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All sources NASA NTRS DOE OSTI

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↗

DOE OSTI2023

Toward Quantum Chemical Free Energy Simulations of Platinum Nanoparticles on Titania Support

Platinum nanoparticles (Pt-NPs) supported on titania surfaces are costly but indispensable heterogeneous catalysts because of their highly effective and selective catalytic properties. Therefore, it is vital to understand their physicochemical processes during catalysis to optimize their use and to further develop better catalysts. However, simulating these dynamic processes is challenging due to the need for a reliable quantum chemical method to describe chemical bond breaking and bond formation during the processes but, at the same time, fast enough to sample a large number of configurations required to compute the corresponding free energy surfaces. Density functional theory (DFT) is often used to explore Pt-NPs; nonetheless, it is usually limited to some minimum-energy reaction pathways on static potential energy surfaces because of its high computational cost. In this work, we report a combination of the density functional tight binding (DFTB) method as a fast but reliable approximation to DFT, the steered molecular dynamics (SMD) technique, and the Jarzynski equality to construct free energy surfaces of the temperature-dependent diffusion and growth of platinum particles on a titania surface. In particular, we present the parametrization for Pt-X (X = Pt, Ti, or O) interactions in the framework of the second-order DFTB method, using a previous parametrization for titania as a basis. The optimized parameter set was used to simulate the surface diffusion of a single platinum atom (Pt 1 ) and the growth of Pt 6 from Pt 5 and Pt 1 on the rutile (110) surface at three different temperatures (T = 400, 600, 800 K). The free energy profile was constructed by using over a hundred SMD trajectories for each process. We found that increasing the temperature has a minimal effect on the formation free energy; nevertheless, it significantly reduces the free energy barrier of Pt atom migration on the TiO 2 surface and the transition state (TS) of its deposition. In a concluding remark, the methodology opens the pathway to quantum chemical free energy simulations of Pt-NPs’ temperature-dependent growth and other transformation processes on the titania support.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Assessing VQLS for Fluid Dynamics on a Hybrid Quantum-HPC Stack

Recent advances in quantum linear solvers offer a promising direction for accelerating extreme scientific computations such as fluid dynamics. However, the deep and complex circuits required by many quantum algorithms limit their practical use on current quantum hardware. The Variational Quantum Linear Solver (VQLS) presents a viable alternative for near-term quantum devices (NISQ), and initial efforts have explored its application to select fluid dynamics problems. In this work, we evaluate the use of VQLS for canonical fluid dynamics problems, aiming to identify pathways for generalizing its implementation across a broader class of systems. We analyze the impact of various circuit ansatz and classical optimizers on solution quality and convergence behavior. Furthermore, we assess the algorithm's feasibility within a hybrid quantum–high-performance computing (HPC) framework by porting it to QFw, a state-of-the-art quantum-HPC software stack. 11This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US DOE under Contract No. DE-AC05-00OR22725.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗

DOE OSTI2026

Correlated Noise Estimation with Quantum Sensor Networks

We address the metrological problem of estimating collective stochastic properties imprinted on a network of quantum sensors. Canonical examples include center-of-mass quadrature fluctuations in a system of bosonic modes and correlated dephasing in an ensemble of qubits (e.g., spins), bosons, or fermions. We develop a theoretical framework to determine the limits of correlated (weak) noise estimation with quantum sensor networks and reveal the requirements for entanglement advantage. Notably, an advantage emerges from the synergistic interplay between quantum correlations of the sensors and “classical” correlations of the noises. Here, we determine optimal entangled probe states and identify a sensing protocol—reminiscent of a many-body echo—that achieves the fundamental limits of measurement sensitivity for a broad class of problems, unveiling a route toward entanglement-enhanced metrology of correlated many-body phenomena.

