QuantOm SciDAC Collaboration

Highlights

April 16, 2025

This work introduces a Scalable Asynchronous Generative Inverse Problem Solver (SAGIPS) for high-performance computing systems. The resulting workflow utilizes an asynchronous ring-allreduce algorithm to transfer the gradients of a Generative Adverserial Network (GAN) across multiple GPUs. Experiments with a scientific proxy application demonstrate convergence quality comparable to traditional methods and near-linear weak scaling. This novel approach holds the potential to significantly advance methods for solving complex, large-scale inverse problems.

Stress in quantum systems

February 27, 2025

This work studies how stresses and momentum flow operate in bound quantum systems, with special attention paid to how stresses induced by the wave function guide particle motion. This study additionally shows how mechanical form factors—which are extracted from the quantum correlation functions measured at JLab and the EIC—can be used to image forces in bound quantum systems. The hydrogen atom is used as a case study, and it is shown that the mechanical form factors allow the Coulomb force law to be mapped out.

Differentiable evolution code for generalized parton distributions

February 24, 2025

The QuantOm has collaboration developed an ultra-fast software package called tiktaalik to perform renormalization group evolution of generalized parton distributions. The code uses finite element methods to make the evolution auto-differentiable, allowing it to be used in AI/ML frameworks, such as those being developed by QuantOm.

QuantOm Collaboration

The Quantum Chromodynamics Nuclear Tomography (QuantOm) Collaboration brings together theoretical and experimental physicists, applied mathematicians, data scientists, and computer scientists with a common goal: to understand the fundamental nature of visible matter.

Our mission is to reconstruct three-dimensional images of the fundamental particles—quarks and gluons—that form the femtometer-scale structure of visible matter—that is, atomic nuclei and their proton and neutron building blocks. The binding of quarks and gluons into protons and neutrons generates nearly all the mass of the visible universe. This mass arises from a sophisticated dance of quantum interactions which are described by quantum chromodynamics (QCD), the fundamental theory of the strong nuclear force.

The multi-dimensional internal structure of protons and neutrons in terms of quarks and gluons is encoded in Quantum Correlation Functions (QCFs). The QuantOm Collaboration is developing a state-of-the-art inference framework to extract these QCFs from experimental data taken at particle accelerator facilities worldwide, including Jefferson Lab (JLab) and the upcoming Electron-Ion Collider (EIC).

Our approach extends traditional histogram-based methods by incorporating event-level analysis, which preserves the full richness and maximal amount of information contained in the experimental data. In doing so, detector effects are included via folding in contrast to the traditional unfolding methods. This requires unifying modern theory frameworks based on QCD factorization theorems with sophisticated simulations of experimental detectors into a single pipeline. This provides a workflow to robustly incorporate both theoretical and experimental effects, such as radiative corrections and quantum-mechanical interference between real and virtual processes, on equal footing.

Central to our methodology is the use of advanced data science and artificial intelligence techniques. These tools enable the extraction of QCFs in a manner that is scalable and fully auto-differentiable. As a byproduct of our research, the QuantOm Collaboration is developing and releasing world-class physics software packages for computing QCFs and related observables, and for their inference from experimental event-level data.

QuantOm Collaboration

Cross-Disciplinary Collaboration

Accessing the hidden universe of quarks and gluons is a challenge requiring expertise across multiple disciplines. Theorists provide rigorous mathematical expressions that connect the footprints of this hidden universe to observational data. Experimentalists enable realistic simulations of complex particle reactions at world-class facilities such as Jefferson Lab and the future Electron-Ion Collider. To reconstruct the details of this hidden universe, data scientists orchestrate end-to-end, simulation-based inference. Processing the full simulation pipeline and the large-scale data from these experiments demands high-performance computing expertise. Finally, data-visualization specialists render the reconstructed information as tomographic images of the sub-femtometer-scale world.

Theory

Our theory team, consisting of researchers from Jefferson Lab and Argonne National Laboratory, is pushing forward the frontiers of quantum chromodynamics by developing and extending factorization frameworks. These allow quantum correlation functions to be measured in an increasingly large variety of experimental processes at Jefferson Lab and the future Electron Ion Collider, and also allow radiative effects from electromagnetic interactions to be incorporated into theoretical models. Moreover, we are developing and deploying numerical implementations of these frameworks using modern finite element methods, allowing the frameworks to be employed in an auto-differentiable, machine learning-friendly pipeline.

Experiment and Detector Simulation

The Experimental Working Group, led by Jefferson Lab, is developing a new folding approach that bridges theoretical calculations and experimental measurements. This approach enables the joint theoretical-experimental workflow of the QuantOm collaboration. Using modern machine learning techniques, including generative AI, the team is modeling how theoretical predictions would appear in real-world experiments. These state-of-the-art models help ensure that comparisons between theory and data are accurate, scalable, and efficient. The folding method also enables real-time data analysis and, when combined with streaming readout, is expected to improve precision and reduce background in next-generation measurements.

Generative and Bayesian Machine Learning

Our experimental working group, including researchers from Jefferson Lab, is developing a novel, simulation-based inference method in a conceptual Bayesian framework designed for Generative Adversarial Networks (GANs). We develop new techniques to solve ill-posed inverse problems powered by advanced scientific computing and machine learning methods. The conceptual Bayesian GAN framework allows for accessible quantification of statistical uncertainties. The accurate estimation of statistical uncertainties is necessary to reveal systematic effects and bias. These methods provide high-fidelity and expressive extraction of Quantum Correlation Functions that can be seamlessly incorporated into our event-level framework for effective representation of physics experiments.

Mathematics and Computer Science

Our applied mathematics team is developing a next-generation, simulation-based inference methodology that allows accurate event-level data analysis. We develop scalable inverse-problem solvers powered by advanced scientific machine learning coupled with rigorous statistical methodology, advanced sampling, and fast surrogate models that accelerate inference by orders of magnitude. Together, these advances enable the rapid and high-fidelity extraction of QCFs, providing theory with sharper constraints and experiment and detector models with more realistic representations.

Supercomputing and Visualization

Our supercomputing and visualization team is developing a scalable distributed learning framework, along with associated libraries and tools, to overcome the computational challenges of performing quark-gluon correlation function (QCF) inference, which, in turn, enables the femtoscale imaging of nuclei on exascale systems. As such, the team interfaces with applied mathematicians and AI researchers to map, optimize, or refactor the algorithms within the aforementioned framework, libraries, and tools for high performance on exascale systems, including a comprehensive visualization framework or library to support real-time, human-in-the-loop decision-making in machine-learning workflows, focusing on QCFs and their uncertainties.