Danyang's Teahouse

Research Experiences

A Two-year Summary

I joined the Laboratory of Integrated Photons and Quantum Information (IPQI) when I was a sophomore, and with the guidance of my adviser Prof. Xian-Min Jin and some doctors, I took part in several researches and started my own project on Characterization of any linear optical devices using gradient descent method. Besides, I did some research on the Neural Network and quantum computation as well. Currently, I’m under the guidance of Carlos Navarrete-Benlloch, I’m researching theoretically on superconducting qubit systems. Now I’m interested in Quantum Engineering and Quantum Optics, which will be my main research target for my Ph.D in the future.

List of Experiences

My research experiences are listed in the order of importance.

Quantum Van der Pol model

Aug. 2020 – Present
Advisor: Carlos Navarrete-Benlloch

  • Establishing a Quantum Van der Pol oscillator based on superconducting qubit and exploring its physics properties.
  • Details are not available now.

Characterization on Linear Photonic Chips with Machine Learning

Sep. 2019 – Present
Advisor: Xian-Min Jin

  • Simulated Boson Sampling with python.
  • Established an integrated optimizing model with multiple constraints on it, which reduced the error of traditional methods significantly.
  • Experimentally tested the model with a 15-output integrated photonic chip.

Research on the Learning Process of Neural Networks (NN)

Sep. 2019 – Feb. 2020
Advisor: Zhi-Qin Xu

  • Learned to build arbitrary NN models with TensorFlow.
  • Revealed the different learning processes between Deep Neural Networks (DNNs) and Convolution Neural Networks (CNNs).
  • Analyzed the most probable image of DNNs and visualized the convolution kernel. Observed their changing features quantitatively and test the validity of the Frequency Principle in two models.

Other Contributions

Review of Quantum Algorithms: Investigated algorithms based on Quantum Phase Estimation (HHL, QAE, and QPCA) and algorithms related to data science (Quantum Autoencoder and QGAN). The reviews compose three chapters of the book Quantum Computing Technologies, which is about to be published by Shanghai Jiao Tong University Press in 2021. (Aug. 2020 – Present, Adviser: Hao Tang)

Algebraic Machine Learning (AML) & N Queens Problem: Studied AML algorithms and realized its graph representation in python scripts.

Shining Light on Quantum Transport in Fractal Networks: Compared the simulating and experimental result of quantum walk on fractal waveguide arrays, processed the data, and helped writing the paper.

Reconstruction of Quantum Channel via Convex Optimizing: Conducted optical experiments and analyzed the data.

Recognition of Anderson Localization with Machine Learning: Simulated quantum walk on waveguide arrays of different shapes (Square, triangle, hexagon…) with MATLAB and analyzed the evaluation patterns of result with ML.

Last update: Dec. 2020