[Joint CQSE & NCTS Seminar] Quantum Computing Applications: From Theory to Real-World Scenarios

• Event Date: 2025-09-12
• Chen, Jhan Yu 陳詹喻
• Quantum Information and Quantum Computing Quantum computing and interdisciplinary applications
• Speaker: Dr. Ming-Fong Sie (SuDo Research Labs)  /  Host: Prof. Hsi-Sheng Goan
  Place: Rm. 104, Chin-Pao Yang Lecture Hall, Department of Physics/CCMS, NTU

Title: [Joint CQSE & NCTS Seminar] Quantum Computing Applications: From Theory to Real-World Scenarios
Speaker: Dr. Ming-Fong Sie (SuDo Research Labs)
Time: 2025/09/12 (Fri.) 14:20-16:20
Place: Rm. 104, Chin-Pao Yang Lecture Hall, Department of Physics/CCMS, NTU
Online: https://nationaltaiwanuniversity-zbh.my.webex.com/nationaltaiwanuniversity-zbh.my/j.php?MTID=m35c57ceb3c91fd5af3ca30d927e989a1
 

Abstract

Quantum computing holds the potential to revolutionize various fields by solving complex optimization problems beyond classical capabilities. This talk will explore key application scenarios of quantum computing, with a focus on quantum annealing for tackling challenges in Bitcoin transaction data analysis and large-scale optimization tasks. Drawing from recent research, we will delve into quantum-inspired algorithms for identifying high-risk Bitcoin addresses, such as mixers, using simulated annealing and quantum annealing to efficiently select features in complex solution spaces. Over 900 million Bitcoin transactions pose significant challenges for traditional machine learning in terms of computation time and accuracy. Our approach demonstrates a 30.3% reduction in training time with a random forest model while maintaining a 91% F1-score for mixer detection. The discussion will also explore challenges in applying quantum annealing to large-scale data, such as balancing computational efficiency with prediction accuracy, and strategies to overcome these hurdles. We will highlight future prospects for quantum annealing in financial fraud detection demonstrating its potential to transform data-driven industries. Attendees will gain insights into how quantum annealing enables efficient, high-accuracy solutions for complex optimization problems.
 

Biography

Ming-Fong Sie is a researcher at SuDo Research Labs, specializing in quantum-inspired algorithms, blockchain, and machine learning. He earned his Ph.D. in Computer Science and Information Engineering from National Taiwan University, where his research focused on algorithmic optimization and machine learning for complex systems. Prior to joining SuDo Research Labs, Dr. Sie contributed to international projects in blockchain, developing novel approaches for transaction pattern analysis. His recent work includes efficient feature selection methods for Bitcoin transaction classification using quantum annealing techniques, as published in leading journals. Dr. Sie is passionate about translating quantum theories into real-world solutions, particularly in FinTech and cybersecurity, and actively collaborates with academic and industry partners to advance quantum  interdisciplinary research.