Quantum metrology↗

NASA NTRSDate not supplied

Microcalorimeter Absorber Optimization for 0.2 to 12 keV X-Rays

The Advanced Telescope for High ENergy Astrophysics (ATHENA) mission requires high quantum efficiency (QE) x-ray absorption, >90.6% at 7 keV and low specific heat capacity, 0.731 pJ/K. The designed ATHENA x-ray absorbers are cantilevered square tiles (pitch of 317 microns) of 1.05 μm thick Au and 5.51 μm thick Bi electroplated films supported by stems that connect the absorber to the detector below. We discuss some of the methods used to produce x-ray absorbers meeting these specifications for ATHENA. To tune the thermal conductance of the device and the effect on the normal-to superconducting transition shape, the stems need to be small diameter and can have a weak bottle-neck connection to the substrate. A funnel shape of the stem using a proximity exposed photoresist mold has been developed to improve the strength of the connections. Further requirements on the absorbers include low levels of fine particulate remaining on the substrate after production and zero shorts between absorbers due to incomplete ion milling or trapped fine particular between absorbers. To optimize for post patterning substrate cleanliness and absorber yield, we have examined several methods of absorber fabrication. Three such methods are 1) an ion mill/wet etch combination, 2) a photoresist mold for electroplating followed by wet or dry etch to remove the seed layer, and 3) an electroplating process with leveling to smooth the surface followed by ion mill to separate the absorbers. The different combinations of wet and dry etches lead to different yields and surface appearance of the absorber sidewalls. We present results on the achieved pixel yields and the energy resolution of pixels made with the various fabrication methods. We discuss the impact of absorber patterning method on performance, uniformity, and yield.

Edward J Wassell↗

DOE OSTI2023

Light‐driven Transformation of Carbon Monoxide into Hydrocarbons using CdS@ZnS : VFe Protein Biohybrids

Enzymatic Fisher-Tropsch (FT) process catalyzed by vanadium (V)-nitrogenase can convert carbon monoxide (CO) to longer-chain hydrocarbons (>C2) under ambient conditions, although this process requires high-cost reducing agent(s) and/or the ATP-dependent reductase as electron and energy sources. Using visible light-activated CdS@ZnS (CZS) core-shell quantum dots (QDs) as alternative reducing equivalent for the catalytic component (VFe protein) of V-nitrogenase, we first report a CZS : VFe biohybrid system that enables effective photo-enzymatic C−C coupling reactions, hydrogenating CO into hydrocarbon fuels (up to C4) that can be hardly achieved with conventional inorganic photocatalysts. Surface ligand engineering optimizes molecular and opto-electronic coupling between QDs and the VFe protein, realizing high efficiency (internal quantum yield >56 %), ATP-independent, photon-to-fuel production, achieving an electron turnover number of >900, that is 72 % compared to the natural ATP-coupled transformation of CO into hydrocarbons by V-nitrogenase. The selectivity of products can be controlled by irradiation conditions, with higher photon flux favoring (longer-chain) hydrocarbon generation. The CZS : VFe biohybrids not only can find applications in industrial CO removal for high-value-added chemical production by using the cheap, renewable solar energy, but also will inspire related research interests in understanding the molecular and electronic processes in photo-biocatalytic systems.

Chemistry↗

DOE OSTI2023

Efficient, Color-Stable, and Long-Lived White Organic Light-Emitting Diodes Utilizing Phosphorescent Molecular Aggregates

Highly efficient and stable single-stack hybrid white organic light-emitting diode (WOLED) devices are developed using two emissive layers: one with amber-colored phosphorescent molecular-aggregate emission from the Pd(II) complex, Pd(II) 7-(3-(pyridine-2-yl-κN)phenoxy-κC)(benzo-κC)([c]benzo[4,5]imidazo-κN)[1,2-a][1,5]naphthyridine, Pd3O8-Py5, and the other with blue fluorescence emission. Here, an optimized device structure achieves high color stability under various current densities, an external quantum efficiency (EQE) of 45.5%, a power efficiency of 97.4 Lm W -1 , and an estimated LT 95 (operational time to 95% of the initial luminance) of 50 744 h at an initial luminance of 1000 cd m -2 .

