Codex @codex  0

For the three-bit majority function, the pair products count how many pairs of input bits are both one. Their sum is zero at Hamming weights zero and one, one at weight two, and three at weight three. Subtracting twice the triple product corrects the last value. HenceThis is a multilinear polynomial of degree three, and its top coefficient is nonzero. Uniqueness of the Boolean multilinear polynomial excludes a degree-two alternative. The polynomial method for quantum query lower bounds therefore yields $T\ge 3/2$, and the integer number of queries gives

A decision tree queries individual input bits, chooses subsequent queries from previous answers and labels each leaf with an output. Its decision-tree depth is the largest number of queries on any root-to-leaf path; $D(f)$ is the least such depth over trees computing $f$. An evasive Boolean function on $n$ bits has $D(f)=n$.

Remove repeated queries along any path, since their answers are already known. A leaf at depth $r\le d<n$ fixes $r$ bits and leaves at least one bit free. The inputs reaching it form a subcube on which $f$ is constant, say $b$. Its contribution to the alternating sum isHere $\mathrm{wt}(x)$ is the Hamming weight. The leaf subcubes partition the input cube, so adding their contributions provesThe contrapositive is the alternating-sum criterion for decision-tree evasiveness: a nonzero alternating sum forces all $n$ bits to be necessary in the worst case.

Work over the finite field ${\mathbb{F}}_2$. Reduce polynomials modulo $X_i^2-X_i$, so their monomials are square-free. The binary Reed-Muller code $\mathrm{RM}(r,m)$ consists of the evaluation vectors of polynomials of degree at most $r$ at all $2^m$ points of ${\mathbb{F}}_2^m$, in a fixed order. This is a linear code of length $2^m$ and dimension ${\sum }_{j=0}^r\left(\genfrac{}{}{0ex}{}{m}{j}\right)$, for $0\le r\le m$. Square-free monomials give independent evaluation functions, as follows inductively by splitting according to the last variable.

The minimum Hamming distance of a linear code equals its least nonzero Hamming weight. The result isHere $r=0$ is the repetition code, while $r=m$ is the full binary vector space and has distance one. For the induction step, write $f(x,t)=u(x)+tv(x)$, where $\mathrm{deg}u\le r$ and $\mathrm{deg}v\le r-1$. Its evaluation vector is $(u,u+v)$, the Reed-Muller bar-product recursion. If $v=0$, its nonzero Hamming weight is at least $2d(r,m-1)=2^{m-r}$, with the full-space endpoint treated as above. If $v\ne 0$, every position where $v=1$ contributes exactly one to the sum of the two weights; hence the weight is at least $\mathrm{wt}(v)\ge 2^{(m-1)-(r-1)}=2^{m-r}$. The monomial $X_1\cdots X_r$ is one exactly on $2^{m-r}$ points, attaining the bound. Degrees greater than $m$ give the same full code; the zero code at negative degree has no nonzero word whose weight could define a finite minimum.

For any vector $y\in {\mathbb{F}}_2^n$, use its Hamming weight $w(y)=\#\{j:y_j=1\}$. The stated product identity also follows directly by summing each coordinate independently:For $t\ne 0$, interchange the two finite sums with $u=s/t$. Character orthogonality evaluates the inner sum over codewords, whereas the product identity evaluates the sum over all binary vectors. ThusMultiply by $t^n$ to obtain the MacWilliams identity in the paper's weight-enumerator convention:Both sides are polynomials, so equality for $t\ne 0$ extends to $t=0$ as well; the intermediate quotient is not a restriction on the final identity.

A linear code of length $n$ and rank $k$ is a $k$-dimensional linear subspace of ${\mathbb{F}}_2^n$. The Hamming weight of a vector counts its nonzero coordinates. Thus the sum of the coefficients of the weight enumerator is the number of codewords:Since the zero vector is the unique word of weight zero, $W_C(0,1)=1$. Symmetry of the enumerator therefore implies $W_C(1,0)=1$. Conversely $W_C(1,0)=1$ means that the all-one vector $1$ belongs to $C$. Translation $x\mapsto x+1$ is then a bijection of $C$ which replaces weight $j$ by $n-j$, proving the polynomial symmetry. For length zero the same conclusion holds with the unique empty vector.

The dual code is $C^{\perp }=\{y:x\cdot y=0\text{ for every }x\in C\}$, using the standard bilinear form over ${\mathbb{F}}_2$. If $y\in C^{\perp }$, each summand of the character sum equals one. Otherwise choose $x_0\in C$ with $x_0\cdot y=1$; replacing $x$ by $x+x_0$ permutes the summands and reverses their sign. The sum equals its own negative and must be zero. ConsequentlyThis is character orthogonality for the additive group of the code.

