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Q&A With Post-Quantum Computing Cryptography Researcher Jintai Ding

How quantum computers threaten our current cryptography system and what we can do about it

4 min read

Quantum computers may be the perennial ”computer of the future,” but if (or when) they do become a reality, their sheer power could threaten the security of our information-technology infrastructure. Online shopping, e-mail, and automatic software updates rely on public-key cryptography methods to ensure those transactions are safe. The two main methods of public-key cryptography are RSA, which is based on an algorithm that relies on the difficulty of factoring large numbers, and elliptic-curve cryptography (ECC), which is based on the mathematical structure of elliptical curves. But a quantum computer could quickly crack either cryptosystem. In October, the University of Cincinnati hosted an international cryptography conference with industry and government experts to address this very problem.

IEEE Spectrum’s Monica Heger talked to Jintai Ding, conference cochair and professor of mathematics at the University of Cincinnati, about the possible solutions to the quantum computer problem.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read
Image of a computer rendering.

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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