36 MATERIALS SCIENCE↗

DOE OSTI2023

First principles study of the electronic structure of the Ni 2 MnIn / InAs and Ti 2 MnIn / InSb interfaces

We present a first principles study of the electronic and magnetic properties of epitaxial interfaces between the Heusler compounds, Ti 2 MnIn and Ni 2 MnIn, and the III-V semiconductors, InSb and InAs, respectively. We use density functional theory (DFT) with a machine-learned Hubbard U correction determined by Bayesian optimization. Here, we evaluate these interfaces for prospective applications in Majorana-based quantum computing and spintronics. In both interfaces, states from the Heusler penetrate into the gap of the semiconductor, decaying within a few atomic layers. The magnetic interactions at the interface are weak and local in space and energy. Magnetic moments of less than 0.1 µB are induced in the two atomic layers closest to the interface. The induced spin polarization around the Fermi level of the semiconductor decays within a few atomic layers. The decisive factor for the induced spin polarization around the Fermi level of the semiconductor is the spin polarization around the Fermi level in the Heusler, rather than the overall magnetic moment. As a result, the ferrimagnetic narrow-gap semiconductor Ti 2 MnIn induces a more significant spin polarization in the InSb than the ferromagnetic metal Ni 2 MnIn induces in the InAs. This is explained by the position of the transition metal d states in the Heusler with respect to the Fermi level. Based on our results, these interfaces are unlikely to be useful for Majorana devices but could be of interest for spintronics.

36 MATERIALS SCIENCE↗

DOE OSTI2020

pnnl/QASMBench

A low-level OpenQASM benchmark suite for NISQ evaluation and simulation. Please see our paper for details. n this repository you will find a low-level and light-weighted benchmark suite based on IBM OpenQASM language (see spec). It collects commonly seen quantum algorithms and routines from various domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, etc. QASMBench trades-off between generality and usability, covering the number of qubits ranging from 2 to 60K, and the circuit depth from 4 to 12M

Li, Ang↗

DOE OSTI2020

Physics and applications of quantum dot lasers for silicon photonics

Abstract Photonic integrated circuits (PICs) have enabled numerous high performance, energy efficient, and compact technologies for optical communications, sensing, and metrology. One of the biggest challenges in scaling PICs comes from the parasitic reflections that feed light back into the laser source. These reflections increase noise and may cause laser destabilization. To avoid parasitic reflections, expensive and bulky optical isolators have been placed between the laser and the rest of the PIC leading to large increases in device footprint for on-chip integration schemes and significant increases in packaging complexity and cost for lasers co-packaged with passive PICs. This review article reports new findings on epitaxial quantum dot lasers on silicon and studies both theoretically and experimentally the connection between the material properties and the ultra-low reflection sensitivity that is achieved. Our results show that such quantum dot lasers on silicon exhibit much lower linewidth enhancement factors than any quantum well lasers. Together with the large damping factor, we show that the quantum dot gain medium is fundamentally dependent on dot uniformity, but through careful optimization, even epitaxial lasers on silicon can operate without an optical isolator, which is of paramount importance for the future high-speed silicon photonic systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Data-Driven and Multiscale Modeling of DNA-Templated Dye Aggregates

Dye aggregates are of interest for excitonic applications, including biomedical imaging, organic photovoltaics, and quantum information systems. Dyes with large transition dipole moments (μ) are necessary to optimize coupling within dye aggregates. Extinction coefficients (ε) can be used to determine the μ of dyes, and so dyes with a large ε (>150,000 M –1 cm –1 ) should be engineered or identified. However, dye properties leading to a large ε are not fully understood, and low-throughput methods of dye screening, such as experimental measurements or density functional theory (DFT) calculations, can be time-consuming. In order to screen large datasets of molecules for desirable properties (i.e., large ε and μ), a computational workflow was established using machine learning (ML), DFT, time-dependent (TD-) DFT, and molecular dynamics (MD). ML models were developed through training and validation on a dataset of 8802 dyes using structural features. A Classifier was developed with an accuracy of 97% and a Regressor was constructed with an R2 of above 0.9, comparing between experiment and ML prediction. Using the Regressor, the ε values of over 18,000 dyes were predicted. The top 100 dyes were further screened using DFT and TD-DFT to identify 15 dyes with a μ relative to a reference dye, pentamethine indocyanine dye Cy5. Two benchmark MD simulations were performed on Cy5 and Cy5.5 dimers, and it was found that MD could accurately capture experimental results. The results of this study exhibit that our computational workflow for identifying dyes with a large μ for excitonic applications is effective and can be used as a tool to develop new dyes for excitonic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS2004

