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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 127 records · Page 7

Several Levels of Theory for Description of Isotope Effects in Ozone: Symmetry Effect and Mass Effect

The essential components of theory for the description of isotope effects in recombination reaction that forms ozone are presented, including the introduction of three reaction pathways for symmetric and asymmetric isotopomers, a brief review of relevant experimental data for singly- and doubly substituted isotopologues, the definitions of ζ-effect and η-effect, and the introduction of isotopic enrichment δ. Two levels of theory are developed to elucidate the role of molecular symmetry, atomic masses, vibrational zero-point energies, and rotational excitations in the recombination process. Here, the issue of symmetry is not trivial, since the important factors, such as 1/2 and 2, appear in seven different places in the formalism. It is demonstrated that if all these effects are taken into account properly, then no anomalous isotope effects emerge. At the next level of theory, a model is considered in which one scattering resonance (sitting right at the top of centrifugal barrier) is introduced per ro-vibrational channel. It is found that this approach is equivalent to statistical treatment with partition functions at the transition state. Accurate calculations using hyper-spherical coordinates show that no isotope effects come from difference in the number of states. In contrast, differences in vibrational and rotational energies lead to significant isotope effects. However, those effects appear to be local, found for the rather extreme values of rotational quantum numbers. They largely cancel when rate coefficients are computed for the thermal distribution of rotational excitations. Although large isotope effects (observed in experiments) are not reproduced here, this level of theory can be used as a foundation for more detailed computational treatment, with accurate information about resonance energies and lifetimes computed and included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Finite Element Solver for Fission Dynamics

FELIX is a physics computer code used to model fission fragment mass distributions in a fully quantum-mechanical, microscopic framework that only relies on our current knowledge of nuclear forces. It is an implementation of the time-dependent generator coordinate method (TDGCM), which simulates the dynamics of a collective quantum wave-packet assuming the motion is adiabatic. In typical applications of the TDGCM, the nuclear collective wavepacket is obtained as a superposition of wavefunctions obtained by solving the Hartree-Fock-Bogoliubov equations of nuclear density functional theory (DFT). The program calculates at each time step the coefficients of that superposition.

Regnier, D.↗

Finite Element Solver for Fission Dynamics

FELIX is a physics computer code used to model fission fragment mass distributions in a fully quantum-mechanical, microscopic framework that only relies on our current knowledge of nuclear forces [1]. It is an implementation of the time-dependent generator coordinate method (TDGCM), which simulates the dynamics of a collective quantum wave-packet assuming the motion is adiabatic. In typical applications of the TDFCM, the nuclear collective wavepacket is obtained as a superposition of wavefunctions obtained by solving the Hartree-Fock-Bogoliubov equations of nuclear density functional theory (DFT). The program calculates at each time step the coefficients of that superposition.

Regnier, D.↗

Operator-level quantum acceleration of non-logconcave sampling

Sampling from probability distributions of the form 𝝈 ∝ e −𝜷V , where V is a continuous potential, is a fundamental task across physics, chemistry, biology, computer science, and statistics. However, when V is nonconvex, the resulting distribution becomes non-logconcave, and classical methods such as Langevin dynamics often exhibit poor performance. We introduce a quantum algorithm that provably accelerates a broad class of continuous-time sampling dynamics. For Langevin dynamics, our method encodes the target Gibbs measure into the amplitudes of aquantum state, identified as the kernel of a block matrix derived from a factorization of the Witten Laplacian operator. This connection enables Gibbs sampling via singular value thresholding and yields up to a quartic quantum speedup over best-knownclassical Langevin-based methods in the non-logconcave setting. Building on this framework, we further develop the first quantum algorithm that accelerates replica exchange Langevin diffusion, a widely used method for sampling from complex, rugged energy landscapes.

97 MATHEMATICS AND COMPUTING↗

Multiscale models and stochastic simulation methods for computing rare but key binding events in cell biology

The main difficulty in simulating diffusion processes at a molecular level in cell microdomains is due to the multiple scales involving nano- to micrometers. Few to many particles have to be simulated and simultaneously tracked while there are exploring a large portion of the space for binding small targets, such as buffers or active sites. Bridging the small and large spatial scales is achieved by rare events representing Brownian particles finding small targets and characterized by long-time distribution. These rare events are the bottleneck of numerical simulations. A naive stochastic simulation requires running many Brownian particles together, which is computationally greedy and inefficient. Solving the associated partial differential equations is also difficult due to the time dependent boundary conditions, narrow passages and mixed boundary conditions at small windows. We present here two reduced modeling approaches for a fast computation of diffusing fluxes in microdomains. The first approach is based on a Markov mass-action law equations coupled to a Markov chain. The second is a Gillespie's method based on the narrow escape theory for coarse-graining the geometry of the domain into Poissonian rates. The main application concerns diffusion in cellular biology, where we compute as an example the distribution of arrival times of calcium ions to small hidden targets to trigger vesicular release.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

