arXiv subjects

Yohannes Abate

Publications and source records attributed to Yohannes Abate.

At least 19 recordsLinked to original sources

arXiv2026

Liquid-Phase Near-Field Infrared Nanoscopy of Ion-Exchange Reactions

In situ monitoring of reactions with chemical specificity and nanometer-scale spatial resolution under aqueous conditions remains a long-standing experimental challenge. Here, we use thin SiC membrane-enabled liquid-phase infrared nanoimaging and nanospectroscopy to monitor Ca$^{2+}$/Na$^{+}$ ion exchange in a mixture of disodium terephthalate and CaCl$_2$ solution. Beneath the membrane, we follow the formation of calcium terephthalate (CaTP), a metal-organic framework (MOF)-type coordination phase in which terephthalate ligands link Ca$^{2+}$ centers. The symmetric and asymmetric carboxylate stretching modes serve as chemically specific near-field reporters of Ca$^{2+}$ coordination to the terephthalate linkers. Nano-FTIR spectra reveal pronounced peak shifts and linewidth changes in these vibrational modes, indicating modification of the carboxylate coordination environment during CaTP formation. Complementary near-field nanoimaging resolves the emergence of nanoscale precipitates beneath the SiC membrane following CaCl$_2$ injection, providing spatially resolved evidence of the formation of the CaTP coordination framework. Solid-phase nanoimaging and nanospectroscopy identify well-defined CaTP particles, providing further evidence for crystalline CaTP formation. We demonstrate that the chemistry of metal-ligand interactions can be monitored in situ via liquid-phase near-field nanoscopy. This capability could enable the study of catalytic, electrochemical, and biological processes under native aqueous conditions.

cond-mat.soft↗

arXiv2026

An Interaction Language Model: Mechanism Discovery from Statistical Patterns of Physical Interactions

Interactions among building blocks in physical, chemical, and biological systems follow structured patterns interpretable as a learnable language: just as language models learn which words tend to follow others, one can learn which physical phenomena follow others and under what conditions. We introduce an Interaction Language Model (ILM): a framework that treats interaction as a statistically structured, learnable language for design-to-function reasoning across fields. By learning statistical dependencies and ordering among components, ILMs infer interaction pathways, identify missing steps, and predict the next likely interaction. We demonstrate it through two complementary components, PhenoLink and PhenoSeq, applied to molecular diffusion, a ubiquitous mechanism for energy and particle transport. PhenoLink is a directed graph of interactions among diffusion-based events extracted from publications, where each edge aggregates paragraph-level evidence and carries a transition probability interpreted as an information cost. PhenoSeq complements this with a sequence model that proposes missing mechanistic steps under endpoint constraints. Together they form a generate-then-verify pipeline returning every explanation with a reproducible, per-step, corpus-grounded audit trail. Across four held-out suites of 172 queries, the pipeline matches a zero-shot Claude Opus 4.7 baseline on in-distribution accuracy, yet unlike the baseline, which fabricates a chain for every impossible query, it refuses up to 93.5% of transitions in corpus evidence by construction. ILM thus delivers what zero-shot generation structurally cannot: deterministic refusal of evidence-absent queries and auditable, per-step support. The framework extends to natural and engineered systems where function emerges from sequential and conditional interactions among molecules, cells, devices, or other components.