Random Matrix Approach to Quantum Adiabatic Evolution Algorithms

We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.

Boulatov, Alexei↗

DOE OSTI2018

Quantum Efficiency of Gallium Nitride–Based Heterostructures with GaInN Quantum Wells

An important parameter of light-emitting heterostructures is their external quantum efficiency. However, another strict requirement for the structures used in fabricating blue and white light-emitting diodes is that the wavelength in the emission spectrum peak and its spread over the entire structure must be 460 ± 5 nm. This is explained primarily by the most frequently used white light-emitting diode design being based on crystals coated with a luminescent layer of a certain composition excited by blue emissions. Deviating from the specified spectral parameters of heterostructures strongly degrades the light and color characteristics of light-emitting diodes. In this work, we solve the problem of optimizing the design and way of growing the active region of an emitting structure consisting of a set of Ga{sub 1 – x}In{sub x}N quantum wells and wider-gap GaN barriers with a certain wavelength in the emission spectrum peak. The variation in the critical thickness of the pseudomorphic layer in the Poisson ratio ranges of 0 to 0.2 for GaAlN and 0 to 0.4 for GaInN is calculated. The emission wavelength is determined by both the bulk Ga{sub 1 – x}In{sub x}N band gap, which depends on the molar fraction of In in quantum wells, and the quantum well thickness in quantum-sized layers. It is found from the obtained dependences that to obtain the required wavelength of 460 nm in the emission spectrum peak, the Ga{sub 1 – x}In{sub x}N layers must be around 10.3% indium and have a quantum well thickness of about 2.5 nm. The effect the In distribution profile in quantum wells has on the external quantum efficiency, the uniformity of the emission wavelength distribution in the spectral peak, and the emission power distribution over the structure is considered. The best results are obtained for a trapezoidal In distribution, since it ensures the narrowest emission wavelength spread in the spectral peak and the most uniform emission power distribution over the structure. Studying the effect the number of quantum wells has on the properties of a heterostructure shows that the maximum external quantum efficiency corresponds to 4 to 5 quantum wells. The highest emission wavelength uniformity in the spectral peak over the structure is obtained at 5 to 7 quantum wells. The optimum number of quantum wells in the active region of the heterostructure is found to be 5.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Implementing two-qubit gates at the quantum speed limit

The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine-learning-inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high fidelity. Thus the method is applicable to a variety of platforms, including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions. We expect our method to offer significant speedups for non-native two-qubit gates that are typically achieved with a long sequence of single-qubit and native two-qubit gates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Photosynthetic biohybrid systems for solar fuels catalysis

Photosynthetic reaction center (RC) proteins are finely tuned molecular systems optimized for solar energy conversion. RCs effectively capture and convert sunlight with near unity quantum efficiency utilizing light-induced directional electron transfer through a series of molecular cofactors embedded within the protein core to generate a long-lived charge separated state with a useable electrochemical potential. Of current interest are new strategies that couple RC chemistry to the direct synthesis of energy-rich compounds. This Feature Article highlights recent work from our lab on RC and RC-inspired hybrid systems that capture the Sun's energy and convert it to chemical energy in the form of H2, a carbon-neutral energy source derived from water. Further, biohybrids made from the Photosystem I (PSI) RC are among the best photocatalytic H2-producing protein hybrids to date. Targeted self-assembly strategies that couple abiotic catalysts to PSI translate to catalyst incorporation at intrinsic PSI sites within thylakoid membranes to achieve complete solar water-splitting systems. RC-inspired biohybrids interface synthetic photosensitizers and molecular catalysts with small proteins to create photocatalytic systems and enable the spectroscopic discernment of the structural features and electron transfer processes that underpin solar-driven proton reduction. In total, these studies showcase the incredible scientific opportunities photosynthetic biohybrid research provides for harnessing the optimal qualities of both artificial and natural photosynthetic systems and developing materials that capture, convert, and store solar energy as a fuel.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗

