Quotient Space Quantum Codes
November 13, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jing-Lei Xia
arXiv ID
2311.07265
Category
quant-ph: Quantum Computing
Cross-listed
cs.IT
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Additive codes and some nonadditive codes use the single and multiple invariant subspaces of the stabilizer G, respectively, to construct quantum codes, so the selection of the invariant subspaces is a key problem. In this paper, I provide the necessary and sufficient conditions for this problem and, establish the quotient space codes to construct quantum codes. These new codes unify additive codes and codeword stabilized codes and can transmit classical codewords. Actually, I give an alternative approach to constructing union stabilizer codes, which is different from that of Markus Grassl and Martin Roetteler, and which is easier to deal with degenerate codes. I also present new bounds for quantum codes and provide a simple proof of the quantum Singleton bound. The quotient space approach provides a concise and clear mathematical framework for the study of quantum error-correcting codes.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Quantum Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Quantum machine learning: a classical perspective
R.I.P.
π»
Ghosted
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
R.I.P.
π»
Ghosted
ProjectQ: An Open Source Software Framework for Quantum Computing
R.I.P.
π»
Ghosted
Quantum Recommendation Systems
R.I.P.
π»
Ghosted
Traffic flow optimization using a quantum annealer
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted