Announce Type: cross Abstract: Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex…
arXiv:2603.25821v1 Announce Type: cross Abstract: We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physician-patient …
arXiv:2603.25813v1 Announce Type: cross Abstract: We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, training, and serving of domain-expert langu…
arXiv:2603.25796v1 Announce Type: cross Abstract: We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representa…
arXiv:2603.25779v1 Announce Type: cross Abstract: Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make its modeling a challe…
arXiv:2603.25777v1 Announce Type: cross Abstract: There is great potential for the application of AI tools in fusion research, and substantial worldwide benefit if fusion power is realised. However, u…
arXiv:2603.25771v1 Announce Type: cross Abstract: Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-t…
arXiv:2603.25770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently emerged as capable coding assistants that operate over large codebases through either agentic exploration o…
arXiv:2603.25769v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but existing methods remain static and stru…