Research

Research, evidence gathering, literature, reports, investigation, and synthesis

skills in category: 21,409
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Arxiv 2608 19881 Interpretable Feature Learning For Rf FingerprintiAInterpretable Feature Learning for RF Fingerprinting via Polar MKANs (arXiv: 2608.19881)researchgosecurityVotes: 0GitHub stars: 3
Alternating Minimization Gate SynthesisAAlternating-minimization methodology for large-scale multimode entangling-gate synthesis in trapped-ion systems. Use when designing multi-tone control fields for entangling gates, optimizing spin-spin interactions while suppressing spin-motion entanglement, or scaling gate synthesis to large ion chains (N=100-1000). Activation: alternating minimization, gate synthesis, trapped-ion, multimode entangling, multi-tone control, spin-spin interaction, programmable interaction engineering, Mølmer-Sø...researchapiVotes: 0GitHub stars: 3
Almost Iid Quantum InformationAAlternative definitions and analysis of "almost i.i.d." quantum states for practical quantum information theory. The i.i.d. assumption is ubiquitous in quantum information theory but too stringent for practical settings. Introduces definitions based on normalized quantum Wasserstein distance and local-structure analysis. Use when: analyzing quantum information sources beyond i.i.d. assumption, designing quantum protocols for correlated sources, studying quantum Wasserstein distance applicatio...researchgosecurityVotes: 0GitHub stars: 3
Ai Quantum Comprehensive ReviewAComprehensive review of the AI-Quantum Information interface — covering AI for quantum systems (measurement, algorithm discovery, hardware stabilization) and quantum for AI (algorithmic speedups, expressivity, trainability, generalization).researchgoexpressVotes: 0GitHub stars: 3
Adaptive Qec DecoderAAdaptive confidence-gated quantum error correction decoding methodology. Two-stage inference: lightweight neural fast-path + MWPM refinement for latency-constrained QEC systems. Use when designing real-time quantum decoders, hardware-aware QEC co-design, or latency-accuracy trade-off optimization.researchpythongitVotes: 0GitHub stars: 3
Quantum Error Correction Biological AnalogyAStructural analogy methodology between quantum error correction (QEC) and biological error correction (BEC) in neural circuits, focusing on redundant encodings, constraint-based inference, and codespace protection.researchpythongoVotes: 0GitHub stars: 3
Quantispect Structure Aware 3d Cnn PredecoderAStructure-aware lightweight 3D CNN pre-decoder for scalable surface code quantum error correction. Use when designing or benchmarking neural QEC decoders for surface codes, implementing spatio-temporal syndrome processing, replacing dense 3D convolutions with factorized branches, or co-designing lightweight pre-decoders with classical matching decoders. Trigger words: QuantiSpect, surface code neural decoder, 3D CNN QEC, FastHyperBlock, quantum error correction pre-decoder, spatio-temporal sy...researchapiperformanceVotes: 0GitHub stars: 3
Pt Snn Csp SolverAParallel Tempering integration for Spiking Neural Network-based CSP solvers — overcoming local-minimum traps in stochastic SNN optimization. From arXiv:2607.08897 (Uludag et al., Jul 2026).researchVotes: 0GitHub stars: 3
Practical Quantum Topological Data AnalysisAPractical Quantum Topological Data Analysis with Applications to High-Dimensional Feature Extraction and Time Series AnalysisresearchgoperformanceVotes: 0GitHub stars: 3
Why Flow Matching Is Particle Swarm OptimizationA**arXiv ID:** 2507.20810 **Authors:** Kaichen Ouyang **Published:** 2025-07-28T13:21:14Z **Abstract:** This paper preliminarily investigates the duality between flow matching in generative models and particle swarm optimization (PSO) in evolutionary computation. Through theoretical analysis, we reveal the intrinsic connections between these two approaches in terms of their mathematical formulations and optimization mechanisms: the vector field learning in flow matching shares similar mathemat...researchgoexpressVotes: 0GitHub stars: 3
Topology From DecoherenceATopology from decoherence methodology — inducing topological quantum phases via correlated environment-induced dephasing in open many-body systems, characterized by winding numbers and non-Hermitian skin effects.researchgoVotes: 0GitHub stars: 3
Social Spatial Navigation Phase TransitionsASocial-spatial dependencies in visual navigation learning with neural network agents. Demonstrates phase transitions from individual to social following strategies based on information quality and spatial effects.researchgoperformanceVotes: 0GitHub stars: 3
