article · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2016
Channel-adapted quantum error correction via code concatenation
Cited by 0Abstract and details only
Abstract
Quantum error correction (QEC) is crucial for faithful transmission of quantum information and successful realization of quantum computers. Typically, QEC is designed with minimal assumptions about the noise process, i.e. depolarizing noise that happens independently on each individual particle. In real physical systems, errors are unlikely to be arbitrary. Rather we have reasonable knowledge about the system to model its quantum decoherence process. In this talk, we examine QEC that are adapted to the physical noise models. We start from an overview of the concepts and general methods for channel-adapted QEC. We then examine a technique, called code concatenation, for constructing channel-adapted quantum codes. Applying to the amplitude damping channels, we demonstrate the power of the code concatenation method for constructing good codes for channel-adapted QEC.
Cite this paper
Zeng, B. (2016). Channel-adapted quantum error correction via code concatenation. Rare & Special e-Zone (The Hong Kong University of Science and Technology).
Zeng, Bei. “Channel-adapted quantum error correction via code concatenation.” Rare & Special e-Zone (The Hong Kong University of Science and Technology), 2016.
Zeng, Bei. 2016. “Channel-adapted quantum error correction via code concatenation.” Rare & Special e-Zone (The Hong Kong University of Science and Technology).
Zeng, B. (2016) ‘Channel-adapted quantum error correction via code concatenation’, Rare & Special e-Zone (The Hong Kong University of Science and Technology).
@article{zeng2016channel,
title = {{Channel-adapted quantum error correction via code concatenation}},
author = {Zeng, Bei},
journal = {Rare & Special e-Zone (The Hong Kong University of Science and Technology)},
year = {2016}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.