2026 CAP Congress / Congrès de l'ACP 2026

Session

W2-2 SYMPOSIUM: Big Data in Matter, Materials, and Beyond | Le Big Data dans la matière, les matériaux et au-delà

W2-2

24 Jun 2026, 14:15

U. Ottawa - Learning Crossroads (CRX) Building

507. Statistical and Machine Learning Methods for Material Science and Quantum Sensing

Carlo Bradac (Trent University)

24/06/2026, 14:15

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

Machine learning (ML) is now ubiquitous. Fuelled by rapid advances in computational power and data accessibility, it has become the preferred paradigm for solving problems involving pattern recognition, classification, and complex, dynamic interactions. In this talk, I discuss the role that machine learning is playing in advancing material and device fabrication, as well as quantum...

515. Smarter Quantum Experiments: Boosting Quantum Research with Artificial Intelligence

Stefanie Czischek (University of Ottawa)

24/06/2026, 14:45

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

The optimized and efficient control of experimentally realized quantum systems is becoming increasingly crucial in the current era of quantum science and technology. Progress in fields like quantum computation, simulation, cryptography, sensing, or metrology depends strongly on the precise preparation, control, and understanding of quantum systems. At the same time, artificial intelligence has...

528. Self-learning Monte Carlo with an Equivariant Transformer

Yuki Nagai (The University of Tokyo)

24/06/2026, 15:15

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

Machine-learning-based approaches have become increasingly important in computational physics, particularly for simulations of complex many-body systems. In this context, equivariance provides a natural way to incorporate physical symmetries into models, acting as an inductive bias on the learned probability distributions. While such symmetry constraints are desirable, their direct...