Publications associées à Stochastic Approximation of Variational Quantum Imaginary Time Evolution

Publications associées (22)

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

2025

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

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As urban populations continue to expand and vehicle density increases, the need for effective traffic monitoring and management becomes increasingly critical. Urban areas face significant challenges such as growing congestion, safety concerns, and parking ...

EPFL2025

On fundamental aspects of quantum extreme learning machines

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Quantum extreme learning machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a quantum substrate-the quantum reservoir-and the efficient post-measurement ...

SPRINGERNATURE2025

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Academic Press2025

Robust Confinement State Classification with Uncertainty Quantification Through Ensembled Data-driven Methods

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Maximizing fusion performance in tokamaks relies on high energy confinement, often achieved through distinct operating regimes. The automated labeling of these confinement states is crucial to enable large-scale analyses or for real-time control applicatio ...

IOP Publishing Ltd2025

Learning by Teaching: Designing Teachable Agents to Support Children's Pronunciation Skill Learning

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Pronunciation is a critical yet challenging aspect of language acquisition, especially for children. In this work, we propose a new teachable agent system designed to support children's pronunciation learning through a Learning-by-Teaching paradigm to addr ...

Association for Computing Machinery2025

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2024

NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila

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Discovering principles underlying the control of animal behavior requires a tight dialogue between experiments and neuromechanical models. Such models have primarily been used to investigate motor control with less emphasis on how the brain and motor syste ...

Nature Publishing Group2024

Hierarchical Training of Deep Neural Networks Using Early Exiting

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Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Programming nonlinear propagation for efficient optical learning machines

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ugur Tegin, Ilker Oguz, Mustafa Yildirim, Carlo Gigli, Jih-Liang Hsieh

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power com ...

Spie-Soc Photo-Optical Instrumentation Engineers2024

Almost exact recovery in noisy semi-supervised learning

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2024.Graph-based semi-supervised learning methods combine the graph structure and labeled data to classify unlabeled data. In this work, we study the effect of a noisy oracle on classification. In particular, we derive the maximum a posteriori (MAP) estima ...

2024

∇2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

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Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. However, high computational complexity limits the scalability of their applica ...

Neural information processing systems foundation2024

A frequency-independent bound on trigonometric polynomials of Gaussians and applications

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We prove a frequency-independent bound on trigonometric functions of a class of singular Gaussian random fields, which arise naturally from weak universality problems for singular stochastic PDEs. This enables us to reduce the regularity assumption on the ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2024

Stable Linear Subspace Identification: A Machine Learning Approach

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Institute of Electrical and Electronics Engineers Inc.2024

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The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

2023

Low-temperature non-equilibrium synthesis of anisotropic multimetallic nanosurface alloys for electrochemical CO2 reduction

Andreas Züttel, Emad Oveisi, Mo Li, Jie Zhang, Cedric David Koolen

Multimetallic nanoparticles are of interest as functional materials due to their highly tunable properties. However, synthesizing congruent mixtures of immiscible components is limited by the need for high-temperature procedures followed by rapid quenching ...

Springernature2023

Forward-forward training of an optical neural network

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Jih-Liang Hsieh

Neural networks (NNs) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics ...

Optica Publishing Group2023

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Martin Jaggi, Luis Felipe Barba Flores

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-sc ...

SPRINGERNATURE2022

Model-Agnostic Learning to Meta-Learn

Yatin Dandi, Arnout Jan J Devos

In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution. We investigate how learning wi ...

PMLR2021

Attention is not all you need: pure attention loses rank doubly exponentially with depth

Andreas Loukas, Jean-Baptiste Francis Marie Juliette Cordonnier

Attention-based architectures have become ubiquitous in machine learning. Yet, our understanding of the reasons for their effectiveness remains limited. This work proposes a new way to understand self-attention networks: we show that their output can be de ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021