cond-mat.other↗

arXiv2026

Quantum algorithm for Valiant-Vazirani reduction

There is growing interest in extensions of the standard model of gate-based quantum computation to include auxiliary degrees of freedom evolving according to a nonlinear Schrödinger equation. By reducing the Boolean satisfiability problem SAT to quantum state discrimination, Abrams and Lloyd argued that the right type of nonlinearity can be used to solve NP and #P problems in polynomial time, at least in an idealized noise-free limit. For practical implementation, however, we are restricted to simulated and emergent nonlinearities, such as that appearing in mean field models for ultracold atoms and similar ensembles. A prominent example is the torsion model, which arises in two-component Bose-Einstein condensates and spin models with all-to-all Ising interaction. But torsion-based state discrimination appears to fall short of solving SAT. Here we close this gap by constructing the filtered oracle of the Valiant-Vazirani theorem, providing a randomized polynomial-time reduction from SAT to UNIQUE SAT, a promise problem where there is at most 1 satisfying assignment. In the noise-free limit, the UNIQUE SAT problem can be solved in polynomial time using torsion nonlinearity. Quantum Valiant-Vazirani reduction is no faster than the efficient classical version, but a fault-tolerant implementation coupled to a nonlinear quantum coprocessor simulating torsion would enable polynomial time solution to NP (but not #P) problems.

quant-ph↗

arXiv2026

Nonlinear Hamiltonians and Boolean satisfiability

We consider an extended model of quantum computation where a scalable fault-tolerant quantum computer is coupled to one or more ancilla qubits that evolve according to a nonlinear Schrödinger equation. Following the approach of Abrams and Lloyd, an efficient quantum circuit evaluating an $n$-bit Boolean function in conjunctive normal form is used to prepare an ancilla encoding its number $s$ of satisfying assignments ($0 \le s \le 2^n$). This is followed by a nonlinear quantum state discrimination gate on the ancilla qubit that is used to learn properties of $s$. Here we consider three types of state discriminators generated by different nonlinear Hamiltonians. First, given a restricted Boolean satisfiability problem with the promise of at most one satisfying assignment ($ 0 \le s \le 1$), we show that a qubit with $\langle σ^z \rangle σ^z$ nonlinearity can be used to efficiently determine whether $s = 0$ or $s = 1$, solving the UNIQUE SAT problem. Here $\langle A \rangle := \langle ψ| A |ψ\rangle $ denotes expectation in the current state. UNIQUE SAT is NP-hard under a randomized polynomial-time reduction (of course any discussion of complexity assumes a scalable, fault-tolerant implementation). Second, for unrestricted satisfiability problems with $ 0 \le s \le 2^n$, a Hamiltonian with $ \langle σ^x \rangle σ^y - \langle σ^y \rangle σ^x$ nonlinearity can be used to efficiently determine whether $s=0$ or $s>0$, thereby solving 3SAT, which is NP-complete. Finally, we show that $ \langle σ^y \rangle \langle σ^z \rangle σ^x - \langle σ^x \rangle \langle σ^z \rangle σ^y $ nonlinearity can be used to efficiently measure $s$ and solve #SAT, which is #P-complete. The nonlinear models are of mean field type and might be simulated with ultracold atoms.

quant-ph↗

arXiv2026

Graduate Training in Quantum Information Science and Engineering: Lessons, Challenges, and a Roadmap from the NSF Research Traineeship Programs

Since 2019, eighteen NSF Research Traineeship (NRT) awards in quantum information science and engineering (QISE) and adjacent fields have been funded, constituting the largest NSF-coordinated investment in graduate QISE training in the United States. Synthesizing lessons from our programs, we work through the central tensions that every QISE graduate program must negotiate: between depth in a home discipline and breadth across the field, between structured instruction and open-ended experiential and hands-on learning, and between training individual specialists and cultivating teams that collectively cover all areas of QISE. We describe the structural and pedagogical innovations the NRT programs have developed in response, assess what is working and what remains unresolved, and sketch 12 open problems the community will need to address as QISE graduate education scales beyond the well-resourced research universities where it has up till now been mainly concentrated. Eight concrete recommendations follow: (1) adopt the startup model of team-based training as an organizing philosophy; (2) invest immediately in sensing and communication curriculum development; (3) build student agency into program governance, not just activities; (4) establish structural mechanisms for industrial engagement rather than depending on goodwill; (5) design for sustainability from year one; (6) develop graduate-level textbooks spanning all three QISE pillars: computing, sensing, and communications; (7) establish shared outcome assessment instruments across programs; and (8) develop structured mechanisms for faculty professional development in QISE.

