Reinforcement learning decoding for Hermitian AG quantum stabilizer codes
Vahid Nourozi
Abstract
Abstract We provide a corrected, consistent development of classical algebraic geometry (AG) codes and associated nonbinary quantum stabilizer codes built fromthe Hermitian curve $y^q+y=x^{q+1}$ over $\Fqsq$. This resolves earlier inconsistencies around the choice of $m$, point counts, dual indices, and field of the inner product. The curve has genus $g=\frac{q(q-1)}{2}$, $q^3{+}1$ $\Fqsq$-rational points, and a unique point at infinity; setting $D$ to be the sum of all affine $\Fqsq$-points gives length $n=\deg D=q^3$. For one-point divisors $G=rP_\infty$ we give a monomial basis for $\mathcal{L}(G)$, the standard duality formula $C_{\mathcal{L}}(D,rP_\infty)^\perp=C_{\mathcal{L}}\!\big(D,(n+2g-2-r)P_\infty\big)$, and a simple sufficient condition for Hermitian self-orthogonality: $(q+1)r\le n+2g-2$ (equivalently, $r\le q^2-2$).Under this condition, Ashikhmin--Knill yields $q$-ary stabilizer codes $[[n,n-2k,\ge d_H^\perp]]_q$ with $k=\dim C_{\mathcal{L}}(D,rP_\infty)$ and $d_H^\perp\ge r-2g+2$.We also retain a complete reinforcement-learning (RL) section: an \emph{RL-on-Greedy} decoder that first applies a greedy pass then a Deep Q-Network to resolve residual syndromes. We include the original evaluation plot and all references, and clarify that the RL case study can be applied to any stabilizer code (including, but not limited to, those from Hermitian AG codes).