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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dependable classical-quantum computing systems engineering

Increasing evidence suggests quantum computing (QC) complements traditional High-Performance Computing (HPC) by leveraging its unique capabilities, leading to the emergence of a new, hybrid paradigm, QHPC. However, this integration introduces new challenges, with dependability–defined by reproducibility, resiliency, and security and privacy–emerging as a central concern for building trustworthy systems that provide an advantage to the users. This paper proposes a framework for dependable QHPC system design, organized around these three pillars. We identify integration challenges, anticipate roadblocks, and highlight productive synergies across QC, HPC, cloud platforms, and network security. Drawing from both classical computing principles and quantum-specific insights, we present a roadmap for co-design that supports robust hybrid architectures. Our approach offers concrete metrics for assessing dependability, provides design guidance for engineers working at the QC-HPC interface, and surfaces new engineering questions around complexity, scale, and fault tolerance. Ultimately, designing for dependability is key to realizing practical, scalable QHPC systems and accelerating the broader quantum ecosystem capable of translating quantum promises into actual application delivery.

HPC↗
DOE OSTI2024

The expressivity of classical and quantum neural networks on entanglement entropy

Abstract Analytically continuing the von Neumann entropy from Rényi entropies is a challenging task in quantum field theory. While then-th Rényi entropy can be computed using the replica method in the path integral representation of quantum field theory, the analytic continuation can only be achieved for some simple systems on a case-by-case basis. In this work, we propose a general framework to tackle this problem using classical and quantum neural networks with supervised learning. We begin by studying several examples with known von Neumann entropy, where the input data is generated by representing$${\text {Tr}}\rho _A^n$$ Tr ρ A n with a generating function. We adopt KerasTuner to determine the optimal network architecture and hyperparameters with limited data. In addition, we frame a similar problem in terms of quantum machine learning models, where the expressivity of the quantum models for the entanglement entropy as a partial Fourier series is established. Our proposed methods can accurately predict the von Neumann and Rényi entropies numerically, highlighting the potential of deep learning techniques for solving problems in quantum information theory.

Physics↗
NASA NTRSDate not supplied

Precision Synchronization for Free-Space Quantum Networking

Precision synchronization is vital for robust long-distance quantum networking over fiber and free-space channels for which high-fidelity entanglement swapping between separate sources via an optical Bell state measurement requires temporal overlap of photonic qubits arriving from either source. This challenge is particularly distinct in satellite-based entanglement distribution in which relative motion, channel effects, and propagation delay must be addressed. This work presents a precision synchronization method for free space entanglement distribution, and reports on risk reduction testing in a quantum networking testbed at MIT Lincoln Laboratory. Primary consideration is for a dual-uplink architecture in which photons from entanglement sources at two ground locations interact in an optical Bell-state measurement implemented on a satellite in a low-earth orbit. The control approach uses independent entanglement sources at each ground location supplemented with a synchronization signal for feedback control from a timing discriminant measured at the spacecraft. The approach is being implemented in a laboratory testbed using 1-GHz repetition rate 1550-nm band entanglement sources generating ~10-MHz source entanglement rates with few-ps photon pulse lengths. The paper describes both fundamental architectural considerations and practical implementation details.

quantum↗
DOE OSTI2022

Observation of well-defined Kohn-anomaly in high-quality graphene devices at room temperature

Abstract Due to its ultra-thin nature, the study of graphene quantum optoelectronics, like gate-dependent graphene Raman properties, is obscured by interactions with substrates and surroundings. For instance, the use of doped silicon with a capping thermal oxide layer limited the observation to low temperatures of a well-defined Kohn-anomaly behavior, related to the breakdown of the adiabatic Born–Oppenheimer approximation. Here, we design an optoelectronic device consisting of single-layer graphene electrically contacted with thin graphite leads, seated on an atomically flat hexagonal boron nitride substrate and gated with an ultra-thin gold layer. We show that this device is optically transparent, has no background optical peaks and photoluminescence from the device components, and no generation of laser-induced electrostatic doping (photodoping). This allows for room-temperature gate-dependent Raman spectroscopy effects that have only been observed at cryogenic temperatures so far, above all the Kohn-anomaly phonon energy normalization. The new device architecture, by decoupling graphene optoelectronic properties from the substrate effects, allows for observing quantum phenomena at room temperature.

