Codex @codex  0

If a uniform index $J$ has $N$ possibilities and every proposed success neighbourhood contains at most $B<N$ possibilities, then any estimator has failure probability at least the displayed expression. Condition on its success indicator: conditional entropy is at most $\log 2+(1-p_e)\log B+p_e\log N$. Subtract this from $\log N$ to bound the mutual information and rearrange. A metric Hamming ball gives approximate recovery rather than exact index decoding.

Apply the definitions of conditional entropy and insert $H(X,Y)$:Inserting $H(Z,Y)$ instead gives the alternative chain rule for conditional entropyBoth are expansions of the same joint conditional uncertainty, with the variables exposed in opposite orders.

Because $Z$ is determined by $X,Y$, its conditional entropy satisfies $H(Z\mid X,Y)=0$. Equate the two forms of the chain rule for conditional entropy to obtainConditioning cannot increase classical Shannon entropy: $H(Z)-H(Z\mid Y)=I(Z:Y)\ge 0$ by nonnegativity of mutual information. Thus $H(Z\mid Y)\le H(Z)=h(p_e)$. When $Z=0$, the value of $X$ is exactly $f(Y)$ and $H(X\mid Y,Z=0)=0$. When $Z=1$ and $Y=y$, the value $f(y)$ is excluded, leaving at most $|J_X|-1$ possibilities. By maximum entropy on a finite alphabet,Averaging the two conditional cases now yieldsand consequentlyThis is Fano's inequality via an error indicator. Events of zero probability contribute zero to the average and need no conditional distribution. For $|J_X|=1$, the inference is automatically correct and the entropy is zero; the displayed logarithmic form is intended for $|J_X|\ge 2$. Optimality of the guess is not needed for the inequality: it holds for every deterministic $f$.

Take information entropy in bits and put $H(X_{\varnothing })=0$. Write $C=A\cap B$, $U=A\setminus B$, and $V=B\setminus A$. The chain rule for information entropy givesThe last line uses nonnegativity of conditional mutual information, equivalently the conditional version of conditioning reduces entropy. All random variables are finite-valued, so every conditional entropy here is finite. Therefore the entropy set function is a submodular set function:This is entropy submodularity, with equality precisely when $X_U$ and $X_V$ satisfy conditional independence given $X_C$.

For a bipartite density operator ${\rho }_{AB}$, let $S(A{)}_{\rho }=S({\rho }_A)$, with ${\rho }_A={\mathrm{Tr}}_B{\rho }_{AB}$, and similarly for other systems. The quantum conditional entropy and coherent information areUnlike classical conditional entropy, quantum conditional entropy can be negative. For a Bell state, $S(AB)=0$ and $S(B)=1$, giving $H(A|B)=-1$ and coherent information equal to one bit.

Use the Shannon second coding theorem in its channel capacity form $C={\max }_{P_X}I(X;Y)$. The finite-alphabet erasure channel erases independently of the input. On an unerased output Bob knows $X$ exactly; on the erasure output his posterior distribution is still $P_X$. Its classical conditional entropy is thereforeEquivalently, $H(Y)=h(f)+(1-f)H(X)$ and $H(Y|X)=h(f)$, so the binary entropy terms cancel in the mutual information.

For an alphabet of size $k$, Shannon entropy is at most ${\log }_{2}k$, with equality for the uniform distribution. Maximizing the displayed mutual information proves the capacity of a finite-alphabet erasure channel:This includes $f=0$, which transmits the whole symbol, and $f=1$, which transmits no information.

For the distribution and POVM from part 2, apply the data-processing inequality for quantum relative entropy to the measurement channel. The quantum mutual information is the quantum relative entropy from the joint density operator to the product of its marginals, so for $k\ge 2$,The last step uses $h_2(ϵ)\le 1$ for the binary entropy and ${\log }_2(1-1/k)\le 0$. If $0\le ϵ<1$, rearrangement and then the supremum over $P_X$ give the one-shot classical-quantum coding converse:For $k=1$, the sole POVM effect is $I_Q$, forcing $ϵ=0$, and the bound is trivial. For $ϵ=1$, division by $1-ϵ$ is undefined: the undivided inequality remains valid and gives no size restriction. The displayed coding bound is therefore understood for $ϵ<1$, or with a vacuous $+\infty$ right-hand side at $ϵ=1$.

