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Choose Kraus representationsThe composite quantum channel has Kraus operators $T_{ba}=D_b{C}_a$. Each has matrix rank at most $k$, because it factors through the $k$-dimensional space $K$, andThe operation fidelity uses the unsquared quantum fidelity on a purification of a density operator, so its square is entanglement fidelity. The Kraus formula for entanglement fidelity givesIndeed each overlap $⟨{\Psi }_{\rho }|(I_R\otimes T_{ba})|{\Psi }_{\rho }⟩$ equals $\mathrm{Tr}({\rho }_Q{T}_{ba})$.

For any one of these Kraus operators $T$, let ${\Pi }_T$ be the orthogonal projection onto its image, of matrix rank $r_T\le k$. Since ${\Pi }_{T}T=T$, the Cauchy-Schwarz inequality for the Hilbert-Schmidt inner product gives the rank bound for a weighted operator trace:The last step uses part (i) at $r_T$, followed by nonnegativity of the eigenvalues of the density operator ${\rho }_Q$. Summing over the Kraus operators and using their completeness relation proves the finite-dimensional quantum compression converse:No invertibility of ${\rho }_Q$, or restriction to an isometric decoder, was used.

A density operator is a pure state exactly when its matrix rank is one. Within the admissible interval from part (a), one of $(1+\alpha )/2$ and $(1-\alpha )/2$ must vanish. Therefore the pure-state parameters and vectors arewhere the Bell states are $|{\Phi }^{\pm }⟩=(|00⟩\pm |11⟩)/\sqrt{2}$. Equivalently, the purity of a density operator is $\mathrm{Tr}{\rho }_{AB}^2=(1+{\alpha }^2)/2$, reaching one exactly at these two endpoints.

Write $|\zeta ⟩={\sum }_{i,j}{M}_{ij}|i{⟩}_A|j{⟩}_B$. Normalization says $\Vert M{\Vert }_{\mathrm{HS}}=1$, and the Schmidt rank equals the matrix rank of $M$. Its nonzero singular values are therefore $s_1,\dots ,s_r$ with ${\sum }_j{s}_j^2=1$.

The overlap with the maximally entangled state is $⟨{\varphi }^{+}|\zeta ⟩=\mathrm{Tr}(M)/\sqrt{d}$. The trace norm estimate of Q3(i), with test linear operator $I$, followed by the Cauchy-Schwarz inequality, givesFor two pure states, the unsquared quantum fidelity is the absolute value of their overlap. Hence the maximally entangled overlap bound from Schmidt rank isThe bound is attained by $|\zeta ⟩=r^{-1/2}{\sum }_{j=0}^{r-1}|jj⟩$. Thus uniform Schmidt coefficients on $r$ aligned basis pairs give the greatest possible overlap for that Schmidt rank.

The quotient theorem for Cartesian tensors here says: if contracting $T_{ij}$ with every vector $v_j$ gives the components $w_i=T_{ij}{v}_j$ of a vector in every right-handed orthonormal basis, then $T$ obeys the second-order tensor transformation law. Conversely that transformation law guarantees the contraction is a vector. One may equivalently test that $u_i{T}_{ij}{v}_j$ is a scalar for every pair of vectors $u,v$.

By part (a), $v^{\prime }=Rv$ and $w^{\prime }=Rw$. The contraction property in the new basis saysSince this holds for every test vector, $T^{\prime }R=RT$. Using $R^{-1}=R^T$ givesFor the converse, $T^{\prime }{v}^{\prime }=RT{R}^{T}Rv=RTv=Rw$, exactly the vector law. For the equivalent scalar formulation, invariance of $u^{T}Tv$ for every $u,v$ gives $u^T{R}^T{T}^{\prime }Rv=u^{T}Tv$, forcing the same matrix identity.

The quantifier “every” matters: an arbitrary nonzero matrix can annihilate one fixed vector, so a single successful contraction cannot establish tensoriality. The stated bases test proper rotations; transformation under orthogonal reflections would require an additional hypothesis if that were wanted. “Second-order” counts tensor indices, rather than the matrix rank of a particular component matrix.

Work in positive dimension $n\ge 1$. A cap set has no distinct zero-sum triple. Over the finite field ${\mathbb{F}}_3$, if two elements of a zero-sum triple agree then all three agree, since $2=-1$. Thus a zero-sum triple in a cap set must be diagonal. We prove the Ellenberg–Gijswijt cap-set bound by the polynomial method in combinatorics, keeping the exponential constant explicit and removing the fixed prefactor.

