Calibration families and exact small oracles
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CLAIM C03 and C04
16.1 An unbalanced disjunction
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For \(k\ge2\) and \(n=k+2\), define
This is a deliberately easy family with two differently sized accepting regions. It separates the cost of finding one safe packet from the degree needed for complete coverage.
Proposition 16.1: Raw calibration parameters
For this family,
Proof
The minterms have supports \(\{a,b\}\) and \(\{z_1,\ldots,z_k\}\). The first two assertions follow from Theorem 15.6. The monomial \(abB\) has degree \(k+2\) and does not cancel with either lower-degree term in the displayed polynomial.
Theorem 16.2: Affine optimization of the one-packet parameter
For every \(k\ge2\),
Proof
First restrict to the face \(z_1=0\). There \(F_k+\ell\) becomes \(ab+\ell|_{z_1=0}\), a nonzero quadratic. If the restricted affine term contains a remaining \(z\) variable, set \(a=0\) and set the remaining affine form to one. This gives an accepting affine subspace of codimension at most two inside the face. If it contains no remaining \(z\) variable, fix \(a,b\) to an accepting pair instead. The indicator of the resulting affine subspace in the full input space has degree at most three and is safe. Thus every affine adjustment has a nonzero safe polynomial of degree at most three.
For equality, take \(\ell=a\). The degree-three polynomial
is safe for \(G_k\). Suppose a safe \(h\) of degree at most two existed. On \(a=0\), acceptance requires \(B=1\). For \(k\ge3\), the monotone-ideal argument forces \(h|_{a=0}=0\), hence \(h=aL\) with \(L\) affine in the remaining variables. The negative face \(a=b=1\) forces \(L(1,z)=0\), so \(L=c(1+b)\). The negative point \(a=1,b=0,z=(1,\ldots,1)\) then forces \(c=0\).
When \(k=2\), the restriction can initially be \(h|_{a=0}=cB\). Write \(h=cB+aL\). On the same negative face \(a=b=1\), the quadratic term \(cB\) cannot be cancelled by affine \(L\), so \(c=0\). The previous argument then applies. There is no nonzero safe polynomial of degree at most two, proving equality.
Theorem 16.3: Affine optimization of complete coverage
For every \(k\ge2\),
Proof
Restrict \(G_k=F_k+a\) to \(b=1\). Then
This is the indicator of one point on \(k+1\) variables. At the positive input \(a=0,b=1,z=(1,\ldots,1)\), a safe detecting polynomial must restrict to that singleton indicator and therefore have degree at least \(k+1\). Thus \(\beta(G_k)\ge k+1\).
For the upper bound, Theorem 16.2 shows that no \(F_k+\ell\) is a singleton indicator: a singleton would have one-packet degree \(n=k+2>3\). These functions are also nonzero because \(F_k\) is not affine. Each has at least two positive inputs, so Proposition 15.1 gives \(\beta(F_k+\ell)\le n-1=k+1\).
Corollary 16.4: Restored cost interval for the calibration family
For \(n=k+2\),
Proof
The lower bound follows from Theorems 15.3 and 16.3. For the upper bound, compute \(A=ab\) with one multiplication and \(B\) using \(k-1\) multiplications. Toggle the output by \(A+B+AB\), requiring one output Toffoli and affine additions. Uncompute \(A\) and \(B\). The total Toffoli count is \(k+1+k=2k+1=2n-3\).
The preceding combined bound \(\max\{0,\deg F_k-1,2\max_{\ell}\alpha_1(F_k+\ell)-3\}\) gives \(n-1\) for this family; its immunity-only term is the constant \(3\). The complete-cover bound gives \(2n-5\). The stronger estimate can also be recognized by restriction to \(b=1\) followed by a free affine output adjustment. No exact optimum is claimed when the displayed endpoints differ.
| Inputs \(n\) | \(\max_\ell\alpha_1\) | \(\beta^*\) | One-packet bound | Complete-cover bound |
|---|---|---|---|---|
| 4 | 3 | 3 | 3 | 3 |
| 5 | 3 | 4 | 4 | 5 |
| 6 | 3 | 5 | 5 | 7 |
| 7 | 3 | 6 | 6 | 9 |
| 8 | 3 | 7 | 7 | 11 |
| 9 | 3 | 8 | 8 | 13 |
The formulas are proved for all \(k\ge2\). The source evaluates the designated family truth tables through nine inputs, but checks every affine mask only through eight. The ninth-input affine maximum is justified by the proof, not by a stored exhaustive mask search.