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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantum Machine Learning in High Energy Physics

Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak↗
DOE OSTI2022

Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Particle Track Classification Using Quantum Associative Memory (Final Technical Report)

This project explored the use of quantum-assisted algorithms for pattern matching in sub-atomic physics experiments. Pattern matching algorithms are commonly employed to prune data of random noise and to help discriminate between signals generated by particle tracks of interest and signals generated by background events. The quantum-assisted algorithms explored in this project were based on an Ising formulation of quantum associative model (QAMM) recall and quantum content-addressable memory (QCAM) recall. The recall is performed by comparing a probe pattern with those stored in a library of patterns encoded in the QAMM/QCAM model. The classification accuracy of QAMM and QCAM recall was determined as a function of detector resolution, noise, and efficiency and pattern density, where pattern density is defined as the ratio of the number of reference signal patterns encoded in the library to each pattern’s length. We found that QAMM achieved high classification accuracy when applied to datasets with low pattern density. QCAM achieved high classification accuracy for datasets with high pattern density and was found to be more robust to detector noise. The project methodology and results are described in detail in our arXiv preprint (arXiv:2011.11848) . This project was conducted by scientists at the Johns Hopkins University Applied Physics Laboratory and Oak Ridge National Laboratory from August 2018 to August 2020 and was supported by DOE grant DE-SC0019497.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Non-Equilibrium Effects in Quantum Magnets

While most often the state of a material will tend toward an equilibrium determined by its environment, there are many cases of scientific and technological interest where materials are manipulated to be or are found in non-equilibrium configurations. For example, data can be stored in hard drives by deliberately altering the magnetic orientation in a material to store information in non-equilibrium pattern. In this project, the main goals were studies of non-equilibrium properties of quantum magnets using neutron scattering as the primary experimental method. Neutron scattering allows characterization of magnetic correlation lengths sensitive to the presence of defects. It can also be used to distinguish equilibrium from non-equilibrium states via energy transfer rates. Typical bulk state magnetization relaxation times are too short to perform many neutron scattering measurements of interest. To enable the study of non-equilibrium conditions, materials with longer magnetic relaxation times were targeted. CoNb 2 O 6 was used in two experiments related to non-equilibrium physics. In the first, evidence for defects created via the Kibble-Zurek mechanism (KZM) was sought by quenching across a magnetic field-dependent phase transition. Somewhat unexpectedly, clear evidence for KZM-induced defects was absent. Additional measurements of CoNb 2 O 6 were made to better characterize its crystal field and other properties to provide a better theoretical understanding to enable more effective non-equilibrium physics measurements. In another project, LiHo 0.45 Y 0.55 F 4 was used to compare a quantum annealing protocol to a thermal annealing one since magnetic fields can be used to control the thermal fluctuations in LiHo 0.45 Y 0.55 F 4 . In addition, a new pulsed magnet power supply and new techniques were developed suitable for neutron scattering experimental environments to enable faster magnetic field changes for producing non-equilibrium conditions. The power supply developed for this project has wider technological applications in addition to faster magnetic field ramps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Improving the short-wave infrared response of strained GeSn/Ge multiple quantum wells by rapid thermal annealing

In this work, the evolution of structural, optical and optoelectronic properties of coherently strained Ge 0.883 Sn 0.117 /Ge multiple quantum wells (MQWs) grown by molecular beam epitaxy under rapid thermal annealing (RTA) is systematically investigated. The MQW structure remains fully-strained state with RTA at 400 °C or below and disrupts at higher annealing temperatures due to Sn segregation and interdiffusion of Ge and Sn atoms. The GeSn well layers exhibit the strongest absorption in 2.0–2.4 μm after annealing at 400 °C and become transparent above 1.8 μm after RTA at 600 °C or beyond due to serve Sn segregation. Owing to improved crystal quality after RTA at 400 °C, the dark current of the fabricated metal-semiconductor-metal photodetector is effectively lowered by more than two times. Additionally, the responsivities at 1.55 and 2.0 μm are improved by 4.15 and 3.78 folds, respectively, compared to those of the as-grown sample. Here, the results can be an insightful guidance for the development of high-performance short-wave infrared photonic devices based on Sn-containing group-IV low-dimensional structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ancilla-entangling Floquet kicks for accelerating quantum algorithms

Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the systems scale up due to the shrinkage of the excitation gap and thus places an annealing rate bottleneck for high success probability. Here, in this study, we accelerate quantum simulation using digital multiqubit gates that entangle primary system qubits with ancillary qubits. The practical benefits originate from tuning the ancillary gauge degrees of freedom to enhance the quantum algorithm's original functionality in the system registry. For simple but nontrivial short-ranged, infinite long-ranged transverse-field Ising models, and the hydrogen molecule model after qubit encoding, we show improvement in the time to solution by one hundred percent but with higher accuracy through exact state-vector numerical simulation in a digital-analog setting. The findings are further supported by time-averaged Hamiltonian theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

First Soft X-ray Quantum Efficiency Measurements on Microwave Annealed Thin-Entrance Window Sensors

