A recipe for cracking the quantum scaling limit with machine learned electron densities

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Aircraft-based monitoring of wildlife is a popular way among conservation practitioners to obtain animal population counts over large areas. Nowadays, these aerial censuses are becoming increasingly scalable due to the advent of drone technology, which is ...

This dataset accompanies the publication "Distinguishing Inner and Outer-Sphere Hot Electron Transfer in Au/p-GaN Photocathodes" published in Nano Letters. The data can be used to reproduce the original plots in figures 2-4 in the main text and all origina ...

Antiferromagnetic spin textures, compared to their ferromagnetic counterparts, innately possess high stability with respect to external disturbance and high-frequency dynamics compatible with ultrafast information processing. However, deterministic creatio ...

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properti ...

Quantum optics studies how photons interact with other forms of matter, the understanding of which was crucial for the development of quantum mechanics as a whole. Starting from the photoelectric effect, the quantum property of light has led to the develop ...

Base excision repair enzymes (BERs) detect and repair oxidative DNA damage with efficacy despite the small size of the defects and their often only minor structural impact. A charge transfer (CT) model for rapid scanning of DNA stretches has been evoked to ...

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today’s world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increas ...

Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models c ...

Coffee production serves as an important source of income for millions of farmers across many tropical countries. Given the scale of the production, accurate and up-to-date maps of coffee plantations are needed to detect, monitor, and mitigate potential ne ...

In this thesis, we give new protocols that offer a quantum advantage for problems in ML, Physics, and Finance. Quantum mechanics gives predictions that are inconsistent with local realism. The experiment proving this fact (Bell, 1964) gives a quantum proto ...

This dataset accompanies the publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films" published in Light: Science & Applications (https://doi.org/10.1038/s41377-024-01408-2). The data can be used to reproduce plots 1-4 ...

Infrared and Raman spectroscopies are ubiquitous techniques employed in many experimental laboratories, thanks to their fast and non-destructive nature able to capture materials' features as spectroscopic fingerprints. Nevertheless, these measurements freq ...

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Dislocation phenomena in crystal materials are fundamental to understanding mechanical behaviors such as plasticity and strengthening. This study investigates the atomistic mechanisms of dislocation phenomena in hexagonal close-packed (HCP) magnesium (Mg) ...

Mechanical oscillators can exhibit modes with ultra-low energy dissipation and compact form factors due to the slow velocity of acoustic waves, and are already used in applications ranging from timing to wireless filters. Over the past decade, novel ways i ...

Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. Access to a computational space that incorporates quantum effects, such as superposition ...

Quantum sensors and qubits are usually two-level systems (TLS), the quantum analogues of classical bits assuming binary values 0 or 1. They are useful to the extent to which superpositions of 0 and 1 persist despite a noisy environment. The standard prescr ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

A recipe for cracking the quantum scaling limit with machine learned electron densities

Chattez avec Graph Search

How to minimize the annotation effort in aerial wildlife surveys

Dataset to accompany publication "Distinguishing Inner and Outer-Sphere Hot Electron Transfer in Au/p-GaN Photocathodes"

Deterministic switching of antiferromagnetic spin textures by nonlinear magnons

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Efficient and insightful descriptors for representing molecular and material space

Probing the effects of broken symmetries in machine learning

Room-Temperature Quantum Optomechanics and Free-Electron Quantum Optics

Oxidative Defect Detection Within Free and Packed DNA Systems: A Quantum Mechanical/Molecular Mechanics (QM/ MM) Approach

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data

The complexity of quantum support vector machines

Semantic Segmentation of Coffee Plantations from Sentinel-2 Time Series

Quantum Protocols for ML, Physics, and Finance

Dataset to accompany publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films"

Automated all-functionals infrared and Raman spectra

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Atomic investigations of dislocation phenomena in HCP magnesium and BCC high entropy alloys

Ultra-high quantum coherent and scalable superconducting circuit optomechanics, from topological lattices to quantum storage

Scalable Quantum Algorithms for Noisy Quantum Computers

Emergence of highly coherent two-level systems in a noisy and dense quantum network

Machine learning-aided generative molecular design