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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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Computational Power of Random Quantum Circuits in Arbitrary Geometries
Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of approximately 50 qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and, therefore, how much infidelity is suffered) in generating highly entangled states. Here, we describe recent hardware upgrades to Quantinuum’s H2 quantum computer, enabling it to operate on up to 56 qubits with arbitrary connectivity and 99.843(5)% two-qubit gate fidelity. We define a class of circuits with random geometries that become hard to classically simulate in very low depth and implement them utilizing the flexible connectivity of H2. A careful analysis demonstrating the fast saturation of classical simulation complexity with depth indicates that H2 can yield data well beyond the reach of state-of-the art classical simulation methods at unprecedented fidelities. We find that the considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the quantum charge-coupled device architecture as continued progress is made toward building larger machines. Published by the American Physical Society 2025
Measuring the Loschmidt Amplitude for Finite-Energy Properties of the Fermi-Hubbard Model on an Ion-Trap Quantum Computer
Calculating the equilibrium properties of condensed-matter systems is one of the promising applications of near-term quantum computing. Recently, hybrid quantum-classical time-series algorithms have been proposed to efficiently extract these properties from a measurement of the Loschmidt amplitude ⟨ ψ | e − i H ^ t | ψ ⟩ from initial states | ψ ⟩ and a time evolution under the Hamiltonian H ^ up to short times t . In this work, we study the operation of this algorithm on a present-day quantum computer. Specifically, we measure the Loschmidt amplitude for the Fermi-Hubbard model on a 16 -site ladder geometry (32 orbitals) on the Quantinuum H2-1 trapped-ion device. We assess the effect of noise on the Loschmidt amplitude and implement algorithm-specific error-mitigation techniques. By using a thus-motivated error model, we numerically analyze the influence of noise on the full operation of the quantum-classical algorithm by measuring expectation values of local observables at finite energies. Finally, we estimate the resources needed for scaling up the algorithm. Published by the American Physical Society 2024
Data-driven Vulnerability Analysis of Networked Pipeline System
This paper introduces an attack generation framework for evaluating the vulnerability of nonlinear networked pipeline systems. The vulnerability analysis is formulated as determining the presence of feasible attack sets, defined by boundary functions representing the effectiveness and stealthiness of attack signals with respect to the objective and attack detection module. The framework utilizes three data-driven models, including two discriminative models that learn the boundary functions and a generative model that produces elements of the feasible attack set. A new loss function ensures successful attack generation with high probability.
Robust Stabilization of Inverter-Based Resources Using Virtual Resistance-Based Control
This letter proposes a virtual resistance-based nonlinear control to stabilize and robustify the current layer of inverter-based resources, subject to the grid voltage disturbances, and the grid parameter uncertainties. A class of virtual resistances is proposed and analyzed using concepts from dissipative systems theory. Moreover, specific nonlinear virtual resistance-based controllers are derived, with their corresponding performance analytically bounded. Here, the theoretical and simulation results show that the proposed nonlinear virtual resistance-based controllers significantly reduce the L 2 gain of the closed-loop error system to grid voltage variation and parametric uncertainties. Significant improvements of the transient and steady-state current responses are also demonstrated over linear virtual resistance counterparts.
Information-Theoretic Exploration of Multivariate Time-Varying Image Databases
Modern scientific simulations produce very large datasets, making interactive exploration of such data computationally prohibitive. An increasingly common data reduction technique is to store visualizations and other data extracts in a database. The Cinema project is one such approach, storing visualizations in an image database for post hoc exploration and interactive image-based analysis. This work focuses on developing efficient algorithms that can quantify various types of multivariate dependencies existing within multi-variable datasets. It applies specific mutual information measures for the quantification of salient regions from multivariate image data. Here, using such information measures, the opacity of the images is modulated so that the salient regions are automatically highlighted and the domain scientists can interactively explore the most relevant regions for scientific discovery.
