Research

My research focuses on Quantum Machine Learning, Quantum Utility, LLM Reasoning, and LLM Agents. You can find my complete publication list on Google Scholar.

Research Interests

Quantum Machine Learning

Exploring the intersection of quantum computing and machine learning algorithms.

Quantum Utility

Investigating practical applications and advantages of quantum computing.

LLM Reasoning

Advancing reasoning capabilities in large language models.

LLM Agents

Developing autonomous agents powered by large language models.

Notable Publications

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

Hao-Yuan Chen, Yen-Jui Chang, Shih-Wei Liao, Ching-Ray Chang • Quantum Machine Intelligence (2024) • 20 citations

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 4x4.a

A novel approach for quantum financial simulation and quantum state preparation

Yen-Jui Chang, Wei-Ting Wang, Hao-Yuan Chen, Shih-Wei Liao, Ching-Ray Chang • Quantum Machine Intelligence (2024) • 9 citations

Quantum state preparation is crucial in quantum computing for various applications, notably in quantum simulation where a quantum state must represent the simulated system. This study unveils the multi-Split-Steps Quantum Walk (multi-SSQW), an advanced simulation algorithm using parameterized quantum circuits (PQC) and a variational solver to manage complex probability distributions. Enhanced from the traditional Split-Steps Quantum Walk (SSQW) to include multi-agent decision-making, the multi-SSQW is adept at financial market modeling. It leverages quantum computation to accurately model intricate financial distributions and scenarios, offering key insights for financial analysis and strategic decisions. The algorithm’s flexibility, reliable convergence, and rapid computation make it a powerful tool for fast-paced financial market predictions.