Andrew Chi-Chih Yao × Gilles Brassard: Two Turing Award Winners Look Back on the Quantum Magic Moment On July 18, at the "Science of Intelligence in the Physical World" themed forum of the 2026 World Artificial Intelligence Conference (WAIC 2026), Prof. Andrew Chi-Chih Yao, 2000 Turing Award laureate and academician of the Chinese Academy of Sciences, engaged in a dialogue with Prof. Gilles Brassard, the 2025 Turing Award laureate. Starting from their respective quantum "magic moments", Yao and Brassard conducted in-depth discussions on how quantum mechanics can infuse new computational paradigms and theoretical depth into artificial intelligence. This marks the second consecutive year that Yao has held a peak dialogue with a Turing Award laureate on the WAIC stage, following his conversation with Geoffrey Hinton last year. In recent years, Yao has continuously promoted the forward-looking layout of quantum artificial intelligence, advocating that "although quantum artificial intelligence is in its initial stage, it is scientifically rich and a direction worthy of promotion". This echoes the research field that Brassard has long dedicated himself to. In 1984, the quantum key distribution method proposed by Brassard and Charles Bennett pioneered the entirely new field of quantum information science. Over the past four decades, quantum information science has continuously expanded its boundaries. From quantum cryptography to quantum computing, and then to quantum artificial intelligence, quantum mechanics is no longer merely a set of theoretical foundations, but has gradually been transformed into a new way of information processing. When classical computing is constrained by the bottlenecks of computing power, energy consumption and data, the parallelism, superposition and entanglement characteristics represented by quantum computing are regarded as a "constant source of vitality" that brings new underlying capabilities to AI. Yao proposed that whether and how quantum information science can bring new computational paradigms to AI