50 citations · 60 across the 8 of their papers we have counts for
8 papers
Topology Obstructs Pure Foundation Neural Quantum States
Timothy Heightman, Elena Orlova, Philip Mantrov +1
Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase diagrams. Nearly all such models…
Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians
Timothy Heightman, Elena Orlova, Philip Mantrov +1
A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods. Here we demonstrate t…
Benchmarking Simulacra AI's Quantum Accurate Synthetic Data Generation for Chemical Sciences
Fabio Falcioni, Elena Orlova, Timothy Heightman +2
In this work, we benchmark \simulacra's synthetic data generation pipeline against a state-of-the-art Microsoft pipeline on a dataset of small to large systems. By analyzing the en…
Training neural operators to preserve invariant measures of chaotic attractors
Ruoxi Jiang, Peter Y. Lu, Elena Orlova +1
Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neura…
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang +2
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schrödinger equation inspired by stochastic mechanics and generative diffusio…
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
Elena Orlova, Haokun Liu, Raphael Rossellini +2
Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This st…
Comparing the costs of abstraction for DL frameworks
Maksim Levental, Elena Orlova
High level abstractions for implementing, training, and testing Deep Learning (DL) models abound. Such frameworks function primarily by abstracting away the implementation details…
Generative Models for Fast Calorimeter Simulation.LHCb case
Viktoria Chekalina, Elena Orlova, Fedor Ratnikov +3
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These…