Moiré Surface States and Enhanced Superconductivity in Topological Insulators
Not Available
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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Results for “Noah”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Microwave-Based Quantum Control and Coherence Protection of Tin-Vacancy Spin Qubits in a Strain-Tuned Diamond-Membrane Heterostructure
Robust spin-photon interfaces in solids are essential components in quantum networking and sensing technologies. Ideally, these interfaces combine a long-lived spin memory, coherent optical transitions, fast and high-fidelity spin manipulation, and straightforward device integration and scaling. The tin- vacancy center (SnV) in diamond is a promising spin-photon interface with desirable optical and spin properties at 1.7 K. However, the SnV spin lacks efficient microwave control, and its spin coherence degrades with higher temperature. In this work, we introduce a new platform that overcomes these challenges—SnV centers in uniformly strained thin diamond membranes. The controlled generation of crystal strain introduces orbital mixing that allows microwave control of the spin state with 99.36(9)% gate fidelity and spin coherence protection beyond a millisecond. Moreover, the presence of crystal strain suppresses temperature-dependent dephasing processes, leading to a considerable improvement of the coherence time up to 223(10) μs at 4 K, a widely accessible temperature in common cryogenic systems. Critically, the coherence of optical transitions is unaffected by the elevated temperature, exhibiting nearly lifetime-limited optical linewidths. Combined with the compatibility of diamond membranes with device integration, the demonstrated platform is an ideal spin-photon interface for future quantum technologies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Atomic-Scale Tracking of Topological Defect Motion and Incommensurate Charge Order Melting
Charge order pervades the phase diagrams of quantum materials where it competes with superconducting and magnetic phases, hosts electronic phase transitions and topological defects, and couples to the lattice generating intricate structural distortions. Incommensurate charge order is readily stabilized in manganese oxides, where it is associated with anomalous electronic and magnetic properties, but its nanoscale structural inhomogeneity complicates precise characterization and understanding of its relationship with competing phases. Leveraging atomic-resolution variable-temperature cryogenic scanning transmission electron microscopy, we characterize the thermal evolution of charge order as it transforms from its ground state in a model manganite system. We find that mobile networks of discommensurations and dislocations generate phase inhomogeneity and induce global incommensurability in an otherwise lattice-locked modulation. Driving the order to melt at high temperatures, the discommensuration density grows, and regions of order locally decouple from the lattice periodicity. Published by the American Physical Society 2025
Schnitzer, Noah (ORCID:0000000210421146)↗
Toward a 2D Local Implementation of Quantum Low-Density Parity-Check Codes
Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes that affects code performance and ease of physical realization. For device architectures restricted to two-dimensional (2D) local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error-correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate-bicycle qLDPC codes and show that they are well suited for a parallel syndrome-measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes, bivariate-bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits. Published by the American Physical Society 2025
Berthusen, Noah (ORCID:0000000275862786)↗
Practical Introduction to Benchmarking and Characterization of Quantum Computers
Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV’s core models and concepts—quantum states, measurements, and processes—and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.
open quantum systems & decoherence↗
Noise Constraints on Sensitivity Scaling in Quantum Nonlinear Metrology
Quantum-enhanced metrology surpasses classical metrology by improving estimation precision scaling with a resource 𝑁 (e.g., particle number or energy) from 1/√𝑁 to 1/𝑁. Through the use of nonlinear effects, Roy and Braunstein [Exponentially enhanced quantum metrology, Phys. Rev. Lett. 100, 220501 (2008)] derived a 1/𝑁 2 scaling. However, later works argued this exponential improvement is unphysical and that even modest gains, like 1/𝑁 2 , may vanish under noise. We show that, in the presence of small errors, the nonlinear interactions enabling metrological enhancement induce emergent errors. Here, the errors propagate through the sensing protocol and are magnified proportional to any intended nonlinear enhancement. We identify a critical value of the parameter to be estimated, for a fixed error, below which the emergent errors can be avoided.
Quantum Fisher information↗
Energy-relaxation pathways in finite-sized artificial square-ice structures
The energy-relaxation pathway in artificial spin ice structures refers to the series of individual moment reorientations the system undergoes as it relaxes from an initial state to a final low-energy state. The probability of the artificial spin ice structure reaching a ground state depends on the energy of the intermediate states as approximated by an Arrhenius-type relation. In this work, we report that by modifying the geometry of artificial square-ice structures, we can influence the probability of observing the ground state in different configurations. Furthermore, our findings are supported by dipolar energy calculations for various energy-relaxation pathways and the corresponding experimental data obtained by magnetic force microscopy.
