Bayesian parameter estimation using Gaussian states and measurements

no code implementations • 8 Sep 2020 • Simon Morelli, Ayaka Usui, Elizabeth Agudelo, Nicolai Friis

Bayesian analysis is a framework for parameter estimation that applies even in uncertainty regimes where the commonly used local (frequentist) analysis based on the Cram\'er-Rao bound is not well defined.

Quantum Physics

Optimizing Quantum Error Correction Codes with Reinforcement Learning

no code implementations • 20 Dec 2018 • Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, Nicolai Friis

Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest.

reinforcement-learning Reinforcement Learning (RL) +1

Speeding-up the decision making of a learning agent using an ion trap quantum processor

no code implementations • 5 Sep 2017 • Theeraphot Sriarunothai, Sabine Wölk, Gouri Shankar Giri, Nicolai Friis, Vedran Dunjko, Hans J. Briegel, Christof Wunderlich

We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions.

BIG-bench Machine Learning Decision Making