Materials Science↗
DOE OSTI2022

The basics of quantum computing for chemists

The rapid and successful strides in quantum chemistry in the past decades can be largely credited to a conspicuous synergy between theoretical and computational advancements. However, the architectural computer archetype that enabled such a progress is approaching a state of more stagnant development. One of the most promising technological avenues for the continuing progress of quantum chemistry is the emerging quantum computing paradigm. This revolutionary proposal comes with several challenges, which span a wide array of disciplines. In chemistry, it implies, among other things, a need to reformulate some of its long established cornerstones in order to adjust to the operational demands and constraints of quantum computers. Due to its relatively recent emergence, much of quantum computing may still seem fairly nebulous and largely unknown to most chemists. It is in this context that here we review and illustrate the basic aspects of quantum information and their relation to quantum computing insofar as enabling simulations of quantum chemistry. As a result, we consider some of the most relevant developments in light of these aspects and discuss the current landscape when of relevance to quantum chemical simulations in quantum computers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2016

A Quantum-Assisted Algorithm for Sampling Applications in Machine Learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact in deep learning and other machine learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggests it will do so with an instance-dependent effective temperature, different from the physical temperature of the device. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this talk, we present a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a kind of restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature we find a performance close to that of CD-100 for the case studied here. We discuss generalizations of the algorithm to other more expressive generative models, beyond restricted Boltzmann machines.

Perdomo-Ortiz, Alejandro↗
DOE OSTI2024

ELECTRONIC STRUCTURE METHODS AND PROTOCOLS WITH APPLICATION TO DYNAMICS, KINETICS AND THERMOCHEMISTRY

Hydrocarbon combustion involves the reaction dynamics of a tremendous number of species beginning with many-component fuel mixtures and proceeding via a complex system of intermediates to form primary and secondary products. Combustion conditions corresponding to new advanced engines and/or alternative fuels rely increasingly on autoignition and low-temperature-combustion chemistry. In these regimes various transient radical species such as HO2, ROO·, ·QOOH, HCO, NO2, HOCO, and Criegee intermediates play important roles in determining the detailed as well as more general dynamics. A clear understanding and accurate representation of these processes is needed for effective modeling. Given the difficulties associated with making reliable experimental measurements of these systems, computation can play an important role in developing these energy technologies. Accurate calculations have their own challenges since even within the simplest dynamical approximations such as transition state theory, the rates depend exponentially on critical barrier heights and these may be sensitive to the level of quantum chemistry. Moreover, it is well-known that in many cases it is necessary to go beyond statistical theories and consider the dynamics. Quantum tunneling, resonances, radiative transitions, and non-adiabatic effects governed by spin-orbit or derivative coupling can be determining factors in those dynamics. Building upon progress made during a period of prior support through the DOE Early Career Program, this project combines developments in the areas of potential energy surface (PES) fitting and multistate multireference quantum chemistry to allow spectroscopically and dynamically/kinetically accurate investigations of key molecular systems (such as those mentioned above), many of which are radicals with strong multireference character and have the possibility of multiple electronic states contributing to the observed dynamics. An ongoing area of investigation is to develop general strategies for robustly convergent electronic structure theory for global multichannel reactive surfaces including diabatization of energy and other relevant surfaces such as dipole transition. Combining advances in ab initio methods with automated interpolative PES fitting allows the construction of high-quality PESs (incorporating thousands of high-level data) to be done rapidly through parallel processing on high-performance computing (HPC) clusters. In addition, new methods and approaches to electronic structure theory will be developed and tested through applications. This project will explore limitations in traditional multireference calculations (e.g., MRCI) such as those imposed by internal contraction, lack of high-order correlation treatment and poor scaling. Methods such as DMRG-based extended active-space CASSCF and various Quantum Monte Carlo (QMC) methods will be applied (including VMC/DMC and FCIQMC). Insight into the relative significance of different orbital spaces and the robustness of application of these approaches on leadership class computing architectures will be gained. Synergy with other components of this research program such as automated PES fitting and multireference quantum chemistry will be used to address challenges encountered by the standard approaches to computational thermochemistry (those being single-reference quantum chemistry and perturbative treatments of the anharmonic vibrational energy, which break down for some cases of electronic structure or floppy strongly coupled vibrational modes).

