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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.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗309 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.
- arXiv · Published · First archived
A photonic integrated comb engine for ultracold quantum gases ↗
Cold atoms underpin quantum sensing, simulation and computation, but their coherent control demands highly stable optical fields whose generation, referencing and power scaling remain formidable integration challenges. While photonic integrated circuits have yielded compact visible lasers and high-$Q$ microresonators have enabled chip-scale optical frequency combs, these crucial technologies have largely remained functionally fragmented. Consequently, the coherent manipulation of ultracold quantum gases using a fully integrated laser-comb source has yet to be realized. Here we demonstrate a sc…
- arXiv · Published · First archived
Broadband Quantum Optical Storage with Chemically Engineered Molecular Eu$^\text{3+}$ Complex ↗
Broadband quantum memory devices are essential elements for future quantum networks. Here we propose a broadband quantum memory scheme called Hole Anti-hole Grating Echo Memory (HAGEM) for rare-earth ions in solids. We provide a Eu$^\text{3+}$ molecular complex with special hyperfine level structures of which the hyperfine level separations are in a specific mathematical correlation that can be obtained by harnessing chemical engineering. Using the memory protocol and material, we experimentally demonstrate a quantum optical storage efficiency of 14.9% and a memory bandwidth of 200MHz, which c…
- arXiv · Published · First archived
Beyond Maximal Entanglement: Exact Resources for Multiparty Encrypted Quantum Cloning ↗
Encrypted quantum cloning distributes an unknown $k$-qubit state among $m$ encrypted clones so that no individual clone reveals the input, yet the state can be recovered from any one clone together with a common quantum key. We ask the inverse question: for a fixed encoding architecture, which multipartite pure states can serve as exact resources for this task? For $m\ge2$, we completely characterize the pure resources compatible with a sector-wise two-Pauli encoder, with necessity holding for arbitrary completely positive trace-preserving (CPTP) recovery maps. For even $m$, exact recovery req…
- arXiv · Published · First archived
Distilling Datasets into Shallow Circuits for Quantum Machine Learning ↗
In quantum machine learning, training a quantum model requires each sample to be prepared as a quantum state by a loading circuit that must be re-executed for every shot at every training step. The total burden therefore scales with both the number of samples and the cost of preparation. Existing approaches reduce the loading cost of individual inputs or distill data and compress their representations before applying a separate quantum encoding. This separation can leave resulting samples costly to prepare or cause additional loss of the information during subsequent compilation into shallow c…
- arXiv · Published · First archived
Circuit-depth optimization of quantum partial-search algorithms ↗
Grover's algorithm is optimal in terms of oracle queries. We can trade accuracy for speed, which gives rise to the quantum partial-search algorithm. The partial-search algorithm is implemented using two kinds of Grover operators, global and local, where the former is the standard Grover operator for full search and the latter has a diffusion operator acting on the search subspace. The global-local-global sequence, also known as the Grover-Radhakrishnan-Korepin (GRK) algorithm, has been proved optimal in the oracle-query metric. In this work, we show that the alternating sequence of global and…
- arXiv · Published · First archived
Unbounded Operators, Physicality, and Modality in Quantum Theories ↗
In this paper, I address the problem regarding the physicality of Hilbert space in quantum mechanics, a topic brought into focus by Carcassi et al.'s paper "The unphysicality of Hilbert spaces" recently. I argue for the physicality of the Hilbert space in quantum mechanics, and demonstrate that there are inevitable ambiguity and arbitrariness if we want to identify the set of physical states with a proper subset of it. Moreover, I distinguish the ontological and modal reading of physicality from the pragmatic and operational reading of physicality, and then develop what I call the hierarchical…
- OpenAlex · Published · First archived
Trip-End Battery State of Charge Prediction for Electric Vehicles Using a Physics-Constrained Machine Learning Stacking Ensemble ↗
This study proposes an approach for predicting the state of charge (SoC) of an electric vehicle battery at the end of an individual operational interval using parameters available before the start of the trip. The input features included the initial SoC, trip distance, and ambient temperature. The driving range estimated by the onboard system was also considered, as it summarizes information about the vehicle’s previous operating conditions. The direct target variable was the change in state of charge over the operational interval (Delta SoC), which was subsequently used to calculate the state…
- OpenAlex · Published · First archived
Measurement-Driven Path-Loss Modeling for Low-Altitude UAV Air-to-Ground Links Using Physics-Aware Ensemble Learning and Calibrated Uncertainty ↗
Air-to-ground (A2G) channel modeling supports low-altitude wireless networks for unmanned aerial vehicle (UAV) services in 5G/6G systems. Existing data-driven models are often benchmarked against weak empirical baselines, decoupled from the underlying measurement campaigns, and prone to overfitting on small UAV datasets. This paper presents PACE-A2G, a calibrated deep-tree ensemble on physics-aware features, together with a stricter evaluation methodology. A quadrotor-mounted software-defined-radio measurement system produces five datasets covering 433, 740, 1500, and 3300 MHz in overwater, su…
- OpenAlex · Published · First archived
A hybrid physics and machine learning framework for trajectory prediction and uncertainty quantification of undrained behaviour in gassy clay ↗
The undrained response of gassy clay is strongly path-dependent and uncertain, making continuous trajectory prediction difficult when only limited experimental data are available. This study develops a lightweight physics-guided machine learning framework for path-level prediction and uncertainty quantification under undrained shearing. The framework combines MCC-based physical-reference trajectories, which provide stress-history-dependent mechanical information, with experimental gassy-clay trajectories, which provide target-domain response information. Both data sources are converted into a…
- OpenAlex · Published · First archived
Federated hybrid quantum convolutional neural network for Alzheimer’s disease classification using magnetic resonance imaging ↗
Alzheimer’s disease (AD) is a progressive neurological disorder and one of the most common types of dementia, causing impaired memory and quality of life. Therefore, an early and accurate diagnosis is essential for intervention and patient care. Convolutional Neural Networks (CNNs) and other deep learning models have demonstrated promise in medical imaging but may need centralized collection of data, which raises privacy concerns. In this study, we propose a Federated Hybrid Quantum Convolutional Neural Network (FH-QCNN) that integrates quantum computing with federated learning for privacy-pre…
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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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