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Earlier sources: Science
Science investigates the natural world through observation, experiments, and mathematical models. Explore papers and developments in biology, medicine, materials, neuroscience, physics, climate, and mathematics; a reported finding is not necessarily established consensus.
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 ↗1638 classified signals in the fourteen-day window. Recent momentum: steady. 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
Climate-informed cryospheric reanalysis via hierarchical Bayesian data assimilation ↗
The cryosphere regulates global cycles of water, energy, and carbon, affecting societies and ecosystems. By assimilating observations into models, we can infer the past trajectory of state variables, generating a cryospheric reanalysis. Existing cryospheric reanalyses typically treat each water year independently, with no pooling of information across years. With sparse and noisy observations, such 'climate-assumed' reanalyses perform poorly, reverting to an assumed yet uncalibrated background climatology. Conversely, Bayesian calibration with static parameters completely pools information acr…
- arXiv · Published · First archived
Multilevel Plaquette-Space Sampling for Lattice Gauge Theories with Local Constraint Solves ↗
Lattice gauge theories are an important class of physical models with high-dimensional structured distributions that underpin first-principles calculations in particle, nuclear, and condensed-matter physics. Recent advances in generative modeling have opened new avenues for sampling Boltzmann distributions in lattice gauge theories, offering the potential to alleviate limitations of traditional Monte Carlo methods, including critical slowing down and topological freezing. However, generative samplers for lattice gauge theories are typically constructed in link space, where correlations become…
- arXiv · Published · First archived
A Si-on-SiC Platform for Interfacing with Vanadium Dopants in the Telecom O-Band ↗
Vanadium color centers in silicon carbide (SiC) offer telecom O-band emission, sub-microsecond optical lifetimes, and second-scale spin relaxation times, rendering them promising candidates for quantum network nodes. However, efficient photon extraction from the high-refractive-index SiC host remains challenging. Here, we introduce a silicon-on-SiC heterogeneous photonic platform in which silicon nanocavities evanescently couple to shallow vanadium dopants in commercial 4H-SiC, enabling efficient zero-phonon line (ZPL) collection under resonant excitation. In as-grown ensembles, we observe Pur…
- arXiv · Published · First archived
Speed-Fisher Information: Chaos and Irreversibility in Classical and Quantum Dynamics ↗
We propose a unified perspective on classical and quantum chaos based on the sensitivity of a system's stationary states to slow driving. We probe this sensitivity via the system's susceptibility to the average protocol speed, which we call the ``speed-Fisher information," and relate it to irreversible entropy production in the system. We show that chaotic dynamics manifests as a divergence of the speed-Fisher information with the protocol time, and that this response is controlled by the perturbation's low-frequency spectral weight. This approach to chaos applies to both classical and quantum…
- arXiv · Published · First archived
Explainable Deep Learning for Probabilistic Nowcasting of Radar Reflectivity in Tornadic Storms ↗
Tornadoes pose substantial risk to human life and property in the United States, causing more than 50 fatalities and \$100 million of property damage on average annually. When tornadoes are likely, weather radar provides critical information for forecasters by providing information on storm morphology, storm motion, and intensity trends. Additional tools such as satellite and numerical weather prediction model runs can provide useful short-term information for understanding changes in storm characteristics. This work demonstrates a U-Net deep-learning system for nowcasting the evolution of rad…
- arXiv · Published · First archived
Optimal Query Complexity for Ground-State Preparation ↗
We determine the optimal query complexity of ground-state preparation to trace-distance error $\varepsilon$ when an energy threshold in the spectral gap is known. Let $U_H$ be an $α$-block-encoding of a Hamiltonian with unique ground state $|ψ_0\rangle$, and suppose $|\langleψ_0|U_I|0\rangle|\geγ$ for a state-preparation oracle $U_I$. The threshold lies at least $Δ/2$ above the ground-state energy and at least $Δ/2$ below every excited-state energy. We give two algorithms that prepare a state within trace distance $\varepsilon$ of the ground state. One uses $O((α/Δ)(γ^{-1}+\log(1/\varepsilon))…
- arXiv · Published · First archived
Accelerating massive ensembles of ordinary differential equations ↗
Solving large ensembles of small, independent ordinary differential equations (ODEs) is a common task in computational science and engineering, arising for example in Bayesian parameter estimation, Monte Carlo uncertainty quantification, and the integration of uncoupled physical systems such as the linearised Einstein-Boltzmann equations of cosmology. Modern graphics processing units (GPUs) with thousands of cores are well suited to such parallel workloads, but realising this potential requires careful attention to GPU-specific architectural constraints. We present modax, a Python library of G…
- arXiv · Published · First archived
An Optimal Quantum Linear Systems Algorithm ↗
In the quantum linear systems problem (QLSP), we are given query access to a $d$-sparse $N\times N$ matrix $A$ with condition number $κ$, and the ability to prepare a quantum state proportional to a vector $\vec b$. The goal is to output an $ε$-approximation to the quantum state proportional to the solution $\vec x$ of $A\vec{x}=\vec{b}$. Following a long line of work, the best previously known quantum algorithms for the QLSP had query complexities $O(κd\log(1/ε))$ and $κ\sqrt d(κd/ε)^{o(1)}$, while the best known lower bounds were $Ω(κ\log(1/ε))$ and $Ω(κ\sqrt d)$. We improve these bounds and…
- arXiv · Published · First archived
The power of oracle access: Optimal sample and query complexity of the abelian state hidden subgroup problem ↗
In the quest to identify further quantum algorithms exhibiting superpolynomial speed-ups, a recurring theme is that the complexity of a problem is largely shaped by the input access model. Here, we study this phenomenon for the state hidden subgroup problem (StateHSP), a quantum generalization of the hidden subgroup problem in which the goal is to identify the symmetries of an unknown quantum state. For finite abelian groups, existing Fourier-sampling algorithms use $O(\log(|G|)/ε)$ copies of the state, but whether this scaling is optimal has remained open. We settle the complexity of the abel…
- arXiv · Published · First archived
Optimal performance in a chaotic Floquet quantum battery ↗
We study a quantum battery consisting of $N$ qubits interacting uniformly with each other. Starting from its ground state, the battery is charged by introducing periodic kicks in the transverse direction along with adding disorder in the interaction direction. Although the battery Hamiltonian along with the initial state preserves the permutation symmetry, the dynamics in presence of disorder during the charging phase takes the system out of the permutation symmetric subspace, resulting to a larger amount of stored energy as compared to that without disorder. In the limit of large disorder, th…
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