Free electron lasers, such as SLAC’s LCLS-II, will provide unique scientific imaging opportunities. In order to fully utilize these facilities, we need to develop detectors with shallow entrance windows that will enable detection of soft x-rays from 250eV-1.5KeV. In addition, there are other light sources such as the FERMI, EuXFEL, and FLASH XFELs, as well as numerous synchrotrons such as the ALS, where soft x-ray science would benefit from detectors with higher quantum efficiency in the 20 eV to 1.5KeV range.A new microwave annealing technology provides an efficient way to achieve shallow entrance windows in fully depleted high-resistivity silicon sensors. Previously, SRP and SIMS measurements were used to characterize the shallow dopant profile, and sensors with the new entrance window process were deployed successfully to measure an Fe-55 x-ray spectrum. For the first time, we present quantum efficiency measurement for soft x-rays. Furthermore, the resulting estimate of the entrance window is 20nm for an arsenic implanted entrance window and 55nm for a boron implanted entrance window.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Enhancement of 2D topological semimetal transport properties by current annealing

Observation of intrinsic quantum transport properties of two-dimensional (2D) topological semimetals can be challenging due to suppression of high mobility caused by extrinsic factors introduced during fabrication. We demonstrate current annealing as a method to substantially improve electronic transport properties of 2D topological semimetal flakes. Contact resistance and resistivity were improved by factors up to 2 × 10 6 and 2 × 10 4 , respectively, in devices based on exfoliated flakes of two topological semimetals, ZrSiSe and BaMnSb 2 . Using this method, carrier mobility in ZrSiSe was improved by a factor of 3800, resulting in observation of record-high mobility for exfoliated ZrSiSe. Quantum oscillations in annealed ZrSiSe appeared at magnetic fields as low as 5 T, and magnetoresistance increased by a factor of 10 4 . We argue that a thermal process underlies this improvement. Finally, Raman spectroscopy and analysis of quantum oscillations in ZrSiSe indicate that the phonon modes and Fermi surface area are unchanged by current annealing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Enhancement in electro-optic performance of InAlGaAs/GaAs quantum dot lasers by ex situ thermal annealing

Here, this Letter reports the growth, fabrication, and characterization of molecular beam epitaxy (MBE)-grown quaternary InAlGaAs/GaAs quantum dot (QD) lasers emitting at sub-900 nm. The presence of Al in QD-based active regions acts as the origin of defects and non-radiative recombination centers. Applying optimized thermal annealing annihilates the defects in p-i-n diodes, thus lowering the reverse leakage current by six orders of magnitude compared to as-grown devices. A systematic improvement in the optical properties of the devices is also observed in the laser devices with increasing annealing time. At an annealing temperature of 700°C for 180 s, Fabry–Pérot lasers exhibit a lower pulsed threshold current density at infinite length of 570 A/cm 2 .

42 ENGINEERING↗
DOE OSTI2022

3-regular three-XORSAT planted solutions benchmark of classical and quantum heuristic optimizers

With current semiconductor technology reaching its physical limits, special-purpose hardware has emerged as an option to tackle specific computing-intensive challenges. Optimization in the form of solving quadratic unconstrained binary optimization problems, or equivalently Ising spin glasses, has been the focus of several new dedicated hardware platforms. These platforms come in many different flavors, from highly-efficient hardware implementations on digital-logic of established algorithms to proposals of analog hardware implementing new algorithms. In this work, we use a mapping of a specific class of linear equations whose solutions can be found efficiently, to a hard constraint satisfaction problem (three-regular three-XORSAT, or an Ising spin glass) with a 'golf-course' shaped energy landscape, to benchmark several of these different approaches. We perform a scaling and prefactor analysis of the performance of Fujitsu's digital annealer unit (DAU), the D-Wave advantage quantum annealer, a virtual MemComputing machine, Toshiba's simulated bifurcation machine (SBM), the SATonGPU algorithm from Bernaschi et al, and our implementation of parallel tempering. We identify the SATonGPU and DAU as currently having the smallest scaling exponent for this benchmark, with SATonGPU having a small scaling advantage and in addition having by far the smallest prefactor thanks to its use of massive parallelism. Furthermore, our work provides an objective assessment and a snapshot of the promise and limitations of dedicated optimization hardware relative to a particular class of optimization problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Transitions, Dynamics, and Driven States in Quantum Magnets (Final Report Revised)