Analog computation↗
Robust negativity in the quantum-to-classical transition of Kerr dynamics
Here, we quantify the quantum-to-classical transition of the single-mode Kerr nonlinear dynamics in the presence of loss. We establish three timescales that govern the dynamics, each with distinct characteristics. For times short compared with the Ehrenfest time, the evolution is classical, characterized by Gaussian dynamics. For sufficiently long times, as we increase the initial photon number, unitary Kerr evolution would generate macroscopic superpositions of coherent states (so-called kitten states). However, this is severely restricted in the presence of small photon loss, and the expectation values of observables coincide with their classical values. The intermediate timescale, however, shows resilient quantum behavior in the macroscopic limit. We show that in the mean-field non-Gaussian regime, the Kerr Hamiltonian (with small photon loss) generates a significant amount of Wigner-negativity, and classical flow is recovered only if the loss rate grows with system size. Our results broaden the usual understanding of quantum-to-classical transitions and demonstrate the potential for creating robust nonclassical resources for continuous-variable quantum information processing in the presence of loss.
Raza, Mohsin [University of New Mexico, Albuquerqu↗
Measurement of the 252 Cf (sf) prompt fission neutron spectrum utilizing 12 C (𝑛, 𝑛) and 9 Be (𝑛, 𝑛) neutron scattering reference measurements
The 252 Cf spontaneous fission (sf), prompt fission neutron spectrum (PFNS) is a fundamental quantity for nuclear physics measurements of neutron-emitting reactions. This energy distribution of neutrons emitted from fission has been considered a neutron data standard for decades and has been utilized as a reference for neutron detection efficiency, validation of Monte Carlo simulations, benchmarking of dosimetry standards, and more. A significant portion of the global collection of nuclear data on neutron-induced reactions is correlated with the 252 Cf (sf) PFNS. Despite the reliance on this quantity by the nuclear physics community, the historical collection of 252 Cf PFNS measurements display systematic disagreements that are not understood or easily explained. These experimental discrepancies could potentially bias the 252 Cf PFNS Standard evaluation. On top of this, these past experiments frequently employed correlated experimental measurement or analysis methods. The artificial intelligence (AI)/machine learning (ML)-informed californium chi-nuclear data experiment (AIACHNE) project was formed to (a) investigate these discrepancies utilizing AI/ML methods to identify outlying regions of literature data, assign these regions to features of the experiment itself, and perform an improved evaluation of the 252 Cf PFNS and (b) perform a new experimental measurement of this quantity designed to improve upon the existing literature database. Here, in this work, we report on the AIACHNE 252 Cf PFNS experiment utilizing a new analysis method uncorrelated with all previous measurements: neutron efficiency determinations based on elastic neutron scattering on 12 C and 9 Be . This new method provides an independent test of the existing literature data and evaluation of the 252 Cf (sf) PFNS. The method is described with detailed covariance quantification procedures, as well as a direct discussion of the sources of uncertainty described as requirements in the “Templates” series of papers. The 252 Cf (sf) PFNS reported in this work agrees well with the overall shape of the existing standard PFNS evaluation as well as many literature measurements, thus verifying the current evaluation utilizing new techniques. However, the results suggest that there are deficiencies in the angle-differential 12 C and 9 Be (𝑛, 𝑛) evaluated nuclear data, which produce unphysical structures in the reported result. While these structures are relatively minor, they become obvious because of the high statistical precision of the data and the expected smooth continuity of the 252 Cf (sf) PFNS.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Quantitative approaches for multiscale structural analysis with atomic resolution electron microscopy
Atomic-resolution imaging with scanning transmission electron microscopy is a powerful tool for characterizing the nanoscale structure of materials, in particular features such as defects, local strains, and symmetry-breaking distortions. In addition to advanced instrumentation, the effectiveness of the technique depends on computational image analysis to extract meaningful features from complex datasets recorded in experiments, which can be complicated by the presence of noise and artifacts, small or overlapping features, and the need to scale analysis over large representative areas. Here, we present image analysis approaches which synergize real and reciprocal space information to efficiently and reliably obtain meaningful structural information with picometer scale precision across hundreds of nanometers of material from atomic-resolution electron microscope images. Damping superstructure peaks in reciprocal space allows symmetry-breaking structural distortions to be disentangled from other sources of inhomogeneity and measured with high precision. Real-space fitting of the wavelike signals resulting from Fourier filtering enables absolute quantification of lattice parameter variations and strain, as well as the uncertainty associated with these measurements. Implementations of these algorithms are made available as an open source python package.
36 MATERIALS SCIENCE↗
Mapping the gas density with the kinematic Sunyaev-Zel’dovich and patchy screening effects: A self-consistent comparison
The secondary anisotropies of the cosmic microwave background (CMB) provide a wealth of astrophysical and cosmological information. Pairing measurements of the CMB temperature map obtained by DR5 of the Atacama Cosmology Telescope (ACT) with the imaging survey conducted by the Dark Energy Spectroscopic Instrument for the purposes of target selection, DECaLS DR9, we investigate two effects that are sensitive to the gas density 𝜏: kinematic Sunyaev-Zel’dovich (kSZ) and patchy screening or anisotropic screening (resulting from the Thomson scattering of CMB photons away from the line-of-sight by free electrons). In particular, we measure the stacked profiles of the gas density around luminous red galaxies (LRGs) at a mean redshift of 𝑧 ≈ 0.7. We detect the kSZ signal at 7.2𝜎, and we find a signal at ∼ 4.1𝜎 for the patchy screening estimator, which is in excess relative to the kSZ signal. We attribute this excess to contamination from CMB lensing. Here, we demonstrate the effect of lensing using 𝑁-body simulations, and we show that the screening signal is dominated by it. Accounting for lensing, our measurement places a 95% upper bound on the optical depth of the Extended DESI LRG sample of 𝜏 < 2.5 10 −4 for a mean value of the sample of 𝜏 ≈ 1.6 10 −4 . Furthermore, via hydro simulations, we show that the underlying optical depth signal measured by both effects (after removing the CMB lensing contribution) is in perfect agreement when adopting either a compensated aperture photometry (CAP) filter or a high-pass filter. Consistent with previous measurements, we see evidence for excess baryonic feedback around DESI LRGs in the patchy screening measurement. In the future, when both effects can be measured with high signal-to-noise, one can measure the amplitude ratio between them, which is proportional to the root-mean-square velocity of the host halo sample, and even place constraints on velocity-sensitive models such as modified gravity and phantom dark energy.
