Dr.TANG-YOU HUANG
I am currently a postdoctoral researcher at Chalmers University of Technology, working with Dr. Giovanna Tancredi and Prof. Gheorghe Sorin Paraoanu on the field of superconducting quantum computation.
Biography
I completed my Ph.D. at Shanghai University in 2022 under the guidance of Prof. Xi Chen. Additionally, I had the privilege of being a joint Ph.D. student with support from the CSC national scholarship at the University of Basque Country in 2021, where I was supervised and guided by Prof. Evgeny Sherman. In addition to my academic journey, for a period of ten months, I worked with Prof. Xiaopeng Li at the Shanghai Qizhi Institute. Outside of academia, I have a passion for outdoor activities, particularly hiking and skiing. In the realm of leisure, I find joy in computer games, with a particular affinity for "League of Legends." I look forward to the opportunities that lie ahead in combining my academic field with my personal interests.
Research interests
Optimal Control Theory and Shortcuts to Adiabaticity; Machine-learning-assisted Quantum Information Processing; Quantum Computing and Quantum Algorithms
1. Machine-learning-assisted Quantum Control
The deployment of machine learning, as a forefront computational methodology, has become pervasive within the domain of quantum science and technologies [RMP 91, 045002 (2019)]. In the context of quantum control, the overarching objective is to manipulate a quantum system while preserving coherence, a formidable task complicated by decoherence effects within noisy environments [PRX 12, 011059 (2022)]. My research interest lies in the application of machine learning techniques to quantum control problems for systems encompassing superconducting circuits, cold atoms, and other pertinent quantum domains characterized by multifaceted controllability.
The Workflow of ML-assisted quantum control in quantum Computing.
The workflow of VQA-based quantum control.
2. Variational Quantum Algorithms
Variational quantum algorithms (VQAs) [NRP 3, 625–644 (2021)] leverage deep-depth quantum circuits and classical optimizers. The formal facet of these algorithms involves encoding intractable problems, whether of quantum or classical aspects, into a sequence of gate operations on a qubit register. The subsequent component is meticulously designed to discern solutions to complex problems, ranging from dynamic simulations for many-body systems to the resolution of optimization challenges. My specific interest lies in the intersection of quantum control problems and variational quantum algorithms. Furthermore, my emphasis extends to the optimal realization of these applications within the framework of a superconducting quantum computer.
3. Shortcuts-to-Adiabaticity and Optimal Control Theory
Shortcuts-to-Adiabaticity (STAs) [RMP 91.045001 (2019)] refers to a set of techniques employed in quantum mechanics to achieve results similar to those obtained through adiabatic processes, but in a significantly shorter time (or with less energy cost). Optimal Control Theory is a mathematical framework used to find the optimal set of control parameters that steer a system from one state to another in the most efficient way possible. The concept of STA, i.e., counter-diabatic, motivates fields ranging from quantum control, quantum sensing, and quantum algorithms. My primary interest lies in converting the idea of STAs into various quantum applications.
A turtle on wheels is a good metaphor for shortcuts to adiabaticity.
Publication
See my publications on arXiv and Google Scholar.
Feel free to contact me by Email: tangyou@chalmers.se.