AI for Science and Quantum: The Ultimate Quest

AI Can Imagine Possibilities. Can It Discover How Nature Works?

AI’s extraordinary progress in mathematics and coding draws strength from fast, reliable digital checks. But discovering a new medicine or a better material presents a different challenge: nature must supply the evidence.

In AI for Science and Quantum: The Ultimate Quest, Peter Y. Lee and Yiyong Huang explore two fundamental obstacles—and how connecting AI, experiments, and quantum computing could help overcome them.

THE PHYSICAL LIMIT

AI generates ideas quickly, but testing them in nature takes time and resources.

THE QUANTUM LIMIT

Complex quantum systems challenge classical AI. Quantum devices offer new ways to investigate them.

GUIDING FUTURE DISCOVERY

People must guide powerful research tools and use discoveries responsibly.

Through accessible explanations and examples—from protein prediction and robotic chemistry to biological models and quantum experiments—this book invites general readers to explore a new frontier of science.

The ultimate quest is to learn more from nature—and use what we learn wisely.

Peter Y. Lee holds a Ph.D. in Electrical Engineering from Princeton University. His research at Princeton focused on quantum nanostructures, the fractional quantum Hall effect, and Wigner crystals. Following his academic tenure, he joined Bell Labs, making significant contributions to the fields of photonics and optical communications and securing over 20 patents. Dr. Lee's multifaceted expertise extends to educational settings; he has a rich history of teaching, academic program oversight, and computer programming.

Yiyong Huang is an engineer and researcher with more than 25 years of experience in electronic engineering, telecommunications, and technology leadership. He has served as a chief technology officer and led product development in electronics manufacturing. He holds master's degrees in electronic engineering and data science and is pursuing a doctorate in engineering focused on artificial intelligence and machine learning at George Washington University. His research includes scheduling optimization and prediction of battery charge and health.