Data-centric Learning of Quantum Matter | 9am PT, Tues Jan 6, 2026

[Grigory Bronevetsky]

Modeling Talks

Data-centric Learning of Quantum Matter  Eunah Kim, Cornell U Tues, Jan 6, 2026 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/data-centric-learning-of-quantum-matter More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Modern quantum materials and quantum devices generate data that are simultaneously high-dimensional, heterogeneous, and constrained by rich physical structure. From diffraction movies and readout of quantum hardware to materials databases accumulated over decades, extracting interpretable insight from such data poses fundamental challenges for both physics-based modeling and machine learning. In this talk, I will present a unifying modeling perspective on learning quantum matter, highlighting how domain structure and machine learning tools an be combined to offer new insights and predictions. I will introduce several case studies developed at the interface of condensed-matter physics and machine learning: (i) Quantum Attention Networks (QuAN) that use self-attention to characterize of complex quantum states; (ii) X-TEC, a clustering evolving high-dimensional diffraction data, which has led to the discovery of Bragg glass order in disordered charge-density-wave systems; and (iii) GPTc that predict superconducting transition temperatures with calibrated uncertainty from heterogeneous experimental databases. Together, these examples illustrate how modern modeling—grounded in physics but enabled by machine learning—can turn complex quantum data into predictive understanding, while revealing new opportunities for collaboration between ML and the physical sciences. Bio: Eun-Ah Kim is the Hans Bethe Professor of Physics at Cornell University. A pioneer at the intersection of quantum many-body physics, quantum simulation and artificial intelligence. She is the director of NSF AI institute: AI-Materials Institute (AI-MI). Her contributions have been recognized with prestigious honors, including a Radcliffe Fellowship, two Simons Fellowships for Theoretical Physics, and election as a Fellow of the American Physical Society. She received her Ph.D. from the University of Illinois at Urbana-Champaign and completed postdoctoral research at Stanford University before joining the Cornell faculty in 2008.

[Grigory Bronevetsky]

Video Recording: https://youtube.com/live/TsnQ0Qfybvs

Slides: https://drive.google.com/file/d/1ayhy4jQ7pWVXDiiHCxHEyBq2acTmg5Qn/view?usp=sharing

Summary:

• Focus: AI techniques for modeling quantum matter/materials

• Materials the behavior of which is government by a many-body quantum state

• Behavior is collective, not single particle

• Very sensitive to values of external knobs

• Ex: superconductors, topological materials, quantum magnets

• Physics:

• Wavefunction is a complex valued vector function in an exponentially large space

• Evolves over time

• Need ensemble of wavefunctions to finite temperature equilibrium state

• Though, not all phenomena can be described as equilibrium states

• Task:

• Need to design a material from given set of chemicals with set of desired properties

• Many forward modeling challenges

• Exponential wavefunction statespace

• Relevant latent variables are unknown and emergent

• Never observe wavefunction directly

• Traditional forward models require strong inductive bias (results in errors where bias is incorrect)

• Data is invaluable since the inverse modeling problem (given data, infer best model) is very challenging and data intensive

• A representation learning problem where we need to ingest diverse datasets that collectively constrain the behavior space of the physics into a common representation that can be used to constrain models

• Representation Learning

• Quantum attention networks for state characterization

• Scenario: state space of many binary qubits

• Given a fine number of samples, summarize them in the most informative representation

• Approach: classification problem

• Volume law vs area law state

• Topological vs trivial state

• Shallow circuit vs deep circuit

• Model looks at correlation in the moments; has access to higher order moments without explicitly representing them

• Exploit permutation invariance in the samples by training model on random batches of observations

• X-TEC for X-Ray diffraction

• Goal: infer temperature/disorder from X ray images of materials

• Infer time series and its phase transition across temperature thresholds

• Enables experimentalists to collect data at the key phase transition temperature range

• GPTc: Gaussian process Tc Predictor

• Accumulated Heterogeneous Data

• Gather materials structure information across the literature

• Molecular structure is the key differentiator for the differences in physical behavior beyond chemical composition

• Elemental features: electron affinity, electronegativity, ionization potential, covalent radius, atomic weight, column, # valence s/p/d e

• Considering many candidate 2nd order features (e.g. interatomic distance)

• Using ML to evaluate various features by using them as inputs to an ML model to predict observable properties

• Identify which features are useful

• Using Gaussian Processes to use histograms as features

• Was able to predict superconductivity and transition temperature of a new material where it was previously unknown