Robust Optimal Control of Inverter-Based Resources Under Grid-Forming Operation
In this paper, we propose and solve a robust control problem for inverter-based resources under grid-forming operation to regulate the voltage and frequency. One major challenge is to mitigate the effect of unmeasurable load current disturbance, grid and load parametric uncertainties. Moreover, strong coupling between the state variables on both the AC and DC sides, as well as between the modulating control input and the frequency impose additional challenges. To address these challenges, first, a robust control problem is solved at the high level via transformation into an equivalent, but more tractable, optimal control problem. Then, in the middle layer a voltage control law is designed on the one side, and a frequency control law on the other side. Finally, an inverter filter current controller is designed to complete the controller design. Theoretical results are derived to provide stability guarantees for the resulting closed-loop system. Specifically, we show that the inverter current injection error is dissipative, the frequency error is semi-globally asymptotically stable, and the inverter terminal voltage error is globally asymptotically stable, all with provided sufficient conditions. Here, numerical simulation experiments are used to validate the theoretical claims. Furthermore, the developed controller is compared with existing work in literature to show the efficacy of the proposed approach.
Soil fertility controls on tropical forest productivity and mortality: synthesis and roadmap
Tropical forests are highly diverse and productive ecosystems and store nearly 60% of vegetation biomass. Across the tropics, forests span large gradients of soil fertility, from vast regions situated on highly weathered, ancient geological formations to others on young, nutrient-rich landscapes. Tropical forest productivity, mortality, and biomass all vary systematically across these gradients in soil fertility. Aboveground forest productivity tends to increase with higher soil fertility, while counterintuitively, aboveground biomass does not increase proportionally. This disconnect is likely due to coinciding increases in mortality with higher soil fertility. However, we know relatively little about the mechanisms underlying how soil fertility regulates productivity or mortality – two critical determinants of forest biomass – and even less about how these relationships will shape tropical forest responses to global change. Here, we present a mechanistic framework for understanding how soil fertility affects productivity through photosynthesis, carbon allocation, and carbon-use efficiency; how it modulates mortality via direct and indirect interactions with various drivers of mortality; and how global change may alter these relationships. Based on this synthesis, we outline a roadmap to advance our understanding of tropical forests and improve predictions of their responses to global change.
Hardware-in-the-Loop Investigation of Emissions Challenges in Hybrid Medium- and Heavy-Duty Powertrains Using a Pre-Production Diesel-Electric Parallel Hybrid System With and Without Stop-Start Operation
Hybrid electric powertrains are a growing market in medium- and heavy-duty applications. There is a lack of available information to understand the challenges in the integration of engine platforms into electrified powertrains, such as cold-start, restart, and load-reduction effects on emissions and emission control devices. Results from the Heavy Heavy-Duty Diesel Truck (HHDDT) cycle using a conventional medium-duty diesel engine were compared with those of a parallel hybrid architecture. Oak Ridge National Laboratory in collaboration with the US Department of Energy and Odyne Systems, LLC developed a powertrain in a hardware-in-the-loop environment, integrating the Odyne Systems, LLC medium-duty parallel hybrid system, which was used for the hybrid portion of this study. Experiments under the HHDDT cycle showed increasing improvements in fuel consumption and engine-out emissions with the integration of stop/start, hybrid, and hybrid with stop/start. However, the effects of load reduction and exhaust temperature on the thermal management strategy have shown an increase in fueling in the second part of the HHDDT cycle. Four configurations of medium-duty electrification were studied and contributed to building a unique data set containing combustion, emissions, and system integration data. Each electrification level was compared with the conventional baseline. The calibration of the conventional engine was not altered for this study. Opportunities to tailor the combustion process were identified with the stop/start strategy.
Alkylperoxy radicals are responsible for the formation of oxygenated primary organic aerosol
Organic aerosol (OA) is an air pollutant ubiquitous in urban atmospheres. Urban OA is usually apportioned into primary OA (POA), mostly emitted by mobile sources, and secondary OA (SOA), which forms in the atmosphere due to oxidation of gas-phase precursors from anthropogenic and biogenic sources. By performing coordinated measurements in the particle phase and the gas phase, we show that the alkylperoxy radical chemistry that is responsible for low-temperature ignition also leads to the formation of oxygenated POA (OxyPOA). OxyPOA is distinct from POA emitted during high-temperature ignition and is chemically similar to SOA. We present evidence for the prevalence of OxyPOA in emissions of a spark-ignition engine and a next-generation advanced compression-ignition engine, highlighting the importance of understanding OxyPOA for predicting urban air pollution patterns in current and future atmospheres.
Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances. We perform noiseless simulations with up to 40 qubits and observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers, which are the state-of-the-art exact solvers for LABS. The combination of QAOA with quantum minimum finding gives the best empirical scaling of any algorithm for the LABS problem. We demonstrate experimental progress in executing QAOA for the LABS problem using an algorithm-specific error detection scheme on Quantinuum trapped-ion processors. Our results provide evidence for the utility of QAOA as an algorithmic component that enables quantum speedups.
Interface-induced superconductivity in magnetic topological insulators
The interface between two different materials can show unexpected quantum phenomena. Here, in this study, we used molecular beam epitaxy to synthesize heterostructures formed by stacking together two magnetic materials, a ferromagnetic topological insulator (TI) and an antiferromagnetic iron chalcogenide (FeTe). We observed emergent interface-induced superconductivity in these heterostructures and demonstrated the co-occurrence of superconductivity, ferromagnetism, and topological band structure in the magnetic TI layer—the three essential ingredients of chiral topological superconductivity (TSC). The unusual coexistence of ferromagnetism and superconductivity is accompanied by a high upper critical magnetic field that exceeds the Pauli paramagnetic limit for conventional superconductors at low temperatures. These magnetic TI/FeTe heterostructures with robust superconductivity and atomically sharp interfaces provide an ideal wafer-scale platform for the exploration of chiral TSC and Majorana physics.
Resilient State Recovery Using Prior Measurement Support Information
Resilient state recovery of cyber-physical systems has attracted much research attention due to the unique challenges posed by the tight coupling between communication, computation, and the underlying physics of such systems. By modeling attacks as additive adversary signals to a sparse subset of measurements, this resilient recovery problem can be formulated as an error correction problem. To achieve exact state recovery, most existing results require less than 50% of the measurement nodes to be compromised, which limits the resiliency of the estimators. In this paper, we show that observer resiliency can be further improved by incorporating data-driven prior information. Here, we provide an analytical bridge between the precision of prior information and the resiliency of the estimator. By quantifying the relationship between the estimation error of the weighted ℓ 1 observer and the precision of the support prior, this quantified relationship provides guidance for the estimator’s weight design to achieve optimal resiliency. Several numerical simulations and an application case study are presented to validate the theoretical claims.
High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs
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Generative Vulnerability Assessment for Cyber-Physical Systems
Cyber-physical systems (CPS) are highly susceptible to malicious attacks due to their complex dynamics and interconnectivity. A comprehensive understanding of their vulnerabilities is essential for designing effective resilience measures. This paper presents a data-driven attack generative system for evaluating the vulnerability of CPS. The proposed approach formulates the vulnerability assessment problem as determining the feasibility of a specific attack set based on two boundary functions that represent the effectiveness and stealthiness of attacks. The attack generative model is trained using a custom loss function, with two universal approximators designed to learn the effectiveness and stealthiness functions simultaneously. Theoretical results for successful generation and asymptotic convergence of the resulting training algorithm are given. As a result, the proposed approach is evaluated via numerical simulation of an IEEE 14-bus system and gas pipeline systems, demonstrating its viability in learning how to attack nonlinear CPS and identify potential vulnerabilities.
Natural Language Processing for Biological Relationship Extraction
Code allows users to easily pull publications from PubMed, OSTI, and SCOPUS; identify biological terms and extract their contexts from these papers; and determine which terms are related to other terms using one of the eleven wrapped algorithms
PNNL-Predictive-Phenomics/DancePartner
Mining multi-omics relationship networks from literature and databases
ComPile: A Large IR Dataset from Production Sources
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