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Pavithran Iyer

dblp:167/0469 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Quantum computing and quantum information · 54% Coding theory · 29% Computational complexity · 18%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational complexity › counting complexity
#p-completeness
0.212015
Hardness of Decoding Quantum Stabilizer Codes · IEEE Trans. Inf. Theory 2015
Coding theory › error-correcting codes › decoding › decoding problems
decoding complexity
0.212015
Hardness of Decoding Quantum Stabilizer Codes · IEEE Trans. Inf. Theory 2015
Quantum computing and quantum information › quantum error correction
quantum code decoding
0.212015
Hardness of Decoding Quantum Stabilizer Codes · IEEE Trans. Inf. Theory 2015
Quantum computing and quantum information
quantum error correction
0.212015
Hardness of Decoding Quantum Stabilizer Codes · IEEE Trans. Inf. Theory 2015
Quantum computing and quantum information › quantum error correction
stabilizer codes
0.212015
Hardness of Decoding Quantum Stabilizer Codes · IEEE Trans. Inf. Theory 2015
Coding theory › error-correcting codes › block codes
linear code
0.112015
Hardness of Decoding Quantum Stabilizer Codes · IEEE Trans. Inf. Theory 2015
Coding theory › error-correcting codes › decoding › decoding algorithms
optimal decoding
0.112015
Hardness of Decoding Quantum Stabilizer Codes · IEEE Trans. Inf. Theory 2015

Methods — techniques the papers use, named apart from their topics

syndrome decoding · 0.2degeneracy analysis · 0.2
YearPublicationVenuePosition
2015 Hardness of Decoding Quantum Stabilizer Codes
abstract
In this paper, we address the computational hardness of optimally decoding a quantum stabilizer code. Much like classical linear codes, errors are detected by measuring certain check operators which yield an error syndrome, and the decoding problem consists of determining the most likely recovery given the syndrome. The corresponding classical problem is known to be NP-complete, and are appropriate a similar decoding problem for quantum codes is also known to be NP-complete. However, this decoding strategy is not optimal in the quantum setting as it does not consider error degeneracy, which causes distinct errors to have the same effect on the code. Here, we show that optimal decoding of stabilizer codes (previously known to be NP-hard) is in fact computationally much harder than optimal decoding of classical linear codes, it is #P-complete.
Pavithran Iyer, David Poulin
IEEE Trans. Inf. Theory1