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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 325 records · Page 18

Solving the nuclear pairing model with neural network quantum states

In this work, we present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the groundstate wave function. A memory-efficient version of the stochastic reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian. We benchmark this approach against widely used nuclear many-body methods by solving a model used to describe pairing in nuclei for different types of interaction and different values of the interaction strength. Despite its polynomial computational cost, our method outperforms coupled-cluster and provides energies that are in excellent agreement with the numerically-exact full configuration interaction values.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Delocalization Transition of a Disordered Axion Insulator

The axion insulator is a higher-order topological insulator protected by inversion symmetry. We show that, under quenched disorder respecting inversion symmetry on average, the topology of the axion insulator stays robust, and an intermediate metallic phase in which states are delocalized is unavoidable at the transition from an axion insulator to a trivial insulator. We derive this conclusion from general arguments, from classical percolation theory, and from the numerical study of a 3D quantum network model simulating a disordered axion insulator through a layer construction. We find the localization length critical exponent near the delocalization transition to be ν = 1.42 ± 0.12. We further show that this delocalization transition is stable even to weak breaking of the average inversion symmetry, up to a critical strength. Finally, we also quantitatively map our quantum network model to an effective Hamiltonian and we find its low-energy k ∙ p expansion.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗

Generation of entangled-photons by a quantum dot cascade source in polarized cavities: Using cavity resonances to boost signals and preserve the entanglements

Motivated by recent advances in the development of single photon emitters for quantum information sciences, here we design and formulate a quantum cascade model that describes cascade emission by a quantum dot (QD) in a cavity structure while preserving entanglement that stores information needed for single photon emission. The theoretical approach is based on a photonic structure that consists of two orthogonal cavities in which resonance with either the first or second of the two emitted photons is possible, leading to amplification and rerouting of the entangled light. The cavity–QD scheme uses a four-level cascade emitter that involves three levels for each polarization, leading to two spatially entangled photons for each polarization. By solving the Schrodinger equation, we identify the characteristic properties of the system, which can be used in conjunction with optimization techniques to achieve the “best” design relative to a set of prioritized criteria or constraints in our optical system. The theoretical investigations include an analysis of emission spectra in addition to the joint spectral density profile, and the results demonstrate the ability of the cavities to act as frequency filters for the photons that make up the entanglements and to modify entanglement properties. The results provide new opportunities for the experimental design and engineering of on-demand single photon sources.

Chemistry↗

InterQnet: A Heterogeneous Full-Stack Approach to Co-Designing Scalable Quantum Networks

Quantum communications have progressed significantly, moving from a theoretical concept to small-scale experiments to recent metropolitan-scale demonstrations. As the technology matures, it is expected to revolutionize quantum computing in much the same way that classical networks revolutionized classical computing. Quantum communications will also enable breakthroughs in quantum sensing, metrology, and other areas. However, scalability has emerged as a major challenge, particularly in terms of the number and heterogeneity of nodes, the distances between nodes, the diversity of applications, and the scale of user demand. This article describes InterQnet, a multidisciplinary project that advances scalable quantum communications through a comprehensive approach that improves devices, error handling, and network architecture. InterQnet has a two-pronged strategy to address scalability challenges: InterQnet-Achieve focuses on practical realizations of heterogeneous quantum networks by building and then integrating first-generation quantum repeaters with error mitigation schemes and centralized automated network control systems. The resulting system will enable quantum communications between two heterogeneous quantum platforms through a third type of platform operating as a repeater node. InterQnet-Scale focuses on a systems study of architectural choices for scalable quantum networks by developing forward-looking models of quantum network devices, advanced error correction schemes, and entanglement protocols. Here, we report our current progress toward achieving our scalability goals.

Chung, Joaquin [Argonne] (ORCID:0000000173833810)↗

Multilevel Combinatorial Optimization across Quantum Architectures

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Furthermore, hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this article, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. To demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave’s quantum annealer and IBM’s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitude larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution.

