Efficient Fault-Tolerant Quantum Computing


Martin Suchara, AT&T Labs Research - USA -- 08-09-2015


Some of the greatest challenges that must be resolved in order to build an efficient quantum computer are in the area of quantum error correction. Quantum error correction techniques enable the use of unreliable physical technologies to successfully run quantum algorithms. Because quantum hardware is prone to errors, and these errors corrupt delicate quantum states rather than binary information, the known error correction techniques have a high overhead.

In my talk I first introduce the two main families of quantum error correcting codes and quantify their overhead using specific examples of algorithms and hardware technologies. Then I describe several new techniques that help to reduce this overhead. For example, our maximum likelihood decoder (MLD) is an efficient algorithm that finds the recovery operation that maximizes the probability of a successful error correction given the observed error syndrome. Numerical simulations of the MLD algorithm for physical error rates around 10% showed a 100 fold reduction of the logical error probability compared to earlier techniques. I also show new designs of error correcting codes that are tailored to work more efficiently with specific physical technologies.


Martin Suchara is a Principal Member of Technical Staff at AT&T Labs Research since 2015. Prior to joining AT&T he was a Postdoctoral Scholar in the quantum computation group at IBM T. J. Watson Research Center. His work focuses on making computation with quantum computers more efficient and reliable. He developed new quantum error correcting codes that improve error decoding efficiency. Martin received his PhD from the Computer Science department at Princeton University and postdoctoral training from UC Berkeley. Between 2011 and 2013 he coordinated the work of a small group of postdocs and students on the IARPA QCS Program and delivered the results to the Program Manager. Martin is the recipient of the Best Student Paper Award at ACM Sigmetrics 2011.


Will be available soon

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