Monte Carlo
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Related Articles from SNS
A Diffusion Monte Carlo algorithm employing depth first traversal and a stack instead of a swarm
arXiv:2606.08946v1 Announce Type: cross Abstract: Diffusion Monte Carlo (DMC) and Monte Carlo for particle transport with importance sampling both involve simulations of weighted walkers that undergo birth and death processes (splitting and Russian Roulette). The established implementations of these methods are quite different: Particle simulation Monte Carlo employs a stack to handle the splitting history whereas in traditional DMC one follows a swarm of walkers. The particle simulation...
A geometric $q$-analogue of Hamiltonian Monte Carlo
arXiv:2512.13246v3 Announce Type: replace Abstract: Hamiltonian Monte Carlo (HMC) generates efficient Markov transitions by combining Hamiltonian dynamics with a Metropolis correction. This paper develops a geometric \(q\)-analogue of HMC by replacing classical Hamiltonian dynamics with a \(q\)-deformed Hamiltonian system arising from \(q\)-calculus. Starting from a Lagrangian formulation, we derive the corresponding \(q\)-Hamiltonian equations and prove the formal invariance of the...
GPU optical photon Monte Carlo for noble liquid detectors: validation against Geant4 in a large liquid argon TPC benchmark
Announce Type: new Abstract: Optical photon Monte Carlo simulation is a computational bottleneck for noble liquid Time Projection Chambers. Design studies require repeated, geometry dependent simulations of timing, wavelength shifting, and optical response, while reconstruction and particle identification workflows need labeled optical datasets. We present Simphony, a GPU optical simulation tool, formerly EIC-Opticks, built on Opticks with CUDA and NVIDIA OptiX. Simphony implements a GPU...
GPU optical photon Monte Carlo for noble liquid detectors: validation against Geant4 in a large liquid argon TPC benchmark
Announce Type: replace Abstract: Optical photon Monte Carlo simulation is a computational bottleneck for noble liquid Time Projection Chambers. Design studies require repeated, geometry dependent simulations of timing, wavelength shifting, and optical response, while reconstruction and particle identification workflows need labeled optical datasets. We present Simphony, a GPU optical simulation tool, formerly EIC-Opticks, built on Opticks with CUDA and NVIDIA OptiX. Simphony implements a GPU...
Optimality of quasi-Monte Carlo methods and suboptimality of the sparse-grid Gauss--Hermite rule in Gaussian Sobolev spaces
Announce Type: replace Abstract: Optimality of several quasi-Monte Carlo methods and suboptimality of the sparse-grid quadrature based on the univariate Gauss--Hermite rule is proved in the Sobolev spaces of mixed dominating smoothness of order $\alpha$, where the optimality is in the sense of worst-case convergence rate. For sparse-grid Gauss--Hermite quadrature, lower and upper bounds are established, with rates coinciding up to a logarithmic factor. The dominant rate is found to be only...
Towards stable and accurate electron dynamics via neural network based time-dependent variational Monte Carlo
arXiv:2606.05850v1 Announce Type: new Abstract: Real-time dynamics of interacting electrons lies at the interface between quantum mechanics and non-equilibrium physics, governing the microscopic origin of ultrafast phenomena of molecules and nano-materials. Though neural network variational Monte Carlo has achieved unprecedented accuracy for stationary state calculations, its extension to real-time evolution remains challenging. In this work, we introduce the neural basis time-dependent...
Reinforced sequential Monte Carlo for amortised sampling
arXiv:2510.11711v2 Announce Type: replace Abstract: This paper proposes a synergy of amortised and particle-based methods for sampling from distributions defined by unnormalised density functions. We state a connection between sequential Monte Carlo (SMC) and neural sequential samplers trained by maximum-entropy reinforcement learning (MaxEnt RL), wherein learnt sampling policies and value functions define proposal kernels and twist functions. Exploiting this connection, we introduce an...
Diagrammatic Monte Carlo for positron-molecule many-body theory
Announce Type: new Abstract: A diagrammatic Monte Carlo evaluation of the ladder series contributions to the correlation potential (self energy) of a positron in the field of a molecule is presented. The $GW$@TDHF, virtual-positronium ($T$-matrix), and positron-hole Goldstone ladder series contributions are stochastically sampled order-by-order within the Tamm-Dancoff approximation, which is exact for the latter two classes, with Ces{\'a}ro-Riesz resummation used to extrapolate to infinite...
Electron-Ion Path Integral Monte Carlo with Hard Core
arXiv:2606.04667v1 Announce Type: cross Abstract: We performed numerical (restricted) path integral Monte Carlo experiments on metallic Hydrogen from first principles. We study a quantum two component plasma where one component is made of pointwise particles of negative unitary charge and the other is made of charged hard spheres of positive unitary charge. We study both the additive mixture and a nonadditive mixture where we only keep a hard core between unlike species.
Enhancing Neural-Network Variational Monte Carlo through Basis Transformation
arXiv:2604.15888v2 Announce Type: replace-cross Abstract: Neural-network variational Monte Carlo (NNVMC) has emerged as a powerful tool for solving quantum many-body problems, yet systematic pathways for improving its accuracy remain largely heuristic. Here, we introduce a physically motivated basis transformation for NNVMC that enhances variational expressivity without increasing the complexity of the neural-network ansatz itself. By formulating the many-body wave function in a Gaussian...