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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Visualizing interaction-driven restructuring of quantum Hall edge states

Many topological phases host gapless boundary modes that can be dramatically modified by electronic interactions. Even for the long-studied edge modes of quantum Hall phases, forming at the boundaries of two-dimensional (2D) electron systems, the nature of such interaction-induced changes has been elusive. Despite advances made using local probes, key experimental challenges persist: the lack of direct information about the internal structure of edge states on microscopic scales, and complications from edge disorder. Here, we use scanning tunneling microscopy (STM) to image pristine electrostatically defined quantum Hall edge states in graphene with high spatial resolution and demonstrate how correlations dictate the structures of edge channels on both magnetic and atomic length scales. For integer quantum Hall states in the zeroth Landau level, we show that interactions renormalize the edge velocity, dictate the spatial profile for copropagating modes, and induce unexpected edge valley polarization that differ from those of the bulk. While some of our findings can be understood by mean-field theory, others show breakdown of this picture, highlighting the roles of edge fluctuations and inter-channel couplings. We also extend our measurements to spatially resolve the edge state of fractional quantum Hall phases and detect spectroscopic signatures of interactions in this chiral Luttinger liquid. Furthermore, our study establishes STM as a promising tool for exploring edge physics of the rapidly expanding 2D topological phases, including newly realized fractional Chern insulators.

Electronic properties and materials↗
DOE OSTI2024

Local probe of bulk and edge states in a fractional Chern insulator

The fractional quantum Hall effect is a key example of topological quantum many-body phenomena, arising from the interplay between strong electron correlation, topological order and time-reversal symmetry breaking. Recently, a lattice analogue of the fractional quantum Hall effect at zero magnetic field has been observed, confirming the existence of a zero-field fractional Chern insulator (FCI). Despite this, the bulk–edge correspondence—a hallmark of a FCI featuring an insulating bulk with conductive edges—has not been directly observed. In fact, this correspondence has not been visualized in any system for fractional states owing to experimental challenges. Here we report the imaging of FCI edge states in twisted MoTe 2 (t-MoTe 2 ) using microwave impedance microscopy. By tuning the carrier density, we observe the system evolving between metallic and FCI states, the latter of which exhibits insulating bulk and conductive edges, as expected from the bulk–boundary correspondence. Further analysis suggests the composite nature of the FCI edge states. We also observe the evolution of edge states across the topological phase transition as a function of interlayer electric field and reveal exciting prospects of neighbouring domains with different fractional orders. Furthermore, these findings pave the way for research into topologically protected one-dimensional interfaces between various anyonic states at zero magnetic field, such as gapped one-dimensional symmetry-protected phases with non-zero topological entanglement entropy, Halperin–Laughlin interfaces and the creation of non-abelian anyons.

Imaging techniques↗
DOE OSTI2025

Low-temperature state in strontium titanate microcrystals using in situ multireflection Bragg coherent x-ray diffraction imaging

Strontium titanate is a classic quantum paraelectric oxide material that has been widely studied in bulk and thin films. It exhibits a well-known cubic-to-tetragonal antiferrodistortive phase transition at 105 K, characterized by the rotation of oxygen octahedra. A possible second phase transition at lower temperature is suppressed by quantum fluctuations, preventing the onset of ferroelectric order. However, recent studies have shown that ferroelectric order can be established at low temperatures by inducing strain and other means. Here, we used in situ multireflection Bragg coherent x-ray diffraction imaging to measure the strain and rotation tensors for two strontium titanate microcrystals at low temperature. We observe strains induced by dislocations and inclusion-like impurities in the microcrystals. Based on radial magnitude plots, these strains increase in magnitude and spread as the temperature decreases. Pearson's correlation heat maps show a structural transition at 50 K, which could possibly be the formation of a low-temperature ferroelectric phase in the presence of strain. We do not observe any change in local strains associated with the tetragonal phase transition at 105 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware

We implement a quantum generalization of a neural network on trapped-ion and IBM superconducting quantum computers to classify MNIST images, a common benchmark in computer vision. The network feedforward involves qubit rotations whose angles depend on the results of measurements in the previous layer. The network is trained via simulation, but inference is performed experimentally on quantum hardware. The classical-to-quantum correspondence is controlled by an interpolation parameter, $a$, which is zero in the classical limit. Increasing $a$ introduces quantum uncertainty into the measurements, which is shown to improve network performance at moderate values of the interpolation parameter. We then focus on particular images that fail to be classified by a classical neural network but are detected correctly in the quantum network. For such borderline cases, we observe strong deviations from the simulated behavior. We attribute this to physical noise, which causes the output to fluctuate between nearby minima of the classification energy landscape. Such strong sensitivity to physical noise is absent for clear images. We further benchmark physical noise by inserting additional single-qubit and two-qubit gate pairs into the neural network circuits. Our work provides a springboard toward more complex quantum neural networks on current devices: while the approach is rooted in standard classical machine learning, scaling up such networks may prove classically non-simulable and could offer a route to near-term quantum advantage.