Astrophysical & cosmological simulations↗
POWTEX visits POWGEN
The high-intensity time-of-flight (TOF) neutron diffractometer POWTEX for powder and texture analysis is currently being built prior to operation in the eastern guide hall of the research reactor FRM II at Garching close to Munich, Germany. Because of the world-wide 3 He crisis in 2009, the authors promptly initiated the development of 3 He-free detector alternatives that are tailor-made for the requirements of large-area diffractometers. Herein is reported the 2017 enterprise to operate one mounting unit of the final POWTEX detector on the neutron powder diffractometer POWGEN at the Spallation Neutron Source located at Oak Ridge National Laboratory, USA. As a result, presented here are the first angular- and wavelength-dependent data from the POWTEX detector, unfortunately damaged by a 50 g shock but still operating, as well as the efforts made both to characterize the transport damage and to successfully recalibrate the voxel positions in order to yield nonetheless reliable measurements. Also described is the current data reduction process using the PowderReduceP2D algorithm implemented in Mantid [Arnold et al. (2014). Nucl. Instrum. Methods Phys. Res. A , 764 , 156–166]. The final part of the data treatment chain, namely a novel multi-dimensional refinement using a modified version of the GSAS-II software suite [Toby & Von Dreele (2013). J. Appl. Cryst. 46 , 544–549], is compared with a standard data treatment of the same event data conventionally reduced as TOF diffraction patterns and refined with the unmodified version of GSAS-II . This involves both determining the instrumental resolution parameters using POWGEN's powdered diamond standard sample and the refinement of a friendly-user sample, BaZn(NCN) 2 . Although each structural parameter on its own looks similar upon comparing the conventional (1D) and multi-dimensional (2D) treatments, also in terms of precision, a closer view shows small but possibly significant differences. For example, the somewhat suspicious proximity of the a and b lattice parameters of BaZn(NCN) 2 crystallizing in Pbca as resulting from the 1D refinement (0.008 Å) is five times less pronounced in the 2D refinement (0.038 Å). Similar features are found when comparing bond lengths and bond angles, e.g. the two N—C—N units are less differently bent in the 1D results (173 and 175°) than in the 2D results (167 and 173°). The results are of importance not only for POWTEX but also for other neutron TOF diffractometers with large-area detectors, like POWGEN at the SNS or the future DREAM beamline at the European Spallation Source.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Verifying Qthreads: Is Model Checking viable for User-Level Tasking runtimes?.
Abstract not provided.
Evans, Noah↗
Load Margin Constrained Moving Target Defense against False Data Injection Attacks
Cyber physical security of power systems with high penetration of renewable generation has attracted attention from researchers. One critical issue is that cyber-physical attacks, disguised as uncertain renewable generation, can target conventional power system state estimation (SE). Moving target defense (MTD) is a promising defense strategy to detect stealthy false data injection (FDI) attacks against SE. However, all existing studies myopically perturb the reactance of transmission lines equipped with distributed flexible AC transmission system (D-FACTS) devices without adequately considering the system voltage stability. Exacerbated by the renewable generation uncertainty, existing MTD may cause voltage instability when the power grid is under stress. To address this issue, we propose a novel MTD framework that explicitly considers system voltage stability by using continuation power flow. We utilize the sensitivity matrix of power injection to line impedance, on which an optimization problem for maximizing load margin is formulated. This framework is validated on the IEEE 14-bus system and the IEEE 118-bus system, in which net load redistribution attacks are launched by sophisticated attackers. Steady-state simulations and dynamic simulations on PSS/E show the effectiveness of the proposed framework in circumventing the voltage instability while maintaining the detection effectiveness of MTD. The impact of the proposed method on attack detection effectiveness is also revealed.
Zhang, Hang↗
Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis
The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.
benchmark↗
Limitations of Transformers on Clinical Text Classification
Not Available
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces
Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.
Tadimeti, Vijay [ORNL]↗
Advancements in Superconducting Electric Propulsion: Comparative Analysis and Progress Update of CHEETA and CRUISE Motors
Not Available
Balachandran, Thanatheepan [Hinetics Inc, Champaig↗