97 MATHEMATICS AND COMPUTING↗

Hamiltonian Simulation [Slides]

Given a (qubit) quantum system modeled by a Hamiltonian H, use a quantum computer to obtain time-evolved properties of the system. One way of solving this problem is by actually preparing the evolved quantum state (in some encoded fashion). The goal is to provide a sequence of elementary 1 and 2-qubit gates that would prepare such a state.

97 MATHEMATICS AND COMPUTING↗

Characterization and Optimization of Building Blocks for Specialized Computing Platforms

As noise limits the performance of quantum processors, the ability to characterize this noise and develop methods to overcome it is essential for the future of quantum computing. In this report, we develop a complete set of tools for improving quantum processor performance at the application level, including low-level physical models of quantum gates, a numerically efficient method of producing process matrices that span a wide range of model parameters, and full-channel quantum simulations. We then provide a few examples of how to use these tools to study the effects of noise on quantum circuits.

42 ENGINEERING↗

Emergent quantum mechanics at the boundary of a local classical lattice model

We formulate a model in which quantum mechanics emerges from classical mechanics. Given a local Hamiltonian H acting on n qubits, we define a local classical model with an additional spatial dimension whose boundary dynamics is approximately—but to arbitrary precision—described by Schrödinger's equation and H. The bulk consists of a lattice of classical bits that propagate towards the boundary through a circuit of stochastic matrices. The bits reaching the boundary are governed by a probability distribution whose deviation from the uniform distribution can be interpreted as the quantum-mechanical wave function. Bell nonlocality is achieved because information can move through the bulk much faster than the boundary speed of light. Finally, we analytically estimate how much the model deviates from quantum mechanics, and we validate these estimates using computer simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Quantum dynamics of positron-hydrogen scattering and three-body bound state formation with an assisting laser field: predictions of a reduced-dimensionality model

Abstract We investigate the quantum dynamics of target excitation and positronium formation in the positron-hydrogen atom scattering without and with an external assisting laser field within a reduced-dimensional quantum model. Strong interference fringes between the incident and reflected positron wave packets are observed in the reaction region. We further investigate the critical behavior of transition probabilities near the channel-opening thresholds for hydrogen excitation and positronium formation and find a strong competition between channels with similar threshold energies, but different parities. The transmission ratios of the incident positron in different reaction channels are calculated, and it is shown that only positronium formation in the ground state prefers forward scattering. Our simulation of the positron-hydrogen scattering with an assisting laser field indicates that the three-particle bound states can be formed during the collisions due to the photon emission induced by the external laser field.

Optics↗

Quantum efficiency of InGaN–GaN multi-quantum well solar cells: Experimental characterization and modeling

InGaN-based multi-quantum well (MQW) solar cells are promising devices for photovoltaics (e.g., for tandem solar cells and concentrator systems), space applications, and wireless power transfer. In order to improve the efficiency of these devices, the factors limiting their efficiency and stability must be investigated in detail. Due to the complexity of a MQW structure, compared with a simple pn junction, modeling the spectral response of these solar cells is not straightforward, and ad hoc methodologies must be implemented. In this paper, we propose a model, based on material parameters and closed-formula equations, that describes the shape of the quantum efficiency of InGaN/GaN MQW solar cells, by taking into account the layer thickness, the temperature dependence of the absorption coefficient, and quantum confinement effects. We demonstrate (i) that the proposed model can effectively reproduce the spectral response of the cells; in addition, (ii) we prove that the bulk p-GaN layer absorbs radiation, but the carriers photogenerated in this region do not significantly contribute to device current. Finally, we show that (iii) by increasing the temperature, there is a redshift of the absorption edge due to bandgap narrowing, which can be described by Varshni law and is taken into account by the model, and a lowering in the extraction efficiency due to the increase in recombination (mostly Shockley–Read–Hall) inside the quantum wells, which is also visible by decreasing light intensity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling

The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.

machine learning↗

Application of the variational autoencoder to detect the critical points of the anisotropic Ising model

We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of the system, the critical points can be located exactly for the entire range of anisotropic coupling. This presents an excellent test bed for the validity of using a variational autoencoder to characterize an anisotropic classical model. Furthermore, we reproduce the phase diagram for a wide range of anisotropic couplings and temperatures via a variational autoencoder without the explicit construction of an order parameter. Considering that the partition function of ($d$ + 1)-dimensional anisotropic models can be mapped to that of the $d$-dimensional quantum spin models, the present study provides numerical evidence that a variational autoencoder can be applied to analyze quantum systems via the quantum Monte Carlo method.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

An Acoustic Charge Transport Imager for High Definition Television Applications: Reliability Modeling and Parametric Yield Prediction of GaAs Multiple Quantum Well Avalanche Photodiodes

Reliability modeling and parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs), which are of interest as an ultra-low noise image capture mechanism for high definition systems, have been investigated. First, the effect of various doping methods on the reliability of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiode (APD) structures fabricated by molecular beam epitaxy is investigated. Reliability is examined by accelerated life tests by monitoring dark current and breakdown voltage. Median device lifetime and the activation energy of the degradation mechanism are computed for undoped, doped-barrier, and doped-well APD structures. Lifetimes for each device structure are examined via a statistically designed experiment. Analysis of variance shows that dark-current is affected primarily by device diameter, temperature and stressing time, and breakdown voltage depends on the diameter, stressing time and APD type. It is concluded that the undoped APD has the highest reliability, followed by the doped well and doped barrier devices, respectively. To determine the source of the degradation mechanism for each device structure, failure analysis using the electron-beam induced current method is performed. This analysis reveals some degree of device degradation caused by ionic impurities in the passivation layer, and energy-dispersive spectrometry subsequently verified the presence of ionic sodium as the primary contaminant. However, since all device structures are similarly passivated, sodium contamination alone does not account for the observed variation between the differently doped APDs. This effect is explained by the dopant migration during stressing, which is verified by free carrier concentration measurements using the capacitance-voltage technique.

Hunt, W. D.↗

Modeling and Simulation of Semiconductor Quantum Well Structures and Lasers

In this talk I will cover two aspects of modeling and simulation efforts at NASA Ames Research Center. In the quantum well structure simulation, we typically start from the quantum mechanical calculation of the quantum well structures for the confined/and unconfined eigen states and functions. A bandstructure calculation of the k*p type is then performed for the confined valence states. This information is then used to computer the optical gain and refractive index of the quantum well structures by solving the linearized multiband semiconductor Bloch equations with the many-body interactions included. In our laser simulation, we typically solve the envelope equations for the laser field in space-time domain, coupled with a reduced set of material equations using the microscopic calculation of the first step. Finally I will show some examples of both aspects of simulation and modeling.

Ning, Cun-Zheng↗

Steady-state properties of multi-orbital systems using quantum Monte Carlo

A precise dynamical characterization of quantum impurity models with multiple interacting orbitals is challenging. In quantum Monte Carlo methods, this is embodied by sign problems. A dynamical sign problem makes it exponentially difficult to simulate long times. A multi-orbital sign problem generally results in a prohibitive computational cost for systems with multiple impurity degrees of freedom even in static equilibrium calculations. Here, we present a numerically exact inchworm method that simultaneously alleviates both sign problems, enabling simulation of multi-orbital systems directly in the equilibrium or nonequilibrium steady-state. The method combines ideas from the recently developed steady-state inchworm Monte Carlo framework [Erpenbeck et al., Phys. Rev. Lett. 130, 186301 (2023)] with other ideas from the equilibrium multi-orbital inchworm algorithm [Eidelstein et al., Phys. Rev. Lett. 124, 206405 (2020)]. We verify our method by comparison with analytical limits and numerical results from previous methods.

Chemistry↗