FOS: Physical sciences↗
DOE OSTI2025

Colossal Terahertz Emission with Ultrafast Tunability Based on Van der Waals Ferroelectric NbOI 2

Abstract Terahertz (THz) technology is critical for quantum material physics, biomedical imaging, ultrafast electronics, and next‐generation wireless communications. However, standing in the way of widespread applications is the scarcity of efficient ultrafast THz sources with on‐demand fast modulation and easy on‐chip integration capability. Here the discovery of colossal THz emission is reported from a van der Waals (vdW) ferroelectric semiconductor NbOI 2 . Using THz emission spectroscopy, a THz generation efficiency an order of magnitude higher than that of ZnTe, a standard nonlinear crystal for ultrafast THz generation is observed. The underlying generation mechanisms associated are further uncovered with its large ferroelectric polarization by studying the THz emission dependence on excitation wavelength, incident polarization, and fluence. Moreover, the ultrafast coherent amplification and annihilation of the THz emission and associated coherent phonon oscillations by employing a double‐pump scheme are demonstrated. These findings combined with first‐principles calculations, inform a new understanding of the THz light–matter interaction in emergent vdW ferroelectrics and pave the way to develop high‐performance THz devices on them for quantum materials sensing and ultrafast electronics.

Subedi, Sujan [Department of Materials Science and↗
DOE OSTI2026

Quantum Computing Algorithms and Applications for Coherent and Strongly Correlated Chemical Systems

This project advanced quantum algorithms, quantum information theory, strongly correlated electronic structure methods, molecular quantum materials, and exciton transport imaging in coherent condensed phase systems. Across the award period, the team developed new methods for open-quantum-system simulation, Hamiltonian learning, state tomography, reduced-density-matrix and contracted-quantum eigensolver approaches, and quantum diagnostics for device capability and openness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exploiting a Shortcoming of Coupled-Cluster Theory: The Extent of Non-Hermiticity as a Diagnostic Indicator of Computational Accuracy

The fundamental non-Hermitian nature of the forms of the coupled-cluster (CC) theory widely used in quantum chemistry has usually been viewed as a negative, but the present paper shows how this can be used to an advantage. Specifically, the non-symmetric nature of the reduced one-particle density matrix (in the molecular orbital basis) is advocated as a diagnostic indicator of computational quality. In the limit of the full coupled-cluster theory [which is equivalent to full configuration interaction (FCI)], the electronic wave function and correlation energy are exact within a given one-particle basis set, and the symmetric character of the exact density matrix is recovered. The extent of the density matrix asymmetry is shown to provide a measure of “how difficult the problem is” (like the well-known T 1 diagnostic), but its variation with the level of theory also gives information about “how well this particular method works”, irrespective of the difficulty of the problem at hand. The proposed diagnostic is described and applied to a select group of small molecules, and an example of its overall utility for the practicing quantum chemist is illustrated through its application to the beryllium dimer (Be 2 ). Future application of this idea to excited states, open-shell systems, and symmetry-breaking problems and an extension of the method to the two-particle density are then proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Sensitive proton-radiation detectors

In this study, halide perovskite nanocrystal scintillators detect high-energy protons with sensitivity suitable for clinical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nanoscale engineering and dynamic stabilization of mesoscopic spin textures

Thermalization, while ubiquitous in physics, has traditionally been viewed as an obstacle to be mitigated. In contrast, we demonstrate here the use of thermalization in the generation, control, and readout of “shell-like” spin textures with interacting 13 C nuclear spins in diamond, wherein spins are polarized oppositely on either side of a critical radius. The textures span several nanometers and encompass many hundred spins; they are created and interrogated without manipulating the nuclear spins individually. Long-time stabilization is achieved via prethermalization to a Floquet-engineered Hamiltonian under the electronic gradient field: The texture is therefore metastable and robust against spin diffusion. This enables the state to endure over multiple minutes before it decays. Our work on spin-state engineering paves the way for applications in quantum simulation and nanoscale imaging.

42 ENGINEERING↗
DOE OSTI2026

Fast and bright scintillators for ultrafast materials dynamics using 4th generation synchrotron

We present recent advances in fast and bright scintillators for ultrafast X-ray phase contrast imaging of dynamic materials experiments at the upgraded Advanced Photon Source (APS-U), a fourth generation synchrotron. APS-U enables hard X-ray imaging at frame rates of at least 13 MHz (corresponding to 77 ns or shorter interframe intervals), creating a new need for scintillators with faster response and higher light output than lutetium yttrium oxyorthosilicate (LYSO). For indirect imaging and diffraction with ultrafast cameras, commercial lanthanum bromide (LaBr3) and cerium bromide (CeBr3) are promising candidates. These materials exhibit decay times approximately a factor of two shorter than LYSO (around 40 ns) and lutetium oxyorthosilicate (LSO), while maintaining comparable light yield per incident X-ray photon. However, their implementation at APS-U requires addressing several challenges, including material limitations due to hygroscopicity, efficient optical coupling to imaging systems, and high quantum efficiency for conversion of scintillation light, predominantly at wavelengths below 400 nm, into detectable electronic signals. We report results from material characterization, detector integration and packaging, and beamline experiments of materials with impact. In addition, emerging scintillator classes, including perovskites and high-entropy materials, are discussed as potential alternatives for next-generation ultrafast X-ray diagnostics.