physics.ed-ph↗

arXiv2026

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems

This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that potentially combine both human and machine intelligences. The review traces this evolution from early connectionist models to state-of-the-art large language models, demonstrating how key innovations like transformer attention, foundation-model pre-training, and multi-agent architectures mirror neurobiological processes like cortical mechanisms, working memory, and episodic consolidation. We then discuss emerging physical substrates capable of breaking the von Neumann bottleneck to achieve brain-scale efficiency in silicon: memristive crossbars, in-memory compute arrays, and emerging quantum and photonic devices. There are four critical challenges at this intersection: 1) integrating spiking dynamics with foundation models, 2) maintaining lifelong plasticity without catastrophic forgetting, 3) unifying language with sensorimotor learning in embodied agents, and 4) enforcing ethical safeguards in advanced neuromorphic autonomous systems. This combined perspective across neuroscience, computation, and hardware offers an integrative agenda for in each of these fields.

q-bio.NC↗

arXiv2026

SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation

While Large Language Models (LLMs) excel at generalized reasoning, standard retrieval-augmented approaches fail to address the disconnected nature of long-term agentic memory. To bridge this gap, we introduce Synapse (Synergistic Associative Processing Semantic Encoding), a unified memory architecture that transcends static vector similarity. Drawing from cognitive science, Synapse models memory as a dynamic graph where relevance emerges from spreading activation rather than pre-computed links. By integrating lateral inhibition and temporal decay, the system dynamically highlights relevant sub-graphs while filtering interference. We implement a Triple Hybrid Retrieval strategy that fuses geometric embeddings with activation-based graph traversal. Comprehensive evaluations on the LoCoMo benchmark show that Synapse significantly outperforms state-of-the-art methods in complex temporal and multi-hop reasoning tasks, offering a robust solution to the "Contextual Tunneling" problem. Our code and data will be made publicly available upon acceptance.

cs.CL↗

arXiv2026

Nanoscopy of Excitons in Atomically Thin In-Plane Heterostructures with Nanointerfaces

Atomically sharp 2D in-plane heterostructures with nanoscale interfaces provide a powerful platform for tailoring optical and electrical properties at the nanoscale, enabling novel device engineering and the exploration of new physical phenomena. However, direct experimental correlation between local dielectric response and excitonic properties across such interfaces has remained elusive. Here, we probed the nanoscale complex dielectric function and the corresponding localized photoluminescence (PL) modulations in heterostructure domains of lateral monolayer MoxW1-xS2 - WxMo1-xS2, synthesized using a liquid-phase precursor-assisted approach. Near-field nano imaging across the visible-near-infrared range enables real space mapping of sharp amplitude and phase changes at the heterointerface, resolving the local complex dielectric function with nano-meter scale spatial resolution. Excitation energy-dependent nano spectroscopy reveals a reversal of dielectric contrast between Mo-rich and W-rich domains at their respective excitonic resonances, consistent with Lorentz-oscillator fits. Complementary hyperspectral nano-PL mapping resolves the evolution of excitonic emission across the lateral heterointerface, with neutral-exciton intensities varying continuously from W-rich to Mo-rich regions. Effective-medium theory modeling of the imaginary part of the effective dielectric function of the heterostructure as a function of photon energy and Mo filling fraction reproduces the observed excitonic trends, linking the PL evolution to a composition-dependent dielectric response. Together, these results provide direct nanoscale correlation between dielectric and excitonic boundaries in laterally stitched monolayer heterostructures and establish a multimodal near-field spectroscopy framework for probing excitonic phenomena at the nanoscale.

cond-mat.mtrl-sci↗

arXiv2026

Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models

Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.

