7 citations · 7 across the 1 of their papers we have counts for
7 papers
Experimentally Realizing Efficient Quantum Control with Reinforcement Learning
Ming-Zhong Ai, Yongcheng Ding, Yue Ban +7
Robust and high-precision quantum control is crucial but challenging for scalable quantum computation and quantum information processing. Traditional adiabatic control suffers seve…
Breaking Adiabatic Quantum Control with Deep Learning
Yongcheng Ding, Yue Ban, José D. Martín-Guerrero +3
In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep rei…
Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing
Narendra N. Hegade, Koushik Paul, Yongcheng Ding +4
Shortcuts to adiabaticity are well-known methods for controlling the quantum dynamics beyond the adiabatic criteria, where counter-diabatic (CD) driving provides a promising means…
Smooth bang-bang shortcuts to adiabaticity for atomic transport in a moving harmonic trap
Yongcheng Ding, Tang-You Huang, Koushik Paul +2
Bang-bang control is often used to implement a minimal-time shortcut to adiabaticity for efficient transport of atoms in a moving harmonic trap. However, drastic changes of the on-…
Retrieving Quantum Information with Active Learning
Yongcheng Ding, José D. Martín-Guerrero, Mikel Sanz +3
Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides a…
Implementation of a Hybrid Classical-Quantum Annealing Algorithm for Logistic Network Design
Yongcheng Ding, Xi Chen, Lucas Lamata +2
The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the optimal configuration of the supply chain's infrastructures an…