Wang, Zhehui [Los Alamos] (ORCID:0000000178264063)↗
DOE OSTI2025

A Quantum Leap for Dynamic Radiography

Dynamic radiography techniques have been instrumental in advancing the National Nuclear Security Administration's (NNSA) mission since WWII. This paper explores the transformative potential of quantum information science (QIS) to revolutionize dynamic radiography through enhanced image processing, tomographic reconstruction, statistics and uncertainty quantification (UQ), and artificial intelligence integration. By leveraging quantum algorithms to extract previously inaccessible information from existing datasets, this interdisciplinary approach promises unprecedented insights at the intersection of dynamic imaging, artificial intelligence, and quantum information technologies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Bright and durable scintillation from colloidal quantum shells

Abstract Efficient, fast, and robust scintillators for ionizing radiation detection are crucial in various fields, including medical diagnostics, defense, and particle physics. However, traditional scintillator technologies face challenges in simultaneously achieving optimal performance and high-speed operation. Herein we introduce colloidal quantum shell heterostructures as X-ray and electron scintillators, combining efficiency, speed, and durability. Quantum shells exhibit light yields up to 70,000 photons MeV −1 at room temperature, enabled by their high multiexciton radiative efficiency thanks to long Auger-Meitner lifetimes (>10 ns). Radioluminescence is fast, with lifetimes of 2.5 ns and sub-100 ps rise times. Additionally, quantum shells do not exhibit afterglow and maintain stable scintillation even under high X-ray doses (>10 9 Gy). Furthermore, we showcase quantum shells for X-ray imaging achieving a spatial resolution as high as 28 line pairs per millimeter. Overall, efficient, fast, and durable scintillation make quantum shells appealing in applications ranging from ultrafast radiation detection to high-resolution imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

AI↗
DOE OSTI2025

Identifying the quantum properties of hadronic resonances using machine learning

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) (‘color’) as well as the spin of a two-prong resonance using its substructure. Additionally, jet-images are useful in determining what information in the jet radiation pattern is useful for classification, which could inspire future taggers. These techniques improve the categorization of new particles and are an important addition to the growing jet substructure toolkit, for searches and measurements at the LHC now and in the future.

Filipek, Jakub↗
DOE OSTI2024

Quantum Annealing for Real-World Machine Learning Applications

Optimizing the training of a machine learning pipeline is important for reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications have shown interesting results especially under the conditions where the performance of classical machine learning techniques are limited such as limited training data and high dimensional features. This chapter explores the application of D-Wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, security, computational biology, biomedical sciences, and physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

Kumar nath, Rajdeep↗
DOE OSTI2025

Automated Detection of Qubit Structures on Quantum Chips

In this study, YOLO (You Only Look Once), a well-known object detection and image segmentation model, is used to detect qubits on a quantum chip. The model is trained by exposing it to various images of objects of interest and by setting various training parameters to allow the model to learn in unconventional conditions. We were able to provide the model with numerous images of qubits and various prominent features of note located on a qubit. We then created a simple GUI to display the model's live detections that would be integrated with the SEMs. This allowed us to create various functions such as auto-imagery of the qubits across a chip, auto-focusing, auto-contrasting, automatic staging, and directional corrections, enabling users to scan and analyze qubit surfaces in a fast and effective manner.

Perjuste, Ruth [Wellesley Coll.]↗
DOE OSTI2025

Automated Detection of Qubit Structures on Quantum Chips

In this study, YOLO (You Only Look Once), a well-known object detection and image segmentation model, is used to detect qubits on a quantum chip. The model is trained by exposing it to various images of objects of interest and by tweaking various training parameters to allow the model to learn from a limited and highly specialized dataset of SEM imagery. The model was provided with numerous images of qubits and various prominent features of note located on a qubit. These detections were then integrated into a simple GUI, allowing for real-time feedback alongside SEM usage. This will allow us to create various functions enabling automated functions like chip-wide scanning, auto-focusing, and image correction.

Perjuste, Ruth [Wellesley Coll.]↗
DOE OSTI2025

Quantum frequency resampling

In signal processing, resampling algorithms can modify the number of resources encoding a collection of data points. Downsampling reduces the cost of storage and communication, while upsampling interpolates new data from limited one, e.g., when resizing a digital image. We present a toolset of quantum algorithms to resample data encoded in the probabilities of a quantum register, using the quantum Fourier transform to adjust the number of high-frequency encoding qubits. We discuss advantage over classical resampling algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