On the Moduli Space of Wigner Quasiprobability Distributions for N-Dimensional Quantum Systems

A mapping between operators on the Hilbert space of an N-dimensional quantum system and Wigner quasiprobability distributions defined on the symplectic flag manifold is discussed. The Wigner quasiprobability distribution is constructed as a dual pairing between the density matrix and the Stratonovich–Weyl kernel. It is shown that the moduli space of Stratonovich–Weyl kernels is given by the intersection of the coadjoint orbit space of the group SU(N) and a unit (N − 2)-dimensional sphere. The general considerations are exemplified by a detailed description of the moduli space of 2, 3, and 4-dimensional systems.

97 MATHEMATICS AND COMPUTING↗

Quantum supremacy using a programmable superconducting processor

The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2 53 (about 10 16 ). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. Finally, this dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy for this specific computational task, heralding a much-anticipated computing paradigm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Long-haul high rate quantum key distribution

Multiple bit values can be encoded on a single photon in a quantum key distribution (QKD) system using a plurality of sidebands of an optical carrier frequency. Computational and conjugate bases can be defined, and photons decoded based on a selected state from either basis. If n sidebands are available, as many as log2 n bits can be encoded on a single photon. Errors in detected bit values due to selection of an incorrect basis state or other errors can be at least partially corrected by bit distillation to identity bit strings for which a transmitter and a receiver record the same values, without insecure transmission of these values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

97 MATHEMATICS AND COMPUTING↗

Light-Promoted C(sp 3 )–C(sp 3 ) Reductive Elimination from Dialkyl NiII Complexes

Ni-catalyzed cross-coupling is a powerful strategy to forge C(sp 3 )–C(sp 3 ) bonds. Typically, to do so requires overcoming a challenging C–C bond-forming reductive elimination, often enabled by the intermediacy of highly oxidized Ni species or outer-sphere processes. While direct C(sp 3 )–C(sp 3 ) reductive elimination from the Ni II base oxidation state is normally thermally inaccessible, light-activation provides an avenue to affect such transformations. Here, we investigate the mechanism of light-induced C(sp 3 )–C(sp 3 ) bond formation from dialkyl bipyridine Ni II complexes through a variety of organometallic, spectroscopic, and computational studies. Wavelength-dependent quantum yields, ligand electronics–reactivity relationships, excited-state lifetimes, computed barriers, and product distributions from crossover studies support a photolysis/radical rebound mechanism. Furthermore, this reactivity paradigm complements existing strategies in the literature to promote reductive elimination from Ni II , such as the use of destabilizing, sterically hindered ligands and reduction of electron density at Ni through the binding of electron-deficient olefins. Hence, we envision that light-induced reductive elimination may enable the development of challenging C(sp 3 )–C(sp 3 ) couplings.

Alkyls↗

Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning

Quantum error mitigation (QEM) is critical in reducing the impact of noise in the pre-fault-tolerant era, and is expected to complement error correction in fault-tolerant quantum computing (FTQC). In this work, we propose a novel QEM approach, Q-Cluster, that uses unsupervised learning (clustering) to reshape the measured bit-string distribution. Our approach starts with a simplified bit-flip noise model. It first performs clustering on noisy measurement results, i.e., bit-strings, based on the Hamming distance. The centroid of each cluster is calculated using a qubit-wise majority vote. Next, the noisy distribution is adjusted with the clustering outcomes and the bitflip error rates using Bayesian inference. Our simulation results show that Q-Cluster can mitigate high noise rates (up to 40% per qubit) with the simple bit-flip noise model. However, real quantum computers do not fit such a simple noise model. To address the problem, we (a) apply Pauli twirling to tailor the complex noise channels to Pauli errors, and (b) employ a machine learning model, ExtraTrees regressor, to estimate an effective bit-flip error rate using a feature vector consisting of machine calibration data (gate & measurement error rates), circuit features (number of qubits, numbers of different types of gates, etc.) and the shape of the noisy distribution (entropy). Our experimental results show that our proposed Q-Cluster scheme improves the fidelity by a factor of 1.46x, on average, compared to the unmitigated output distribution, for a set of low-entropy benchmarks on five different IBM quantum machines. Our approach outperforms the state-of-art QEM approaches RZNE [28], M3 [24], Hammer [35], and QBEEP [33] by 1.26x,1.29x,1.47x, and 2.65 x, respectively.

