3 citations · 4 across the 12 of their papers we have counts for
14 papers
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks
J. Arturo Esquivel, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo-García +2
Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping react…
Grand Canonical Generators
Andreas Burger, Malte Franke, Luka Mucko +2
We introduce Grand Canonical Generators (GCG), a generative framework that extends Boltzmann generators to the grand canonical ensemble. We present two designs. The first condition…
Colour me shocked: Exact Molecular Hessians from local MLIPs in O(N) time using sparse differentiation!
Luca Thiede, Andreas Burger, Alán Aspuru-Guzik
The Hessian of the energy with respect to the nuclear positions is indispensable in atomistic modelling. However, constructing this matrix requires Hessian vector products,…
From Global to Factor-Wise Expert Composition in Discrete Diffusion Models
Haozhe Huang, Yudong Xu, Abhijoy Mandal +1
Discrete diffusion models offer a powerful framework for solving complex reasoning tasks, particularly through compositional generation, which combines multiple pre-trained experts…
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
Yifan Ruan, Chenyang Cao, Andreas Burger +7
Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy…
De novo molecular generation with optical property preconditioning at the token level
Haozhe Huang, Manuel Gonzalez Lastre, Hyun Suk Park +3
Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generat…
MōLe-Λ: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties
Andreas Burger, Luca Thiede, Abdulrahman Aldossary +4
Coupled-cluster (CC) theory is often considered the gold standard of quantum chemistry, but its high computational cost limits routine access to accurate energies, forces and respo…
Optimizing ground state preparation protocols with autoresearch
Luis Mantilla Calderón, Jérôme F. Gonthier, Ignacio Gustin +2
Artificial intelligent language-model based coding agents have significantly changed the way we interact with computers in our day-to-day, as it is common to use them to create, im…
Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupl…
Learning with springs and sticks
Luis Mantilla Calderón, Alán Aspuru-Guzik
Learning is a physical process. Here, we aim to study a simple dynamical system composed of springs and sticks capable of arbitrarily approximating any continuous function. The mai…
Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays
Kouhei Nakaji, Jonathan Wurtz, Haozhe Huang +4
We introduce the "quantum circuit daemon" (QC-Daemon), a reinforcement learning agent for compiling quantum device operations aimed at efficient quantum hardware execution. We appl…
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
Shunya Minami, Kouhei Nakaji, Yohichi Suzuki +2
Quantum computing is entering a transformative phase with the emergence of logical quantum processors, which hold the potential to tackle complex problems beyond classical capabili…
Symmetry From Scratch: Group Equivariance as a Supervised Learning Task
Haozhe Huang, Leo Kaixuan Cheng, Kaiwen Chen +1
In machine learning datasets with symmetries, the paradigm for backward compatibility with symmetry-breaking has been to relax equivariant architectural constraints, engineering ex…
Quantum Transformer: Accelerating model inference via quantum linear algebra
Naixu Guo, Zhan Yu, Matthew Choi +5
Powerful generative artificial intelligence from large language models (LLMs) harnesses extensive computational resources for inference. In this work, we investigate the transforme…