How can we use symmetry to do better quantum machine learning (QML)? Excited to announce our new preprint arxiv.org/abs/2210.07980, an example-driven tutorial paper which introduces the fundamentals of representation theory in a QML context. Here's some highlights: (1/8)

Given a symmetric quantum learning task, how can we exploit this symmetry in quantum learning models? The answer is to use Equivariant Quantum Neural Networks (EQNN)! We provide a comprehensive theoretical framework for EQNNs in our new preprint: arxiv.org/abs/2210.08566 (1/7)

After giving a theory for geometric quantum machine learning, in arxiv.org/abs/2210.09974 we prove that permutation-equivariant quantum neural networks (EQNNs) •Don’t exhibit barren plateaus •Quickly reach overparameterization •Generalize well from small datasets (🧵1/8)

Applications are now open for the 2023 Los Alamos Quantum Computing Summer School! Our school focuses on theory, applications, and programming of quantum computers. Apply here: smr.to/p85950

🚨Applications are still open for the 2023 Quantum Computing Summer School at LANL. Apply here: lnkd.in/gYTmkabM 🚨 Deadline for the applications is January 22nd. More information, including highlights from past schools can be found at lnkd.in/gkx_29Dn

New preprint: Lie algebraic methods for the classical simulations of quantum circuits, and their applications !! 👇👇👇