Taking gradients through experiments: LSTMs and memory proximal policy
optimization for black-box quantum control

Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control

12 February 2018

José Miguel Hernández-Lobato

Papers citing "Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control"

6 / 6 papers shown

Title
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits Jiahao Yao Haoya Li Marin Bukov Lin Lin Lexing Ying 16 15 0 30 Mar 2022
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving Jiahao Yao Lin Lin Marin Bukov BDL AI4CE 21 61 0 07 Oct 2020
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices Vu-Linh Nguyen S. B. Orbell D. Lennon H. Moon F. Vigneau ... D. Zumbuhl G. Briggs Michael A. Osborne D. Sejdinovic N. Ares 19 39 0 30 Sep 2020
Quantum enhancements for deep reinforcement learning in large spaces Sofiene Jerbi Lea M. Trenkwalder Hendrik Poulsen Nautrup H. Briegel Vedran Dunjko 27 5 0 28 Oct 2019
Optimizing Quantum Error Correction Codes with Reinforcement Learning Hendrik Poulsen Nautrup Nicolas Delfosse Vedran Dunjko H. Briegel N. Friis 14 155 0 20 Dec 2018
Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics Pantita Palittapongarnpim P. Wittek E. Zahedinejad Shakib Vedaie B. Sanders AI4CE 51 87 0 12 Jul 2016