Room Temperature Dipole Synchronization NanocavityARoom-temperature synchronized dipole state methodology in plasmonic nanogap 2D arrays under continuous-wave pumping.researchapiVotes: 0GitHub stars: 3
Proximodistal Exploration In Motor Learning As An Emergent Property Of OptimizationA**arXiv ID:** 1712.05249 **Authors:** Freek Stulp, Pierre-Yves Oudeyer **Published:** 2017-12-14T14:31:51Z **Abstract:** To harness the complexity of their high-dimensional bodies during sensorimotor development, infants are guided by patterns of freezing and freeing of degrees of freedom. For instance, when learning to reach, infants free the degrees of freedom in their arm proximodistally, i.e. from joints that are closer to the body to those that are more distant. Here, we formulate and st...researchVotes: 0GitHub stars: 3
Prediktor Patient Knowledge Graph Drug ResponseAPREDIKTOR: Patient-centered multi-view framework aligning personalized knowledge graphs with gene-level perturbation representations for clinical drug response prediction. Combines DysRegNet GRN construction, DrugBank integration, GNN encoding, LINCS L1000 pretraining, and CLIP-style contrastive alignment.researchpythonexpressVotes: 0GitHub stars: 3
Physicsinformed Neural Networks Biological 2mathrmdt ReactioAPhysics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-informed neural networks (BINNs) are a variant of PINNs that preserve the known differential operator Activation: neural, network, dynamics, populationresearchgoreactVotes: 0GitHub stars: 3
Phenokg Knowledge Graphdriven Gene Discovery And Patient Insights From Phenotypes AloneA**arXiv ID:** 2506.13119 **Authors:** Kamilia Zaripova, Ege Özsoy, Nassir Navab, Azade Farshad **Published:** 2025-06-16T05:54:12Z **Abstract:** Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic disorders. We propose a novel graph-based approach for predicting causative genes from patient phenotypes, with or without an available list of candidate genes, by integratin...researchVotes: 0GitHub stars: 3
Only Strict Saddles In The Energy Landscape Of Predictive Coding NetworksA**arXiv ID:** 2408.11979 **Authors:** Francesco Innocenti, El Mehdi Achour, Ryan Singh, Christopher L. Buckley **Published:** 2024-08-21T20:23:44Z **Abstract:** Predictive coding (PC) is an energy-based learning algorithm that performs iterative inference over network activities before updating weights. Recent work suggests that PC can converge in fewer learning steps than backpropagation thanks to its inference procedure. However, these advantages are not always observed, and the impact of P...researchgoVotes: 0GitHub stars: 3
On The Decision Boundary Of Deep Neural NetworksA**arXiv ID:** 1808.05385 **Authors:** Yu Li, Lizhong Ding, Xin Gao **Published:** 2018-08-16T09:25:50Z **Abstract:** While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to bridge the gap, we investigate the decision boundary of a production deep learning architecture with weak assumptions on both the training data and the model. We demonstrate, both theoretically and emp...researchgitVotes: 0GitHub stars: 3
On The Ability Of Graph Neural Networks To Model Interactions Between VerticesA**arXiv ID:** 2211.16494 **Authors:** Noam Razin, Tom Verbin, Nadav Cohen **Published:** 2022-11-29T18:58:07Z **Abstract:** Graph neural networks (GNNs) are widely used for modeling complex interactions between entities represented as vertices of a graph. Despite recent efforts to theoretically analyze the expressive power of GNNs, a formal characterization of their ability to model interactions is lacking. The current paper aims to address this gap. Formalizing strength of interactions throu...researchgoexpressVotes: 0GitHub stars: 3
Nonadiabatic Holonomic Nonhermitian GatesANonadiabatic holonomic single-qubit gates in non-Hermitian systems — leveraging exceptional points for faster geometric quantum gates while maintaining fault tolerance.researchVotes: 0GitHub stars: 3
Non Hermitian Ssh Charge CorrelationsAEnhancement of charge correlations and topological markers in interacting non-Hermitian Su-Schrieffer-Heeger models.researchVotes: 0GitHub stars: 3
Magic Entropy CftAMagic Rényi entropy methodology unifying quantification of nonstabilizerness and non-Gaussianity across spins, bosons, and fermions using conformal field theory analysis.researchgoVotes: 0GitHub stars: 3
Irreducible Correlator GeometryA**Source**: Kaito Kobayashi, "Irreducible Geometry of Higher-Order Correlator Families" (arXiv:2607.08761, July 2026)researchgoVotes: 0GitHub stars: 3