Publications related to Exact Sampling for Classical and Quantum Many-Body Systems

Related publications (13)

On-chip Germanium nanowires for hole spin qubits

The creation of a universal quantum computer that can solve complex problems beyond the reach of classical computers - the so-called "quantum supremacy" - is an open challenge. For this to become a reality, a large number of qubits should be integrated in ...

EPFL2025

Landau–Zener without a qubit: multiphoton sidebands interaction and signatures of dissipative quantum chaos

Vincenzo Savona, Pasquale Scarlino, Fabrizio Minganti, Marco Scigliuzzo, Vincent Jean Yves Jouanny, Filippo Ferrari, Léo Paul Peyruchat

Landau–Zener–Stückelberg–Majorana (LZSM) interference occurs when qubit parameters are periodically modulated across avoided level crossings. We explore this phenomenon in nonlinear multilevel bosonic systems, where interference is influenced by multiple e ...

2025

Barren plateaus in variational quantum computing

Zoë Holmes, Thanasilp Supanut

Variational quantum computing offers a flexible computational approach with a broad range of applications. However, a key obstacle to realizing their potential is the barren plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization la ...

NATURE PORTFOLIO2025

Nonparametric network summaries

Arthur Verdeyme

This thesis is concerned with improving the interpretability of network summaries in both a theoretical and an applied framework, with an application to clinically relevant problems in neuroscience. The theoretical part of this thesis is concerned with gra ...

EPFL2025

BlueCelluLab: Biologically Detailed Neural Network Experimentation API

Henry Markram, Eilif Benjamin Muller, Werner Alfons Hilda Van Geit, Lida Kanari, Mustafa Anil Tuncel, Darshan Mandge, Aurélien Tristan Jaquier

The NEURON simulator, established in 1984 and continuously developed since, stands as the preeminent tool for neuron simulation within computational neuroscience. Its widespread adoption and compatibility with computational clusters and supercomputers unde ...

The Open Journal2024

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Volkan Cevher, Fanghui Liu, Leello Tadesse Dadi

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

2024

Programming the scalable optical learning operator with spatial-spectral optimization

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Carlo Gigli, Jih-Liang Hsieh, Yi Zhou Ju

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical tech ...

2024

High-fidelity dimer excitations using quantum hardware

Zoë Holmes

The quantum simulation of entangled spin systems can play a central role in quantum magnetic materials discovery. Additionally, the simulation of spectroscopic signatures, such as the dynamical structure factor accessed in inelastic neutron scattering (INS ...

AMER PHYSICAL SOC2024

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Ante Maric, Yiming Li, Sylvain Calinon

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Sampling strategies for the Herman-Kluk propagator of the wavefunction

Fabian Kröninger, Jiri Vanicek

When the semiclassical Herman-Kluk propagator is used for evaluating quantum-mechanical observables or time-correlation functions, the initial conditions for the guiding trajectories are typically sampled from the Husimi density. Here, we employ this propa ...

2023

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Hossein Darvishi

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomal ...

2023

Cluster-guided Image Synthesis with Unconditional Models

Grigorios Chrysos

Generative Adversarial Networks (GANs) are the driving force behind the state-of-the-art in image generation. Despite their ability to synthesize high-resolution photo-realistic images, generating content with on-demand conditioning of different granularit ...

IEEE COMPUTER SOC2022

Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

Pascal Frossard, Nikolaos Geroliminis, Semin Kwak

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes such complexities by representing the evolution of signals over a space using ...

2021