Transitions into unusual electronic, optical, and magnetic states at the absolute zero of temperature display special properties that teach us about fundamental quantum mechanics and also underlie important technologies. These so-called quantum phase transitions intertwine the static and dynamical responses of the materials changing state. They are extremely sensitive to the effects of disorder and etch in high relief the role of quantum fluctuations. There are ample questions remaining about the character of such transitions and the nature of the competing quantum states. There are also opportunities to drive quantum materials out of equilibrium, with the possibility of new types of correlated and coherent order, and new ways to access the dynamical evolution of ground and excited states. We have investigated how the order develops in time and space, and the nature of the final configurations. We are able to compare and contrast classical and quantum excitations in our experimental system of choice, and thereby can trace the relative speed by which the system settles into its lowest-energy ground state. This comparison of quantum and classical annealing protocols lies at the heart of strategies for harnessing the power of quantum computers dedicated to optimization problems. A combination of magnetic susceptibility measurements at finite frequency where the magnetic spins are queried by an external ac field, dc magnetometry measurements that characterize the macroscopic strength of magnetic order and its resistance to change, noise measurements that reveal the microscopic fluctuations of the spins as they prepare to change state, and microwave spectroscopy to look at the response of both the electronic and nuclear degrees of freedom, have been performed as functions of temperature, magnetic field, frequency, excitation amplitude, magnetic spin concentration, and disorder. This combination of probes addresses issues of stability over time, overlap between spin states, the ability of the spins to respond independently or coherently, and the promise of control on the nanometer length scale. The research reported here provides insights into fundamental quantum physics. It also addresses the question of how best to use complex systems, such as magnetic solids, to stably process quantum information.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Quantum-Cutting Luminescent Coatings for High-Efficiency, Low-Cost Solar Cells (Final Technical Report)

The objective of the project is to optimize the deposition and post-deposition annealing conditions of quantum-cutting materials to enable coatings with high photoluminescence quantum yields and optimal absorption onset. These coatings will be integrated with commercial solar photovoltaic cells to enable improved power conversion efficiency, and the quantum-cutting cell stability will be ensured using industry-standard packaging methods.

14 SOLAR ENERGY↗
DOE OSTI2025

A Review of Quantum Computing Technologies in Power System Optimization

As modern power grids increasingly integrate variable renewable generation, distributed energy resources, and energy storage systems, classical optimization techniques are facing unprecedented challenges. This review examines the emerging application of quantum computing to overcome these challenges in power system optimization, including optimal power flow (OPF), unit commitment (UC), economic dispatch (ED), and intelligent switching and topology optimization (IS-TO). Recent research has introduced various quantum methodologies—such as gate-based, annealing-based, variational algorithms, and quantum-inspired algorithms—to address the combinatorial complexity inherent in grid reconfiguration and energy management. The review summaries the quantum algorithms, quantum devices and the power system test cases, highlighting hybrid quantum–classical strategies that leverage the complementary strengths of both paradigms. Some quantum advantages have been observed, including theoretical speedup, accurate simulation results, scalable qubit usage, efficient QUBO mapping. In particular, the review emphasizes the importance of integrating quantum optimization techniques with classical control frameworks, these hybrid approaches demonstrate the potential to improve real-time grid management and operational reliability. A significant portion of the analysis is devoted to the practical limitations of current quantum devices. Present-day quantum hardware, operating in the noisy intermediate-scale quantum (NISQ) era, remains highly sensitive to noise and limited in qubit connectivity, which constrains the scale and accuracy of implemented algorithms. The review delves into specific challenges such as the need for qubit-efficient encoding techniques and error mitigation strategies that are critical for handling real-world grid optimization problems. In addition, the work draws attention to the performance discrepancies between theoretical quantum speedups and experimental validations, underscoring the importance of rigorous benchmark studies using representative power grid test cases. In summary, this review highlights both the promise and limitations of quantum computing for power system optimization. It provides a comprehensive overview of the state-of-the-art technologies, categorizes recent advancements in algorithm design, and discusses practical considerations for implementation, and serves as an informative resource on current research. Future research directions include developing robust hybrid frameworks, advancing qubit-efficient formulations, and scaling up experimental demonstrations to confirm the theoretical advantages of quantum methods in large-scale power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Quantum optimization of maximum independent set using Rydberg atom arrays

Realizing quantum speedup for practically relevant, computationally hard problems is a central challenge in quantum information science. Using Rydberg atom arrays with up to 289 qubits in two spatial dimensions, we experimentally investigate quantum algorithms for solving the maximum independent set problem. We use a hardware-efficient encoding associated with Rydberg blockade, realize closed-loop optimization to test several variational algorithms, and subsequently apply them to systematically explore a class of graphs with programmable connectivity. We find that the problem hardness is controlled by the solution degeneracy and number of local minima, and we experimentally benchmark the quantum algorithm’s performance against classical simulated annealing. On the hardest graphs, we observe a superlinear quantum speedup in finding exact solutions in the deep circuit regime and analyze its origins.

Science & Technology - Other Topics↗
DOE OSTI2022

Effects of laser-annealing on fixed-frequency superconducting qubits

As superconducting quantum processors increase in complexity, techniques to overcome constraints on frequency crowding are needed. The recently developed method of laser-annealing provides an effective post-fabrication method to adjust the frequency of superconducting qubits. In this work, we present an automated laser-annealing apparatus based on conventional microscopy components and demonstrate preservation of highly coherent transmons. In addition, we perform noise spectroscopy to investigate the change in defect features, in particular, two-level system defects, after laser-annealing. Finally, we present a local heating model as well as demonstrate aging stability for laser-annealing on the wafer scale. Our work constitutes an important step toward both understanding the underlying physical mechanism and scaling up laser-annealing of superconducting qubits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.

42 ENGINEERING↗