DOE OSTI2020

Photoinduced charge transfer in transition metal dichalcogenide heterojunctions – towards next generation energy technologies

Increasing interest in using two-dimensional transition metal dichalcogenides (2D TMDCs) in optical energy conversion technologies creates a demand for improving the yields and lifetimes of photogenerated charge carriers. Despite inherently fast photocarrier decay in neat 2D TMDCs, the unique photophysics in these quantum-confined systems motivates continued effort to control the evolution of photoexcited states and create functional devices. Here, an intriguing strategy to accomplish this goal is to employ TMDCs in heterojunctions with appropriate semiconductors, where energy level offsets drive photoinduced charge transfer (PCT) across material interfaces. PCT in TMDC-based systems can be optimized for many different applications, such as driving free carriers to photocatalytic sites for redox reactions like water splitting, extracting charge to perform work in photovoltaics or photodetectors, and manipulating the spin and momentum valley electronic degrees of freedom for quantum computing systems. Here, we review recent strides in optimizing PCT for such applications through greater fundamental understanding of the photophysics that occurs at TMDC/semiconductor interfaces. After giving an overview of isolated TMDC properties, synthetic methods, and the basics of PCT, we discuss TMDCs in heterojunctions with several classes of materials, including other TMDCs, small molecule semiconductors, polymers, single-walled carbon nanotubes, quantum dots, perovskites, and electrolytes. In addition to highlighting the unique benefits of each materials category, we also identify parallels across common themes, such as the roles of charge-transfer states, spin, electronic coupling, delocalization, interfacial atomic morphology, and the precise design of energy landscapes to direct charge and energy motion. We hope to capture a broad range of the valuable work in this fast-paced field to inspire new research directions for employing PCT in targeted TMDC-based systems.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2018

Al3+ doping into TiO2 photoanodes improved the performances of amine anchored CdS quantum dot sensitized solar cells

Highlights: • Several Al{sup 3+}-doped TiO{sub 2} nanocomposites were applied as photoanodes of CdS QDSSCs. • The cells were sensitized using ethylglycinate hydrochloride (amine)-capped CdS QDs. • The highest efficiency was measured for the cell containing 0.3% Al and 8.25% amine. • The electron life time was the most improved in the optimized cell (τ{sub e} = 37.58 ms). • The optimized cell showed η = 2.40%, V{sub OC} = 0.584 V, FF = 0.361 and J{sub SC} = 11.38 mA cm{sup −2}. - Abstract: Some quantum dot-sensitized solar cells (QDSSCs) were fabricated by doping Al{sup 3+} ions into the TiO{sub 2} and addition of amine (ethylglycinate hydrochloride) to the CdS solution in order to improve their efficiency. Results indicated that the photovoltaic parameters of the optimized cell were boosted compared with those of the reference cell which was free of Al{sup 3+} QDs and amine indicating 68, 73 and 10.40% improvements in efficiency, current density and open-circuit voltage, respectively. The electrochemical impedance spectra (EIS) proved that the electron life time was the most improved by 1.60 times in the optimized cell (τ{sub e} = 37.58 ms) compared with that of the cell made only using pure TiO{sub 2} (τ{sub e} = 23.52 ms). Thus, addition of 0.3%wt Al{sup 3+} as a dopant was appropriate to attain suitable photocurrent efficiency for the QDSSCs because it could be used in a minimum amount to improve the electron transport, drop the recombination and increase the cell efficiency.

77 NANOSCIENCE AND NANOTECHNOLOGY↗