Convergence: Artificial Intelligence and Quantum Computing

CHAPTER 6A New Kind of Knowledge Discovery

Ramin Ayanzadeh, Post-doctoral Fellow, Georgia Institute of Technology; and Milton Halem, Professor, University of Maryland Baltimore County

At the same time as the first solid-state device (the transistor) was being developed at Bell Labs in the mid-20^th century to replace vacuum tubes,^[1] artificial intelligence (AI) was being conceptualized by a generation of scientists, mathematicians, and philosophers. In 1950, Alan Turing suggested two criteria for machine intelligence: memory for enabling machines to store and retrieve data, and reasoning (i.e., having the capacity to process data).^[2] Since then, trends in doubling the transistor count, characterized by Moore's law, have catalyzed AI advancements. Nowadays, AI applications have access to not only large-scale memories but also high-performance computing (HPC) resources.

After decades of predominance, the era of Moore's law seems to be drawing to a close. Are we prepared for the end of this era? Can digital systems keep pace with ever-increasing demand for data storage and information processing capacity? The microelectronics industry (soon to be known as the nanoelectronics industry) is trying to identify new materials and devices to replace the 50-year-old transistor technology—including, but not limited to, nonclassical complementary metal-oxide-semiconductor (CMOS, such as new channel materials) and alternatives to CMOS (e.g., spintronics, single-electron devices, and molecular ...