cs.AI↗

arXiv2025

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

Two-dimensional (2D) in-plane heterostructures including compositionally graded alloys and lateral heterostructures with defined interfaces display rich optoelectronic properties and offer versatile platforms to explore one-dimensional interface physics and many-body interaction effects. Graded $\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2$ alloys show smooth spatial variations in composition and strain that continuously tune excitonic emission, while $\mathrm{MoS}_2$--$\mathrm{WS}_2$ lateral heterostructures contain atomically sharp interfaces supporting one-dimensional excitonic phenomena. These single-layer systems combine tunable optical and electronic properties with potential for stable, high-performance optoelectronic devices. Hyperspectral and nano-resolved photoluminescence (PL) imaging enable spatial mapping of optical features along with local variations in composition, strain, and defects, but manual interpretation of such large datasets is slow and subjective. Here, we introduce a fast and scalable unsupervised machine-learning (ML) framework to extract quantitative and interpretable information from hyperspectral PL datasets of graded $\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2$ alloys and $\mathrm{MoS}_2$--$\mathrm{WS}_2$ heterostructures. Combining principal-component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and density-based spatial clustering (DBSCAN), we uncover spectrally distinct domains associated with composition, strain, and defect variations. Decomposition of representative spectra reveals multiple emission species, including band-edge excitons and defect-related transitions, demonstrating that ML-driven analysis provides a robust and automated route to interpret rich optical properties of 2D materials.

cond-mat.mtrl-sci↗

arXiv2025

Bridging Classical and Quantum Computing for Next-Generation Language Models

Integrating Large Language Models (LLMs) with quantum computing is a critical challenge, hindered by the severe constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, including barren plateaus and limited coherence. Current approaches often fail due to static quantum-classical partitioning. We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge this gap through dynamic, quantum-classical co-design. AQCF's core principle is real-time adaptation: it analyzes input complexity to orchestrate seamless transitions between classical and quantum processing. The framework features three key innovations: (1) entropy-driven adaptive circuits that circumvent barren plateaus; (2) quantum memory banks that unify classical attention with quantum state-based similarity retrieval; and (3) intelligent fusion controllers that allocate tasks for optimal performance. This architecture maintains full compatibility with classical Transformers while progressively incorporating quantum advantages. Experiments on sentiment analysis demonstrate that AQCF achieves competitive performance, significantly improves quantum resource efficiency, and operates successfully within typical NISQ constraints. By providing a seamless integration pathway, AQCF offers both immediate practical value on current quantum hardware and a clear evolution path toward mature Quantum LLMs.

quant-ph↗

arXiv2025

MolQAE: Quantum Autoencoder for Molecular Representation Learning

We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecular characteristics and a remarkable preservation of fidelity, approaching robust molecular reconstruction even with substantial dimensionality reduction. Our model establishes a quantum pathway in cheminformatics by being the first to process complete molecular structural information with a dedicated quantum architecture considering the Noisy Intermediate-Scale Quantum (NISQ)-era development and promising significant advances in drug and materials discovery.

quant-ph↗

arXiv2025

Roadmap for Photonics with 2D Materials

Triggered by the development of exfoliation and the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or few atomic layers, two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and other van der Waals (vdW) crystals currently constitute a wide research field protruding in multiple directions in combination with layer stacking and twisting, nanofabrication, surface-science methods, and integration into nanostructured environments. Photonics encompasses a multidisciplinary collection of those directions, where 2D materials contribute with polaritons of unique characteristics such as strong spatial confinement, large optical-field enhancement, long lifetimes, high sensitivity to external stimuli (e.g., electric and magnetic fields, heating, and strain), a broad spectral range from the far infrared to the ultraviolet, and hybridization with spin and momentum textures of electronic band structures. The explosion of photonics with 2D materials as a vibrant research area is producing breakthroughs, including the discovery and design of new materials and metasurfaces with unprecedented properties as well as applications in integrated photonics, light emission, optical sensing, and exciting prospects for applications in quantum information, and nanoscale thermal transport. This Roadmap summarizes the state of the art in the field, identifies challenges and opportunities, and discusses future goals and how to meet them through a wide collection of topical sections prepared by leading practitioners.

