Quantifying and Reducing the Overhead of Topological Quantum Error Correction in Large-Scale Systems

  • Martin Suchara | UC Berkeley

In this talk I will quantify the resources (number of qubits, number of quantum gates, computation time) needed to perform error correction with the surface code. Using examples of several models of quantum computation and quantum algorithms, I will demonstrate the advantages of the surface code compared to concatenated quantum error correcting codes. Although the surface code can tolerate very high error rates and uses fewer resources than concatenated codes, decoding errors requires solving a large instance of the minimum weight perfect matching problem. I will describe several simple heuristics that reduce the time complexity of error decoding to O(n), allowing us to decode errors in systems with up to million qubits on commodity hardware. I will also describe our recent progress on parallelizing the decoding algorithm.

Speaker Details

Martin Suchara is a Postdoctoral Scholar at UC Berkeley. His research interests are in quantum computation, including quantum error correction and quantum algorithms. He has been working on development of new quantum error correcting codes that have a high error correction threshold, can be efficiently decoded, and scale to large quantum computing systems. A small group of students and postdocs is working under his supervision on estimating and reducing the computational resources required to fault-tolerantly execute quantum algorithms on a realistic quantum computer. In his earlier research in the area of computer networking, Dr. Suchara utilized theoretical techniques such as optimization theory, control theory, and applied cryptography to develop new protocols that improve network performance, safety and security. He received his Ph.D. and B.S. in Computer Science from Princeton University in 2011 and the California Institute of Technology in 2006, respectively.

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      Jeff Running

Series: Microsoft Research Talks