refs + AG correction

codes/classical/q-ary_digits/ag/residueAG/shimura.yml

@@ -11,10 +11,13 @@ name: 'Tsfasman-Vladut-Zink (TVZ) code'
 short_name: 'TVZ'
 introduced: '\cite{doi:10.1002/mana.19821090103}'
 
-description: 'Member of a family of residue AG codes where \(\cal X\) is either a reduction of a Shimura curve or an elliptic curve of varying genus.'
+description: 'Member of a family of residue AG codes where \(\cal X\) is either Drinfeld modular curve, a classic modular curve, or a Garcia-Stichtenoth curve.'
+
+# either a reduction of a Shimura curve or an elliptic curve of varying genus.'
 
 features:
-  rate: 'TVZ codes exceed the asymptotic Gilbert-Varshamov (GV) bound \cite{doi:10.1002/mana.19821090103} (see also Ref. \cite{manual:{Y. Ihara. "Some remarks on the number of rational points of algebraic curves over finite fields." J. Fac. Sci. Univ. Tokyo Sect. IA Math., 28:721-724 (1982),1981.}}). Roughly speaking, this breakthrough result implies that AG codes can outperform random codes. Such families of codes are optimal.'
+  rate: 'TVZ codes exceed the asymptotic Gilbert-Varshamov (GV) bound \cite{doi:10.1002/mana.19821090103} (see also Ref. \cite{manual:{Y. Ihara. "Some remarks on the number of rational points of algebraic curves over finite fields." J. Fac. Sci. Univ. Tokyo Sect. IA Math., 28:721-724 (1982),1981.}}).
+  Roughly speaking, this breakthrough result implies that AG codes can outperform random codes.'
 
 relations:
   parents:

codes/quantum/qubits/qubits_into_qubits.yml

@@ -48,7 +48,8 @@ features:
   general_gates:
     - 'Universal computing can be achieved using Clifford gates and a single type of non-Clifford gate, such as the \(T\) gate \cite{arxiv:quant-ph/9503016}.
     Non-Clifford gates are typically more difficult to implement than Clifford gates and so are treated as a resource.
-    Optimizing T-gate count is \(NP\)-hard \cite{arxiv:2310.05958} and can be done using various procedures \cite{arxiv:1303.2042,arxiv:1308.4134,arxiv:1601.07363,arxiv:1710.07345,arxiv:1712.01557,arxiv:2110.10292}, e.g., ZX calculus \cite{arXiv:1903.10477,arXiv:1911.09039,arxiv:2004.05164,arxiv:2109.01076} or reinforcement learning \cite{arxiv:2402.14396}.'
+    Optimizing T-gate count is \(NP\)-hard \cite{arxiv:2310.05958} and can be done using various procedures \cite{arxiv:1303.2042,arxiv:1308.4134,arxiv:1601.07363,arxiv:1710.07345,arxiv:1712.01557,arxiv:2110.10292}, e.g., ZX calculus \cite{arXiv:1903.10477,arXiv:1911.09039,arxiv:2004.05164,arxiv:2109.01076} or reinforcement learning \cite{arxiv:2402.14396}.
+    Other decompositions (not in terms of T gates) exist \cite{arxiv:quant-ph/0404089}.'
     - 'Arbitrary \(n\)-qubit circuits can be implemented fault-tolerantly in a 3D architecture using \(O(n^{3/2}\log^3 n)\) qubits, and in a 2D architecture using only \(O(n^2 \log^3 n)\) qubits \cite{arxiv:2402.13863}.'
   decoders:
     - 'Incorporating faulty syndrome measurements can be done using the \textit{phenomenological noise model}, which simulates errors during syndrome extraction by flipping some of the bits of the measured syndrome bit string. In the more involved \textit{circuit-level noise model}, every component of the syndrome extraction circuit can be faulty.'