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Feature Article

Feature Article

An Outlook for Quantum Computing

Proceedings of the IEEE recently published an overview of the present and future status of quantum computing, by Dmitri Maslov, Yunseong Nam, and Yungsang Kim, of the US National Science Foundation and IonQ, Inc.

Read the overview here

This work presents a qubit technology associated with trapped ions coupled by optical pulses. While this technology is different from the superconducting integrated circuit approach being pursued by other projects, it has advantages in not requiring deep cryogenic temperatures for operation, and also offers long coherent times. The ion traps can be microfabricated on a chip, as shown in the figure.

Current quantum computing technologies are noisy intermediate-scale quantum systems (NISQ), which cannot carry out desired quantum algorithms without quantum error correction, which is not yet available. The next major step is to demonstrate that a quantum computer can be used to solve a problem of practical utility that cannot otherwise be addressed, such as various kinds of quantum simulations. The transition of the proof-of-concept devices to useful computational systems faces a set of new technical challenges, ranging from improving and expanding qubit hardware to developing control/operating systems to innovations in algorithms and applications.

This issue of Proceedings of the IEEE also contains a set of other articles on alternative modes of computing. See here for the Table of Contents.

Technology Spotlight

Technology Spotlight

The Era of AI Hardware

At the recent IEEE Industry Summit on the Future of Computing, held in Washington DC as part of IEEE Rebooting Computing Week, one of the keynote talks was by Dr. Mukesh Khare, Vice President of Semiconductor Research, IBM Research. A brief introductory video of IBM’s efforts in this field is given here. Dr. Khare’s talk is available here.

The concurrent evolution of broad AI with purpose-built hardware will shift traditional balances between cloud and edge, structured and unstructured data, and training and inference. Distributed deep learning approaches, coupled with heterogeneous system architectures effectively address bandwidth, latency, and scalability requirements of complex AI models. Hardware, purpose-built for AI, holds the potential to unlock exponential gains in AI computations. IBM Research is making further strides in AI hardware through the use of Digital AI Cores using approximate computing, non-Von-Neumann approaches with Analog AI Cores, and the emergence of quantum computing for AI workloads.

Videos of other Industry Summit 2018 invited talks are available from IEEE.tv.