Derive the collective prefactor in the resolved-variance law
Derive why the optimal balanced two-outcome collective measurement on the spin resolves an asymptotic between-outcome variance of \(\mathrm{Var}_{\rm bet}^{\rm coll}\to(\ln 2/4)N\), rather than the Gaussian median-split value \(N/(2\pi)\), thereby establishing the \(N\ln 2\) prefactor theoretically.
References
The prefactor is not derived. Proposition~\ref{prop:linear} fixes the scaling but not the constant, which requires knowing how much of \mathrm{Var}[J_x] a balanced collective split resolves. Numerically \mathrm{Var}{\rm bet}{\rm coll}\to(\ln2/4)N, giving /\to N\ln2. The natural estimate---J_x is a sum of N independent \pm1/2 variables, hence asymptotically Gaussian with \sigma{2}=N/4, and a median split of a Gaussian resolves (2/\pi)\sigma{2}---predicts \mathrm{Var}{\rm bet}{\rm coll}=(1/2\pi)N, which is 9\% below the measured value and clearly excluded by the data (Sec.~\ref{sec:law}). Identifying the correct classical statistics problem, and thereby deriving \ln2, is the main theoretical question left open by this work.
The law is clean over 4\le N\le24, but we cannot exclude a crossover at larger N.
That one bit outperforms a complete readout per bit invites an optimisation over the number and placement of outcomes; we do not know whether the return per bit is always maximised at two outcomes.