42 ENGINEERING↗

Data-driven quantum approximate optimization algorithm for power systems

Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.

Jing, Hang (ORCID:0000000256343489)↗

A quantum encryption design featuring confusion, diffusion, and mode of operation

Abstract Quantum cryptography—the application of quantum information processing and quantum computing techniques to cryptography has been extensively investigated. Two major directions of quantum cryptography are quantum key distribution (QKD) and quantum encryption, with the former focusing on secure key distribution and the latter focusing on encryption using quantum algorithms. In contrast to the success of the QKD, the development of quantum encryption algorithms is limited to designs of mostly one-time pads (OTP) that are unsuitable for most communication needs. In this work we propose a non-OTP quantum encryption design utilizing a quantum state creation process to encrypt messages. As essentially a non-OTP quantum block cipher the method stands out against existing methods with the following features: 1. complex key-ciphertext relation (i.e. confusion) and complex plaintext-ciphertext relation (i.e. diffusion); 2. mode of operation design for practical encryption on multiple blocks. These features provide key reusability and protection against eavesdropping and standard cryptanalytic attacks.

97 MATHEMATICS AND COMPUTING↗

Distributionally Robust Variational Quantum Algorithms With Shifted Noise

Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is the high sensitivity of quantum noise to environmental changes, and its propensity to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step towards improving the reliability of VQAs influenced by real-time noise.

97 MATHEMATICS AND COMPUTING↗

R&D 100, 2016: T-Quake – Quantum-Mechanical Transmitter/Receiver Microchip

Applying advanced microfabrication techniques and innovative microdesign, the Sandia Enabled Communications and Authentication Network (SECANT) team has designed and produced photonic microchips capable of sending, receiving, and processing quantum signals for applications in cyber and physical security.

36 MATERIALS SCIENCE↗

Long-haul high rate quantum key distribution

Multiple bit values can be encoded on a single photon in a quantum key distribution (QKD) system using a plurality of sidebands of an optical carrier frequency. Computational and conjugate bases can be defined, and photons decoded based on a selected state from either basis. If n sidebands are available, as many as log.sub.2n bits can be encoded on a single photon. Errors in detected bit values due to selection of an incorrect basis state or other errors can be at least partially corrected by bit distillation to identity bit strings for which a transmitter and a receiver record the same values, without insecure transmission of these values.

97 MATHEMATICS AND COMPUTING↗

Long-haul high rate quantum key distribution

Multiple bit values can be encoded on a single photon in a quantum key distribution (QKD) system using a plurality of sidebands of an optical carrier frequency. Computational and conjugate bases can be defined, and photons decoded based on a selected state from either basis. If n sidebands are available, as many as log2n bits can be encoded on a single photon. Errors in detected bit values due to selection of an incorrect basis state or other errors can be at least partially corrected by bit distillation to identity bit strings for which a transmitter and a receiver record the same values, without insecure transmission of these values.

Nordholt, Jane Elizabeth↗

Simulating Alpha Particles Incident on MKID Chips for Quantum Sensitivity Analysis

Superconducting quantum devices, such as microwave kinetic inductance detectors (MKIDs), are highly sensitive instruments used in quantum computing and advanced sensing technologies. However, their extreme sensitivity also makes them vulnerable to background noise from natural sources like radiation. One significant contributor to this noise is alpha particles emitted by 210Po, a radon decay daughter that accumulates on surfaces near the detector. This project investigates how alpha particles emitted from 210Po interact with MKID chips. These particles can deposit energy on the detector surface, disrupting its operation and generating false signals. Understanding the energy and behavior of these particles is crucial for improving the design and reliability of quantum devices. To explore this, we first modeled the decay chain starting from 210Pb to 210Po using differential equations. This allowed us to predict how the activity of alpha-emitting isotopes changes over time, reaching a steady state after about two years. Next, we simulated alpha particle interactions with the MKID chip using the Geant4 software toolkit. We built a detailed computer model of the detector housing, including the copper lid where alpha particles originate, the silicon chip, and a thin aluminum sensor layer. Alpha particles were emitted isotropically from just beneath the copper lid’s surface, mimicking natural decay conditions. The simulation tracked how these particles deposit energy on the chip, generating electron-hole pairs and phonons. The results provide insight into the behavior of the resultant electron-hole pairs and phonons, giving us a clear understanding of the energy deposition distribution on the chip. This work supports efforts to mitigate background noise in superconducting sensors, advancing their use in quantum computing and sensitive physics experiments.

Hall, Matthew [Fermilab; UCLA]↗