Get into the unfamiliar water - My journey from quantum and AI newbie to an experienced researcher

Around the end of August 2022, I got a call from Seth Harding (Co-founder of Tokai, a researcher at National Taiwan University). He said, "Hi Mark, my professor got a research program on quantum computing. Would you be interested in this? We have a meeting next week...". After the end of that call, my journey to quantum computing and quantum AI began. I attended the research meeting where I met the research partners from the physics department at NTU. They discussed how a quantum walk algorithm would be advantageous in simulating difficult probability distributions. It was just a series of sounds for me at the beginning. But it started to make sense after jumping into the field of quantum computing; I read books, read lectures, and worked on various demo programs. I tried my best to demonstrate my value to the team.

That day came after the research lead on the team asked me if I would like to introduce a quantum convolutional network initially introduced back in 2018 by a researcher at Harvard University. And I said, "Yes, sure. That is a great idea; when should I present the talk?". About weeks later, I introduced the core concept of quantum convolutional neural network (QCNN) to the team members with a background education in mathematics, computer science, and physics. It was nerve-wracking, but it was one of the best memories and lessons learned in this program. Later, I started to develop virtual quantum circuit optimization algorithms to improve the performance of our quantum algorithm.

One of the biggest lessons I learned from this experience is that we shouldn't think of something hard so that I can't get into this field. Instead, we should consider these difficulties as something cool to tackle, not something to be afraid of. I admit that quantum computing and quantum physics are both challenging subjects, but with the growth mindset, our brain is trainable to learn something we think is too hard. Instead of being imitated constantly, learn to spot hidden patterns in the field and try to tackle them step by step.

Later in this research program, I spontaneously worked on various research projects ranging from the quantum reinforcement learning problem, which is now published by the Quantum Machine Intelligence journal, to the quantum machine learning model that has demonstrated a great quantum machine learning utility. Activeness really plays a significant role in my success in this research domain. Don't wait for instructions, seek for the questions and problems that is valuable and trying to solve it.

Deep Q-learning with hybrid quantum neural network on solving maze problems - Quantum Machine Intelligence

Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches’ performance, advantages, and disadvantages to deep Q-learning problems, especially on larger-scale maze problems larger than 4 $$\times $$ × 4.

SpringerLinkHao-Yuan Chen

Ultimately, I am genuinely grateful for my peers and Seth, who helped me get into this field. My journey on quantum AI has just begun, but there is a long path forward. But I believe that, with the process of building, learning, and iterating combined with the what-if mentality, I will continue to thrive and learn in this field.

My Google Scholar profile:

Mark Chen

University of London - Cited by 4 - Quantum Machine Learning - Quantum-inspired Computing - Quantum Utility - Objective-driven AI

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