From the 1 of 74 linked papers with an AI index.
6 citations · 7 across the 19 of their papers we have counts for
18 papers · 1 filter
Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory
Danish Khan, Maurice D. Hanisch, Nikolai Argatoff +3
Kohn--Sham density functional theory (DFT) underpins electronic-structure simulations, but repeated orbital diagonalizations lead to cubic scaling, restricting quantum calculations…
Inverse Design of Quantum Control Sequences with Fourier Neural Operators
Anastasia Pipi, Valentin Duruisseaux, Emily Been +4
Quantum optimal control is a key tool for steering quantum dynamics, but its computational cost grows rapidly with the Hilbert space dimension. Here, we introduce a Fourier Neural…
M+Adam: Low-Precision Training via Additive-Multiplicative Optimization
Xiaoyuan Liang, Sebastian Loeschcke, Mads Toftrup +1
The paper introduces M+Adam, an optimizer that blends additive and multiplicative updates to enable stable low‑precision training of large language models without keeping high‑prec…
BRIDGE: Building Representations In Domain Guided Program Synthesis
Robert Joseph George, Carson Eisenach, Udaya Ghai +3
Large language models can generate plausible code, but remain brittle for formal verification in proof assistants such as Lean. A central scalability challenge is that verified syn…
Mechanistic Interpretability with Sparse Autoencoder Neural Operators
Bahareh Tolooshams, Ailsa Shen, Anima Anandkumar
We introduce sparse autoencoder neural operators (SAE-NOs), a new class of sparse autoencoders that operate in function spaces rather than fixed-dimensional Euclidean representatio…
Elevating Variational Quantum Semidefinite Programs for Polynomial Objectives
Iria W. Wang, Robin Brown, Taylor L. Patti +3
Many practically important NP-hard optimization problems are inherently higher-order polynomial optimizations, which are typically addressed using approximation algorithms. Classic…