Quantum algorithms claim to outperform classical algorithms by harnessing computational and communication properties unique to quantum systems, such as superposition and entanglement, e.g., Shor's factoring algorithm and Grover's algorithm. Do quantum algorithms live up to the claims? Google's quantum supremacy announcement in 2019 has received broad questions from academia and industry due to the debatable estimate of 10,000 years' running time for the classical simulation task on the Summit supercomputer. Has “quantum supremacy" already come? Or will it come in one decade later?   We take a reinforcement learning approach for the classical simulation of quantum circuits and demonstrate its great potential by reporting an estimated simulation time of less than 4 days, a speedup of 5.40x over the state-of-the-art method.  Specifically, we utilize a tensor network approach and employ a deep reinforcement learning algorithm.    Bio: Xiao-Yang (Yanglet) Liu joined RPI's CS department as a lecturer in September 2023. He holds Ph.D. and M.S. degrees in the Department of Electrical Engineering at Columbia University in 2023 and 2018, respectively. His research interests include quantum computing and tensor networks, deep reinforcement learning, and model-openness framework (licenses) in AI. Xiao-Yang has authored chapters to two graduate textbooks:  tensors for data processing, and reinforcement learning for cyber-physical systems. His papers got over 4400 citations, three of which are ESI-highly cited papers. He received NeurIPS scholar award 2022/2023 and ICAIF-JPM award 2022/2023. He is an academic member of Linux Foundation, LF AI & Data, FinOS, and CRAFT, collaborating on open-source projects FinGPT and FinRL.  As a (senior) PC member, he serves leading AI conferences such as NeurIPS, ICML, ICLR, AAAI, and ACM ICAIF. Xiao-Yang has chaired sessions at IJCAI 2019 and has been a leader organizer of multiple workshops and academic competitions, including NeurIPS 2020/2021 First/Second Workshop on Quantum Tensor Networks in Machine Learning (QTNML), ACM ICAIF FinRL competition 2023, and IJCAI 2020 Workshop on Tensor Networks Representations in Machine Learning.