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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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DOE OSTI2023

Decoding Quantum Gravity and Cosmology from the Hologram (Final Scientific/Technical Report)

Understanding quantum gravity has been a long-standing goal of theoretical physics. Solving this problem is essential for answering fundamental questions about black holes and cosmology. Over the last two years, significant progress has been made in solving the black hole information problem using the holographic duality and its connections to quantum information science. Despite this rapid progress, there are many key issues that remain to be resolved. The research in this project investigated new holographic properties of quantum gravity and cosmology by leveraging recently discovered connections between quantum entanglement and wormholes. One of the main accomplishments of this project is a better understanding of the gravity duals of entanglement measures such as the entanglement negativity and Renyi entropies. These results will help us decode the structure of multipartite entanglement in gravitational holography.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Coupling to short linear motifs creates versatile PME-1 activities in PP2A holoenzyme demethylation and inhibition

Protein phosphatase 2A (PP2A) holoenzymes target broad substrates by recognizing short motifs via regulatory subunits. PP2A methylesterase 1 (PME-1) is a cancer-promoting enzyme and undergoes methylesterase activation upon binding to the PP2A core enzyme. Here, we showed that PME-1 readily demethylates different families of PP2A holoenzymes and blocks substrate recognition in vitro. The high-resolution cryoelectron microscopy structure of a PP2A-B56 holoenzyme–PME-1 complex reveals that PME-1 disordered regions, including a substrate-mimicking motif, tether to the B56 regulatory subunit at remote sites. They occupy the holoenzyme substrate-binding groove and allow large structural shifts in both holoenzyme and PME-1 to enable multipartite contacts at structured cores to activate the methylesterase. B56 interface mutations selectively block PME-1 activity toward PP2A-B56 holoenzymes and affect the methylation of a fraction of total cellular PP2A. The B56 interface mutations allow us to uncover B56-specific PME-1 functions in p53 signaling. Our studies reveal multiple mechanisms of PME-1 in suppressing holoenzyme functions and versatile PME-1 activities derived from coupling substrate-mimicking motifs to dynamic structured cores.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Exact Fock-State Preparation with $n^{1/4}$ Circuit Depth

Efficient, deterministic, and high-fidelity preparation of large Fock states is essential for scaling bosonic quantum technologies and exploring quantum phenomena at large excitation energies. We introduce a deterministic one-parameter (D1p) protocol that maps Fock-state preparation in an infinite-dimensional Hilbert space onto two-dimensional amplitude amplification. Starting from a coherent state with $|α|\simeq\sqrt{n}$, the initial target-state population scales as $n^{-1/2}$, yielding an iteration count and circuit depth of $\mathcal{O}(n^{1/4})$. Phase matching guarantees unit fidelity in the ideal model; remarkably, preparing $|{10^6}\rangle$ requires only 39 iterations. The protocol uses only displacements and number-selective phase operations, requires no numerical optimization, and further extends to state transfer, general superpositions, finite-dimensional systems, and multipartite entangled states. In the large-amplitude regime, its multi-target form prepares $L$-legged cat states with an iteration count determined only by $L$; cats with up to ten legs require only two iterations, independent of the coherent-state amplitude. This framework provides a broadly applicable route to highly excited bosonic states on platforms supporting these elementary controls.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗

DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