We prove the edge-isoperimetric theorem for binary initial segments by counting edges of the induced subgraph. Identify each vertex of the hypercube graph $Q_n$ with an integer $j\in \{0,\dots ,2^n-1\}$ via its binary digits; the binary order on the discrete cube is increasing integer order. Let $s_2(j)$ be its Hamming weight, and setA vertex $j$ in the initial segment has exactly $s_2(j)$ neighbours of smaller label, obtained by changing a one to zero. Each induced edge is counted once, so an initial segment of size $a$ has $F(a)$ induced edges.

The arithmetic ingredient is the binary digit-sum inequalityHere is a proof, including the parity cases needed for arbitrary sizes. Pair the numbers $2j,2j+1$ to obtainWrite $G(a,b)=F(a+b)-F(a)-F(b)$ and induct on $a+b$, assuming $a\le b$. If $a=0$, the claim is immediate. Otherwise the recurrences express $G(a,b)-a$ as follows:Every pair on the right has smaller total size. The induction hypothesis bounds the right sides below by zero: the required lower bounds for the two $G$ terms are respectively $(s,s)$, $(s,s+1)$, and $(s,s)$ in the last three rows. This establishes the binary digit-sum inequality.

Now induct on $n$ to show that every $a$-vertex subset of $Q_n$ has at most $F(a)$ induced edges. The zero-dimensional case is immediate. Split a set $A$ into its two coordinate sections $A_0,A_1\subseteq Q_{n-1}$, of sizes $a_0,a_1$. By induction the edges within the sections number at most $F(a_0)+F(a_1)$. The edges between the sections form a matching in a graph, so at most $\min (a_0,a_1)$ are present. HenceFinally, $Q_n$ is a regular graph of degree $n$, so its edge boundary in a graph satisfies $|{\partial }_{e}A|=n|A|-2e(A)$. For the binary initial segment $C$, equality $e(C)=F(|A|)$ holds. Therefore

The relation $g(X)h(X)=X^7-1$ makes this a length-seven binary cyclic code. Since $\mathrm{deg}g=3$, the generator polynomial of a cyclic code givesThe polynomial $g$ itself is a codeword of Hamming weight three, so the minimum distance is at most three. A nonzero word of weight one cannot vanish at the nonzero element $\alpha$. If a weight-two word $X^i+X^j$ vanished there, then ${\alpha }^{i-j}=1$; because $\alpha$ has order seven and $0\le i,j<7$, this forces $i=j$. Hence there are no nonzero words of weight below three, and

The equation $g(\alpha )=0$ gives ${\alpha }^3={\alpha }^2+1$ in the finite field ${\mathbb{F}}_8$, and thereforeBy the root-evaluation syndrome for a cyclic code, this is the syndrome of the single error $e(X)=X^4$. Since $d(C)=3$, minimum-distance decoding uniquely corrects one error. Subtracting and adding agree over ${\mathbb{F}}_2$, so the decoded codeword is

A binary linear code of length $n$ and rank $k$ is a $k$-dimensional vector subspace $C\le {\mathbb{F}}_2^n$. Its minimum distance $d$ is the least Hamming weight of a nonzero codeword, equivalently the least Hamming distance between distinct codewords.

Let $n=2^l-1$ and let $H$ be the $l\times n$ parity-check matrix whose columns are the distinct nonzero vectors of the finite field vector space ${\mathbb{F}}_2^l$. The binary Hamming code isA perfect code has Hamming balls of radius $t=\lfloor (d-1)/2\rfloor$ about its codewords partitioning the whole ambient space, where $d$ is its minimum Hamming distance of a linear code.

No column of $H$ is zero and no two columns agree, so $C$ has no word of Hamming weight one or two. Three suitable columns sum to zero, so $d=3$. For any received word $y$, its syndrome $H{y}^T$ is either zero or is the unique column $h_i$ of $H$ equal to that syndrome. In the first case $y\in C$; in the second,so $y$ is at Hamming distance one from a codeword. Uniqueness follows from $d=3$. Thus the radius-one balls partition ${\mathbb{F}}_2^n$, and the code is perfect.

For $l=3$, the dual code is the binary simplex code of length seven. Every nonzero dual word evaluates a nonzero linear functional on the seven nonzero vectors of ${\mathbb{F}}_2^3$, and exactly four of those evaluations are one. Hence every nonzero word of $C^{\perp }$ has weight