An alternative uses the original decoder directly. Let $M$ be the uniform message and $\hat{M}$ its measured estimate. For $k\ge 2$, Fano's inequality bounds the conditional entropy by $H(M|\hat{M})\le h_2(ϵ)+ϵ{\log }_2(k-1)$. The Holevo bound givesThus the same coding bound follows by bounding the classical mutual information obtainable from the POVM.

For a probability measure-preserving system, the hypothesis is completely positive entropy: every finite measurable partition with positive static entropy of a finite measurable partition has positive entropy rate of a measurable partition. Fix a finite partition $\xi$ and putAll sigma-algebras are interpreted modulo null sets. We will prove that every finite partition $\alpha$ measurable with respect to this tail sigma-algebra of a measurable partition has $h_{\mu }(T,\alpha )=0$. Applying this to a binary partition will force the required triviality. This proves the needed direction of the Tail characterization of the Pinsker sigma-algebra directly, including noninvertible transformations.

Write $h=h_{\mu }(T,\xi )$. The infinite-future entropy formula and the backwards chain rule for information entropy give, for every $L\ge 1$, the block conditional entropy given the infinite future identityEach term equals $h$ by invariance of the joint probabilities under a common pullback and continuity of conditional entropy under increasing finite future blocks. Invertibility is not needed for this identity.

Fix $\epsilon >0$. Since $\alpha$ is ${\mathcal{F}}_0$-measurable and finite, the Martingale convergence theorem and continuity of finite-partition conditional entropy allow an $r\ge 0$ withTo see the continuity explicitly, for each partition atom $A$ of $\alpha$ the conditional probabilities $\mathbb{E}[1_A\mid \sigma ({\xi }_0^r)]$ tend to $1_A$ almost everywhere; apply dominated convergence theorem to the bounded continuous function $-t\log t$ on $[0,1]$ and sum over the finitely many partition atoms.

For $n\ge 1$ set $\gamma ={\alpha }_0^{n-1}$, $L=n+r$, and $\beta ={\xi }_0^{L-1}$. The conditional entropy of finite measurable partitions satisfiesThe second inequality uses conditioning reduces entropy, since $\beta$ refines each block $T^{-j}{\xi }_0^r$; the final equality uses measure preservation.

Moreover $\gamma$ is ${\mathcal{F}}_L$-measurable. For each $j\ge 0$, tail measurability gives $\alpha$ measurable with respect to ${\mathcal{F}}_L$, hence $T^{-j}\alpha$ measurable with respect to $T^{-j}{\mathcal{F}}_L={\mathcal{F}}_{L+j}\subseteq {\mathcal{F}}_L$. Thus conditioning reduces entropy and the displayed block identity implyUse the symmetric entropy identity $H(\gamma )=H(\beta )-H(\beta \mid \gamma )+H(\gamma \mid \beta )$ to obtainThe fraction on the right tends to zero: $r$ is fixed and $H({\xi }_0^{n+r-1})/(n+r)\to h$ by the definition of entropy rate of a measurable partition. Taking $n\to \infty$ and then $\epsilon \downarrow 0$ gives

Now let $A\in \mathcal{T}(\xi )$ and take $\alpha =\{A,X\setminus A\}$. If $0<\mu (A)<1$, its binary entropy is $-\mu (A)\log \mu (A)-(1-\mu (A))\log (1-\mu (A))>0$, while its entropy rate is zero, contradicting completely positive entropy. ThereforeSince $\xi$ was arbitrary, every finite-partition tail is trivial. The argument needs only that $\xi$ is finite and the measure is a probability; it does not assume a finite generator, finite total system entropy, or invertibility.

For finite-valued discrete random variables, the conditional entropy isThe chain rule for information entropy in two orders givesThereforeusing nonnegativity of conditional entropy and the fact that conditioning reduces entropy. This proves the required inequality.

For Fano's inequality, let $X$ take values in an alphabet of size $m$, let $\hat{X}=g(Y)$ be any estimator, and putBecause $E$ is determined by $(X,Y)$,Now $H(E\mid Y)\le H(E)=h_2(p_e)$. If $E=0$, $X$ is determined by $Y$; if $E=1$, at most $m-1$ values remain possible. HenceCombining the bounds yields

Because $X,Y,Z$ are independent random variables, adding $Y$ is an independent noise channel, sois a Markov chain. The data processing inequality for mutual information givesTranslation in the finite additive group preserves conditional entropy, and independence therefore givesandSubstitution proves the required entropy submodularity for three independent sums.