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

High Efficiency Inverted GaAs and GaInP/GaAs Solar Cells With Strain-Balanced GaInAs/GaAsP Quantum Wells

High-efficiency solar cells are essential for high-density terrestrial applications, as well as space and potentially vehicle applications. The optimum bandgap for the terrestrial spectrum lies beyond the absorption range of a traditional dual junction GaInP/GaAs cell, with the bottom GaAs cell having higher bandgap energy than necessary. Lower energy bandgaps can be achieved with multiple quantum wells (QWs), but such a pathway requires advanced management of the epitaxial growth conditions in order to be useful. Strain-balanced GaAsP/GaInAs QWs are incorporated into a single junction GaAs solar cell and a dual junction GaInP/GaAs solar cell, leading to 27.2% efficiency in the single junction device and a one-sun record 32.9% efficiency in the tandem device. Good carrier collection and low non-radiative recombination are observed in the cells with up to 80 QWs. The GaAs cells employ a rear-heterojunction architecture to boost the open-circuit voltage to over 1.04 V in the quantum well device, despite the large number of QWs.

14 SOLAR ENERGY↗
DOE OSTI2020

Reinterpreting π-stacking

The nature of π–π interactions has long been debated. The term “π-stacking” is considered by some to be a misnomer, in part because overlapping π-electron densities are thought to incur steric repulsion, and the physical origins of the widely-encountered “slip-stacked” motif have variously been attributed to either sterics or electrostatics, in competition with dispersion. Here, we use quantum-mechanical energy decomposition analysis to investigate π–π interactions in supramolecular complexes of polycyclic aromatic hydrocarbons, ranging in size up to realistic models of graphene, and for comparison we perform the same analysis on stacked complexes of polycyclic saturated hydrocarbons, which are cyclohexane-based analogues of graphane. Our results help to explain the short-range structure of liquid hydrocarbons that is inferred from neutron scattering, trends in melting-point data, the interlayer separation of graphene sheets, and finally band gaps and observation of molecular plasmons in graphene nanoribbons. Analysis of intermolecular forces demonstrates that aromatic π–π interactions constitute a unique and fundamentally quantum-mechanical form of non-bonded interaction. Not only do stacked π–π architectures enhance dispersion, but quadrupolar electrostatic interactions that may be repulsive at long range are rendered attractive at the intermolecular distances that characterize π-stacking, as a result of charge penetration effects. The planar geometries of aromatic sp 2 carbon networks lead to attractive interactions that are “served up on a molecular pizza peel”, and adoption of slip-stacked geometries minimizes steric (rather than electrostatic) repulsion. The slip-stacked motif therefore emerges not as a defect induced by electrostatic repulsion but rather as a natural outcome of a conformational landscape that is dominated by van der Waals interactions (dispersion plus Pauli repulsion), and is therefore fundamentally quantum-mechanical in its origins. This reinterpretation of the forces responsible for π-stacking has important implications for the manner in which non-bonded interactions are modeled using classical force fields, and for rationalizing the prevalence of the slip-stacked π–π motif in protein crystal structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A fast, dense Chebyshev solver for electronic structure on GPUs

Matrix diagonalization is almost always involved in computing the density matrix needed in quantum chemistry calculations. In the case of modest matrix sizes (≲4000), performance of traditional dense diagonalization algorithms on modern GPUs is underwhelming compared to the peak performance of these devices. This motivates the exploration of alternative algorithms better suited to these types of architectures. We newly derive, and present in detail, an existing Chebyshev expansion algorithm whose number of required matrix multiplications scales with the square root of the number of terms in the expansion. Focusing on dense matrices of modest size, our implementation on GPUs results in large speed ups when compared to diagonalization. Additionally, we improve upon this existing method by capitalizing on the inherent task parallelism and concurrency in the algorithm. Furthermore, this improvement is implemented on GPUs by using CUDA and HIP streams via the MAGMA library and leads to a significant speed up over the serial-only approach for smaller (≲1000) matrix sizes. Finally, we apply our technique to a model system with a high density of states around the Fermi level, which typically presents significant challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

2D Semiconductor Nanosheets Supported on Colloidal Quantum Cubes

Two-dimensional (2D) colloidal nanocrystals bridge the physics of epitaxial quantum wells with the synthetic scalability of solution-processed nanomaterials, making them an attractive platform for future LEDs, lasers, and photodetectors. However, their strongly anisotropic 2D morphology complicates the formation of close-packed films required for electrically interfaced devices. Here, we address this limitation by introducing semiconductor quantum cubes (QCs) in which 2D CdSe nanosheets are conformally grown on six faces of CdS cubic scaffolds. This architecture preserves excitonic physics of quasi-2D nanosheets while leveraging the mechanical rigidity and self-assembly of nanocubes, enabling close-packed films with improved electronic coupling and electrical conductivity. By extending CdS/CdSe QCs into a CdS/CdSe/CdS core/shell/shell structure, we realize 2D quantum wells that exhibit unusual multiexciton behavior. In particular, CdS/CdSe/CdS QCs show repulsive exciton–exciton interactions, which decouple CdSe facets into quasi-independent emitters. This leads to the suppressed Auger recombination, long multiexciton lifetimes, and broadband optical gain. The demonstrated combination of unusual multiexciton photophysics and favorable film self-assembly characteristics in QCs presents opportunities for solution-processed optoelectronic devices.

