122 citations · 330 across the 19 of their papers we have counts for
19 papers
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…
Weak-valued correlation functions: Insights and precise readout strategies
Yuan Feng, Xi Chen, Yongcheng Ding
The correlation function in quantum systems plays a vital role in decoding their properties and gaining insights into physical phenomena. Its interpretation corresponds to the prop…
Superoscillating Quantum Control Induced By Sequential Selections
Yongcheng Ding, Yiming Pan, Xi Chen
Superoscillation is a counterintuitive phenomenon for its mathematical feature of ``faster-than-Fourier", which has allowed novel optical imaging beyond the diffraction limit. In t…
Time-Optimal Quantum Driving by Variational Circuit Learning
Tangyou Huang, Yongcheng Ding, Léonce Dupays +4
The simulation of quantum dynamics on a digital quantum computer with parameterized circuits has widespread applications in fundamental and applied physics and chemistry. In this c…
Quantum Stream Learning
Yongcheng Ding, Xi Chen, Rafael Magdalena-Benedicto +1
The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery usin…
Active Learning for the Optimal Design of Multinomial Classification in Physics
Yongcheng Ding, José D. Martín-Guerrero, Yujing Song +2
Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according…
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…
Towards Prediction of Financial Crashes with a D-Wave Quantum Computer
Yongcheng Ding, Javier Gonzalez-Conde, Lucas Lamata +7
Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can guarantee to find optimal solutions effici…
Experimental Implementation of a Quantum Autoencoder via Quantum Adders
Yongcheng Ding, Lucas Lamata, Mikel Sanz +2
Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information. R…