7 citations · 7 across the 2 of their papers we have counts for
13 papers · 1 filter
Scrambling in the Charging of Quantum Batteries
Sebastián V. Romero, Yongcheng Ding, Xi Chen +1
Exponentially fast scrambling of an initial state characterizes quantum chaotic systems. Given the importance of quickly populating higher energy levels from low-energy states in q…
Pulse-based variational quantum optimization and metalearning in superconducting circuits
Yapeng Wang, Yongcheng Ding, Francisco Andrés Cárdenas-López +1
Solving optimization problems using variational algorithms stands out as a crucial application for noisy intermediate-scale devices. Instead of constructing gate-based quantum comp…
Quantum Active Learning
Yongcheng Ding, Yue Ban, Mikel Sanz +2
Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Trainin…
Exploring Ground States of Fermi-Hubbard Model on Honeycomb Lattices with Counterdiabaticity
Jialiang Tang, Ruoqian Xu, Yongcheng Ding +6
Exploring the ground state properties of many-body quantum systems conventionally involves adiabatic processes, alongside exact diagonalization, in the context of quantum annealing…
Dropout is all you need: robust two-qubit gate with reinforcement learning
Tian-Niu Xu, Yongcheng Ding, José D. Martín-Guerrero +1
In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal design for experim…
Active Learning in Physics: From 101, to Progress, and Perspective
Yongcheng Ding, José D. Martín-Guerrero, Yolanda Vives-Gilabert +1
Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled sa…