perovskites↗
DOE OSTI2021

Elucidating the local atomic and electronic structure of amorphous oxidized superconducting niobium films

Qubits made from superconducting materials are a mature platform for quantum information science application, such as quantum computing. However, material-based losses are now a limiting factor in reaching the coherence times needed for applications. In particular, knowledge of the atomistic structure and properties of the circuit materials is needed to identify, understand, and mitigate material-based decoherence channels. Here, we characterize the atomic structure of the native oxide film formed on Nb resonators by comparing fluctuation electron microscopy experiments to density functional theory calculations, finding that an amorphous layer is consistent with an Nb 2 O 5 stoichiometry. Comparing x-ray absorption measurements at the Oxygen K edge with first-principles calculations, we find evidence of d-type magnetic impurities in our sample, known to cause impedance in proximal superconductors. This work identifies the structural and chemical composition of the oxide layer grown on Nb superconductors and shows that soft x-ray absorption can fingerprint magnetic impurities in these superconducting systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

El Agente: An autonomous agent for quantum chemistry

Computational chemistry tools are widely used to study the behavior of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.

agentic systems↗
DOE OSTI2025

Exact block encoding of imaginary time evolution with universal quantum neural networks

We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the nonunitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave-function is closed under the action of the imaginary time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the yields the ground state of the system. The number of qubits grows linearly with the number of interactions and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via midcircuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and number of interactions, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the number of interactions, and circuit depth grows linearly with imaginary time only. Every midcircuit measurement has a postselection success probability, and the overall success probability is equal to the product of the probabilities of the midcircuit measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Investigation on the AgPt and AgPd hybrid alloy nanoparticles (HANPs) for the hybrid MoS 2 /ZnO/HANP UV photodetector application

We report hybrid photodetectors made of two or more nanomaterial components can offer the high potential for advanced photodetection applications with enhanced light absorption, improved efficiency and structural tunability. A hybrid UV photodetector (PD) configuration incorporating the plasmonic hybrid alloy nanoparticles (HANP), ZnO quantum dots (QDs) and MoS 2 nanoflakes, namely MoS2/ZnO/HANP PD, is demonstrated. The optimized MoS 2 /ZnO/HANP PD exhibits a high photocurrent of 5.66 mA at 0.34 mW/mm 2 under 385 nm illumination with two orders of improvement from the bare ZnO, which is one of the highest photocurrents by the ZnO-based PDs. It also demonstrates high figure-of-merits with the responsivity of 14,523 mA/W, detectivity of 8.13 x 10 11 jones, and external quantum efficiency (EQE) of 4,680 %, outperforming most of the ZnO-based hybrid architectures. The enhanced photoresponse is primarily attributed to the significantly enhanced hot electron generation by the AgPd HANPs and photocarrier collection through the ZnO QD layer. MoS 2 nanoflakes also provide additional photon absorption sites and boost the photo-carrier transfer process. HANPs are grown by adapting a dual-step solid-state dewetting process (SSD) approach and the superiority of AgPd HANPs is confirmed by systematic investigation, FDTD simulation and comparison of AgPt and AgPd HANPs.

MoS2↗
DOE OSTI2025

Repositioning Quantum Cellular Automata for Dependable Quantum-Classical Systems

Quantum Cellular Automata (QCA) provides a structured model of distributed quantum computation with inherent locality and regularity properties that are suited to dependable execution. However, QCA remain largely absent from discussions on reproducibility, fault management, and orchestration in heterogeneous quantum-classical systems. We propose a dual-axis framework that situates QCA within both computation and physical realizability, revealing regions where robust, scalable, and hardware-constrained quantum dynamics may reside. By revisiting prior results through the lens of reproducibility and architecture resilience, we suggest that QCA offers a potential substrate for benchmarking and system-level co-design.

Stapleton, Nicholas [ORNL] (ORCID:0000000335305325↗