cond-mat.mtrl-sci↗

arXiv2025

Large Language Models for Bioinformatics

With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

q-bio.QM↗

arXiv2024

EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis

Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. Significant advancements have been made in SNN-based convolutional neural networks (CNNs) and Transformer architectures. However, neuromorphic computing for the medical imaging domain remains underexplored. In this study, we introduce EG-SpikeFormer, an SNN architecture tailored for clinical tasks that incorporates eye-gaze data to guide the model's attention to the diagnostically relevant regions in medical images. Our developed approach effectively addresses shortcut learning issues commonly observed in conventional models, especially in scenarios with limited clinical data and high demands for model reliability, generalizability, and transparency. Our EG-SpikeFormer not only demonstrates superior energy efficiency and performance in medical image prediction tasks but also enhances clinical relevance through multi-modal information alignment. By incorporating eye-gaze data, the model improves interpretability and generalization, opening new directions for applying neuromorphic computing in healthcare.

eess.IV↗

arXiv2023

Photodegradation and Thermal Effects in Violet Phosphorus

Violet phosphorus (VP) has garnered attention for its appealing physical properties and potential applications in optoelectronics. We present a comprehensive investigation of the photo-degradation and thermal effects of exfoliated VP on SiO2 substrate. The degradation rate of VP was found to be strongly influenced by the excitation wavelength and light exposure duration. Light exposure to the above bandgap (λ > 532 nm) leads to faster degradation, attributed to interactions with reactive oxygen species. Power-dependent photoluminescence (PL) measurements at low temperature (T=4 K) showed neutral exciton (X0) and trion (T) intensities linearly increased with excitation power, while the energy difference between their peak energies decreased, indicating changes in the exciton energy gap due to degradation. At room temperature X0 and T peaks were observed with higher X0 spectral weight, indicating reduced thermal stability of T. As the temperature decreased to 4 K, both X0 and T emissions intensified with blue-shifted peak positions. The T/X0 spectral weight ratio increased from 0.28 at 300 K to 0.69 at 4 K, suggesting enhanced T formation due to reduced phonon scattering. Temperature-dependent Raman spectroscopy revealed the presence of VP up to 673K. By tracking the peak position of 9 Raman modes with temperature the linear first-order temperature coefficient were obtained and found to be linear for all modes up to 673 K. Our results provide a deeper understanding of VP's degradation behavior and implications for optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2023

Sulfur Vacancy Related Optical Transitions in Graded Alloys of MoxW1-xS2 Monolayers

Engineering the electronic bandgap is of utmost importance in diverse domains ranging from information processing and communication technology to sensing and renewable energy applications. Transition metal dichalcogenides (TMDCs) provide an ideal platform for achieving this goal through techniques including alloying, doping, and creating in-plane or out-of-plane heterostructures. Here, we report on the synthesis and characterization of atomically controlled two-dimensional graded alloy of MoxW1-xS2, wherein the center region is Mo rich and gradually transitions towards a higher concentration of W atoms at the edges. This unique alloy structure leads to a continuously tunable bandgap, ranging from 1.85 eV in the center to 1.95 eV at the edges consistent with the larger band gap of WS2 relative to MoS2. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy showed the presence of sulfur monovacancy, VS, whose concentration varied across the graded MoxW1-xS2 layer as a function of Mo content with the highest value in the Mo rich center region. Optical spectroscopy measurements supported by ab initio calculations reveal a doublet electronic state of VS, which was split due to the spin-orbit interaction, with energy levels close to the conduction band or deep in the band gap depending on whether the vacancy is surrounded by W atoms or Mo atoms. This unique electronic configuration of VS in the alloy gave rise to four spin-allowed optical transitions between the VS levels and the valence bands. Our work highlights the potential of simultaneous defect and optical engineering of novel devices based on these 2D monolayers.

cond-mat.mtrl-sci↗

arXiv2023

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Synaptic Devices

Solid-state devices made from correlated oxides such as perovskite nickelates are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3) devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveil a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in-situ - x-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential to the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

physics.app-ph↗