Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing

no code implementations • 14 Dec 2023 • M. Cerezo, Martin Larocca, Diego García-Martín, N. L. Diaz, Paolo Braccia, Enrico Fontana, Manuel S. Rudolph, Pablo Bermejo, Aroosa Ijaz, Supanut Thanasilp, Eric R. Anschuetz, Zoë Holmes

A large amount of effort has recently been put into understanding the barren plateau phenomenon.

Trainability barriers and opportunities in quantum generative modeling

no code implementations • 4 May 2023 • Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp, Oriel Kiss, Sofia Vallecorsa, Michele Grossi, Zoë Holmes

In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration.

The power and limitations of learning quantum dynamics incoherently

no code implementations • 22 Mar 2023 • Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph, Matthias C. Caro, Patrick J. Coles, Hsin-Yuan Huang, Zoë Holmes

Quantum process learning is emerging as an important tool to study quantum systems.

Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer

no code implementations • 7 Dec 2020 • Manuel S. Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa, Sonika Johri, Borja Peropadre, Alejandro Perdomo-Ortiz

Generating high-quality data (e. g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning.

Quantum Physics