Convergence: Artificial Intelligence and Quantum Computing: Social, Economic, and Policy Impacts
Quantum: A New Kind of Knowledge Discovery
November 3, 2022
While the first solid-state device (known as transistor) was being developed at Bell lab in the mid-twentieth to replace vacuum-tubes, 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). 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 predomination, the era of Moore’s law is drawing to a close. Are we prepared for the end of Moore’s law? Can digital systems keep pace with the ever-increasing demand for data storage and information processing capacity? The microelectronics industry (will be known as the nanoelectronics industry in the near term) trying to identify new materials and devices to replace the 50 years old transistor technology—including, but not limited to, non-classical CMOS (such as new channel materials), and alternatives to CMOS (e.g., spintronics, single-electron devices, and molecular computing). Although the microelectronics industry will continue to reduce electronic devices’ costs, there are theoretical boundaries that can limit future micro/nano-electronic processing devices’ computing power. However, it is worth noting that AI applications can still benefit from the dramatic increase in memory systems’ speed and storage capacity.
Ramin Ayanzadeh and Milton Halem, Quantum: A New Kind of Knowledge Discovery, in Convergence: Artificial Intelligence and Quantum Computing: Social, Economic, and Policy Impacts, Greg Viggiano (Ed.), Wiley, November 2022.
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