Real-time quantum dynamics of thermal states with neural thermofields

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

We describe a quantum algorithm for finding the smallest eigenvalue of a Hermitian matrix. This algorithm combines quantum phase estimation and quantum amplitude estimation to achieve a quadratic speedup with respect to the best classical algorithm in term ...

Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

The Heisenberg quantum antiferromagnet on the maple leaf lattice has been shown to feature highly exotic phases, and therefore material realizations are intensely sought after. We determine the magnetic Hamiltonian of the copper mineral bluebellite using d ...

We study the maximum-average submatrix problem, wherein given an N x N matrix J, one needs to find the k x k submatrix with the largest average number of entries. We investigate the problem for random matrices J, whose entries are i.i.d. random variables, ...

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Quantum many-body dynamics generically result in increasing entanglement that eventually leads to thermalization of local observables. This makes the exact description of the dynamics complex despite the apparent simplicity of (high-temperature) thermal st ...

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

Elemental powder blends are an emerging alternative to prealloyed powders for high-throughput alloy design via additive manufacturing techniques. Elemental Al+Sc(+Zr) powder blends were processed by laser powder bed fusion into Al-Sc and Al-Sc-Zr alloys, w ...

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP d ...

The Hall scattering factor, r, is a key quantity for establishing carrier concentration and drift mobility from Hall measurements; in experiments, it is usually assumed to be 1. In this paper, we use a combination of analytical and ab initio modeling to de ...

Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of inter ...

Real-time quantum dynamics of thermal states with neural thermofields

Chat with Graph Search

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

A square-root speedup for finding the smallest eigenvalue

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

Effective spin-1 breathing kagome Hamiltonian induced by the exchange hierarchy in the maple leaf mineral bluebellite

Statistical mechanics of the maximum-average submatrix problem

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models

Time-evolution of local information: Thermalization dynamics of local observables

Learning of physical systems: from inference to control

Predictive Analysis of Errors During Robot-Mediated Gamified Training

Operando X-ray diffraction study of thermal and phase evolution during laser powder bed fusion of Al-Sc-Zr elemental powder blends

Image Restoration using Plug-and-Play CNN MAP Denoisers

Theory and Computation of Hall Scattering Factor in Graphene

Sparse coupled logistic regression to estimate co-activation and modulatory influences of brain regions