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Earlier sources: Physics
This science thread follows physics through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
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Open in the map ↗316 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Symmetry-driven correlation patterns in one-dimensional periodic fermionic systems: Closed-form expressions and exact selection rules for correlation functions, entanglement entropies, and mutual information ↗
We present an analytical study of the spatial structure of correlations in periodic fermionic systems at the single Slater determinant level, focusing on the cyclic case. Exploiting cyclic symmetry, the transformation from localized orbitals to molecular orbitals is simultaneously a discrete Fourier transform, a Vandermonde matrix on the roots of unity, a complex Hadamard matrix, and the character table of the cyclic group Cm. Within this framework, the spatial structure of correlations is fully determined by the occupied irreducible representations (Fourier modes), independently of the specif…
- OpenAlex · Published · First archived
Simulating Chemical Reaction Dynamics Using Sample-Based Quantum Diagonalization: A Quantum-Centric Supercomputing Approach ↗
Abstract Quantum computers offer the potential to simulate chemical systems beyond the capabilities of classical computers. However, current quantum computing approaches to ab initio molecular dynamics (AIMD) and vibrational analysis largely rely on the Variational Quantum Eigensolver (VQE), whose circuit depth, iterative optimization, and measurement overhead hinder the practical simulation of molecular systems. Here, we extend Sample-based Quantum Diagonalization (SQD), a quantum sampling-based electronic-structure method, to AIMD and vibrational analysis on noisy quantum hardware. The resul…
- OpenAlex · Published · First archived
Holographic multipartite entanglement dynamics in AdS3-Vaidya ↗
A bstract We study how multipartite entanglement is dynamically reorganized during holographic thermalization following a global quench in AdS 3 /CFT 2 . We first use the n -partite information (− 1) n I n to probe collective multipartite entanglement in holographic configurations where the full n -region entanglement wedge is connected while all fewer-party ones are disconnected, thereby excluding fewer-party contributions. The spatial range of multipartite entanglement first expands and then contracts as the system approaches its late-time locally thermal state. Entanglement involving differ…
- OpenAlex · Published · First archived
Toward a Local Manipulation of DNA by Quantum Fields ↗
Abstract Hydrogen bonds are the fundamental backbone for deoxyribo-nucleic acid (DNA) stability. In this Letter we investigate a new strategy based on plasmonic cavities to perform manipulation of hydrogen bonds in DNA. The analysis is performed using state-of-the-art Quantum Electrodynamics Coupled Cluster (QED-CC) calculations. We investigate whether in the strong coupling regime modifications of the nucleotide bases’ interactions can be induced in a totally nonintrusive manner. Our strategy proposes an alternative approach to methodologies based on a collective coupling to perform optical D…
- OpenAlex · Published · First archived
Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks ↗
Reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading remains a major computational bottleneck in multi-scale simulations. We propose a coupled LSTM-GNN framework that links the temporal and spatial aspects of local stress field reconstruction. A Long Short-Term Memory network encodes macroscopic stress–strain sequences into a compact hidden state that captures the path-dependent constitutive response, while a physics-informed Graph Neural Network reconstructs the spatially-resolved stress field at each time step. We introduce a relativ…
- OpenAlex · Published · First archived
Physics-Informed Deep-Operator Learning for Computational Hydraulics Modeling ↗
Abstract Traditional two-dimensional (2D) hydraulic models face significant computational challenges (e.g., long computing times) that limit their use in applications that are time-sensitive or require many model evaluations. This study presents a physics-informed deep-operator network (DeepONet) framework for computational hydraulics modeling that learns the solution operator of the 2D shallow water equations (SWEs) to create fast surrogate models. The framework can operate in two modes: a purely data-driven SWE-DeepONet that learns from a numerical solver such as Sedimentation and River Hydr…
- OpenAlex · Published · First archived
Dilaton physics from asymptotic freedom ↗
The dilaton is investigated from first principles within an asymptotically free Gross-Neveu-Yukawa theory in three dimensions. In the limit of many fermion flavours, the theory features a finite line of strongly interacting fixed points. At its endpoint, a dilaton arises as the massless Goldstone boson due to the spontaneous breaking of scale symmetry. We show that the emergence of a vacuum expectation value and a dilaton can be understood as a double-scaling limit. Exploiting the scalar two-point function, we compute the dilaton decay constant and universal expressions for the induced dilaton…
- OpenAlex · Published · First archived
Exponential advantage in continuous-variable quantum state learning ↗
We consider the task of learning quantum states in bosonic continuous-variable (CV) systems. We present an experimentally feasible protocol that uses entangled measurements and reflected states to efficiently learn, up to sign, the characteristic function of CV quantum states, with sample complexity independent of the number of modes $n$. We prove that any adaptive scheme without entangled measurements requires exponentially many samples in $n$ for this learning task, thereby demonstrating an exponential advantage from entangled measurements. Remarkably, we also prove that any entanglement-ass…
- OpenAlex · Published · First archived
Hybrid Quantum–Classical Convolutional Neural Networks for Medical Image Classification: A Review of Architectures, Applications, and NISQ Challenges ↗
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Qua…
- OpenAlex · Published · First archived
Data Quality and Predictive Reliability in AI-Enabled Aviation Energy Management: Physics-Informed Sensitivity Analysis with Open-Data Validation ↗
The objective of this study is to quantify how independently degraded operational-data quality affects predictive accuracy, predictive uncertainty, and prediction-level eligibility for decision support in AI-enabled aviation energy management. The evidence base combines a 24,000-flight physics-informed synthetic experiment, an independent distribution-shift experiment, a 17,520 h synthetic airport-energy time series, and external validation using 686 observed fuel-burn intervals from 79 flights and 10 aircraft types in the EUROCONTROL PRC 2025/OpenSky dataset. In the primary flight experiment,…
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How to assess evidence about Physics
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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