7 citations · 7 across the 1 of their papers we have counts for
9 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…
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…
Quantum Advantage in Cryptography with a Low-Connectivity Quantum Annealer
Feng Hu, Lucas Lamata, Chao Wang +3
The application in cryptography of quantum algorithms for prime factorization fostered the interest in quantum computing. However, quantum computers, and particularly quantum annea…
Towards Pricing Financial Derivatives with an IBM Quantum Computer
Ana Martin, Bruno Candelas, Ángel Rodríguez-Rozas +6
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates. Several stochastic d…