MindSpore Quantum | Interview with Excellent Developer Xie Qingxing: Do What You Love, Love What You Do

2022/03/16

MindSpore Quantum | Interview with Excellent Developer Xie Qingxing: Do What You Love, Love What You Do

MindSpore Quantum | Interview with Excellent Developer Xie Qingxing: Do What You Love, Love What You Do

March 16, 2022

Interview with an Excellent Developer

Today, we interviewed Xie Qingxing, a winner of the Most Promising Award of Open Source Promotion Plan Summer 2021, "Do what you love, love what you do".

Give a Brief Introduction of Yourself.

My name is Xie Qingxing. Because of my interests in programming and mathematical physics, after I graduated I enrolled as a PhD student at Wuhan University. My research direction is quantum computing in quantum chemistry. In thisopen source program, I picked the MindSpore Community project, A Quantum Neural Network for Calculating the Ground State and First Excited State Energy of a Molecule because it perfectly matched my research interest.

What are your learning methods and tips to improve coding?

Before coding, construct the complete logic of the entire algorithm. Remember, all code is made up of smaller, simpler sub-modules. Leave comments on each sub-module so that you or other readers know the complete code logic. Also, use expressive names for variables to make your code easy to read.

When did you start participating in open source projects? And what is your favorite open source project?

I first heard the open source concept during my undergraduate studies, but this is my first time to be part of an open source project.

My favorite open source project is the current project, MindSpore Quantum, a quantum machine learning on MindSpore to perform quantum programming and simulations. MindSpore Quantum has greatly facilitated my research.

Have you ever participated in other similar competitions?

I took part in the 2021 National Quantum Computing Hackathon in Shanghai University jointly held by Huawei and Shanghai University. Our team, WHU-CCNSL, won the second prize. It was an intensely difficult period, as we had to learn a lot new and complicated content before solving two problems. But the experience was worthwhile, and we found a sense of accomplishment through the process.

I also participated in the HUAWEI CLOUD Quantum Computing Paper Reproduction Contest this year, which was my first taste of state-of-the-art quantum computing research. With help from my mentor, I implemented the adapt-VQE algorithm based on MindSpore Quantum. But, the best thing about this contest is the group of like-minded friends I met. We keep in touch, always communicating with and learning from each other. The flow and sharing of ideas helps open our horizons and inspire us to improve our work. I would like to thank the organizers for providing us such a platform.

Tell us about your open source project in Summer 2021.

My topic for this open source program is A Quantum Neural Network for Calculating the Ground State and First Excited State Energy of a Molecule.

Computational chemistry, as the name implies, is a branch of chemistry that uses computational simulations to assist in solving chemical problems based on the basic physical laws of quantum mechanics.

However, solving or explaining chemical problems using computational simulations and basic theories requires both premium algorithms and huge computing power.

In general terms, the development of quantum chemistry is closely related to that of computers. Because of the huge computing workloads involved in computational chemistry, classical computers fail to deliver the performance needed for computational precision and complexity, limiting the computational chemistry.

That's why we use quantum computers.

Currently, chemical simulations can be implemented on quantum computers by using the variational quantum eigensolver, or VQE. In this project, we run a VQE algorithm on MindSpore Quantum and perform numerical simulations to calculate the energy of the ground state and first excited state of a molecule. The simulation result is then compared with that of full configuration interaction (FCI) to assess the reliability of the VQE algorithm.

What was the biggest challenge during the project? How did you solve the challenge and what did you learn?

This project was difficult, requiring me to both understand the theory, including quantum computing and quantum chemistry, in the research papers, and perform complex, time-consuming programming to implement the algorithm.

Here, I would like to thank my mentor, who guided and enabled me to start the project. Sometimes a few words from my mentor would point me in the right direction. If I encountered bugs that I couldn't solve, my mentor would patiently help me troubleshoot the problem and find a solution.

My coding efficiency has greatly benefited from communication with others, especially my mentor.

Do you think participating in open source projects is helpful to scientific research?

I can't overstate how helpful this open source project has been to my study and future career. It expanded my knowledge, and helped me build up my tech stack and improve coding and modularization abilities. All these are going to help me with my current and future research.

What do you think of open source and open source communities after Summer 2021?

Individuals and teams can gain experience and influence through open source projects. In my opinion, you shouldn't code alone, and must let others help you review the code with fresh eyes, so that you don't run into brick walls. The ultimate purpose of open source is to allow people around the world to work together. That's why, we will harness the strengths of every contributor and use the knowledge in this 'hive mind' to jointly promote the continuous progress of open source projects.

How can a student go from an open source user to a contributor?

Interest is the key. If you love coding, then it's easy to get started. Choose an open source project that suits you, join in the challenges, and keep participating in it.