First prove the needed slice rank of a diagonal tensor over any field. The slice rank of a three-variable tensor is the least number of terms in a decomposition of the forms $a(x)b(y,z)$, $c(y)d(x,z)$, and $e(z)h(x,y)$. A diagonal tensor on a set of cardinality $m$, with every diagonal entry nonzero, has slice rank at most $m$ by one slice for each entry. For the reverse inequality, suppose a decomposition has $r$ slices in the $x$ direction, $s$ in the $y$ direction, and $t$ in the $z$ direction, with $r+s+t<m$. LetThe rank-nullity theorem gives $\dim W\ge m-r$. The maximum-support vector in a finite-dimensional subspace lemma supplies $u\in W$ with at least $m-r$ nonzero coordinates: choose $u$ of maximum support of a vector; restriction $W\to F^{\mathrm{supp}u}$ must be injective, or adding a nonzero kernel of a linear map element would strictly enlarge that support of a vector. This argument is valid over finite fields, without any assumption that a generic vector avoids all coordinate hyperplanes.

Contract the proposed tensor decomposition in $x$ against $u$. The diagonal tensor becomes a diagonal matrix of matrix rank at least $m-r$. All $x$ slices vanish. Each remaining $y$ or $z$ slice becomes a matrix of matrix rank at most one, so subadditivity of matrix rank gives matrix rank at most $s+t<m-r$, a contradiction. Therefore the slice rank of a diagonal tensor is exactly $m$.

Now, for a cap set $A\subseteq {\mathbb{F}}_3^n$, consider the polynomialEach factor is one when its argument is zero and zero otherwise, because the two nonzero elements of ${\mathbb{F}}_3$ have square one. Thus $P$ is the indicator function of $x+y+z=0$. Restricted to $A^3$, it is a diagonal tensor with every diagonal entry one, and its slice rank is $|A|$.

Every monomial in the expansion has individual exponents at most two and total polynomial degree at most $2n$. Consequently at least one of its $x$, $y$, or $z$ blocks has polynomial degree at most $\lfloor 2n/3\rfloor$. Assign each monomial to one such block, breaking ties in a fixed way, and collect assigned terms by the monomial in that block. Each collection is a slice. Ifthere are at most $m_n$ slices in each direction. Thus $|A|\le 3m_n$.

The low-degree monomial count for the cap-set bound follows directly from a weighted sum. Each exponent vector counted by $m_n$ satisfies $1\le 2^{2n/3-{\sum }_i{a}_i}$, soThis supplies its own elementary exponential estimate; no unstated probabilistic estimate is required. Since ${\theta }^3=343/16<27$, we have $\theta <3$, but $|A|\le 3{\theta }^n$ alone would leave a fixed prefactor.

Use Cartesian powers of cap sets to remove that prefactor. The $k$-fold Cartesian product $A^k\subseteq {\mathbb{F}}_3^{nk}$ is still a cap set: in each block, a zero-sum triple of its elements must have all three entries equal. Hence $|A{|}^k\le 3{\theta }^{nk}$. Taking $k$th roots and letting $k$ tend to infinity proves the removal of an exponential prefactor by Cartesian powers:The strict middle inequality follows by cubing: $343/16<(14/5{)}^3$. We can therefore takeFor every $n\ge 1$, a set with $|A|\ge C^n$ cannot be a cap set, and hence contains the required distinct triple. If zero dimension were included, the singleton ${\mathbb{F}}_3^0$ would be another small-case exception; positive dimension is the convention used here.

For $z$ on the unit circle, use the conjugated monomial vectorThen the prescribed diagonal sums givePositive semidefiniteness provesThis is the Gram matrix representation of a trigonometric polynomial. The Hermitian condition also implies $p_{-k}=\overline{p_k}$, so its values on the unit circle are real. The conjugated monomial vector is required by the source's $i-j=k$ convention; the unconjugated vector would represent $p(z^{-1})$ instead.

In particular $p_0=\mathrm{tr}M\ge 0$. If this matrix trace is zero, all nonnegative eigenvalues vanish and $M=0$, so $p=0$. A general feasible Gram matrix need not have matrix rank one; the Fejér–Riesz theorem ensures a rank-one representative exists whenever the nonnegative trigonometric polynomial is nonzero.

Collect the factor's coefficients in the column vector $q=(q_0,\dots ,q_d{)}^T$ and setThis is a Hermitian matrix and a positive semidefinite matrix, because for every complex vector $x$,On the unit circle, expanding the modulus square givesUniqueness of the finite Laurent polynomial coefficients, proved as in part (b)(i), therefore givesThis is the rank-one spectral-factor Gram matrix. The matrix $M$ has matrix rank one when $q\ne 0$ and zero when $q=0$. The orientation $i-j=k$ is the PDF's convention: using $\overline{q_i}{q}_j$ instead would generally interchange $p_k$ and $p_{-k}$.

View $X$ as the matrix of a linear map $T:F^m\to F^n$ of matrix rank $r$. Choose vectors $u_1,\dots ,u_r$ whose images form a basis of $\mathrm{im}T$, extend them by a basis of $\ker T$ to a basis of $F^m$, and extend $T{u}_1,\dots ,T{u}_r$ to a basis of $F^n$. In these two bases the matrix of $T$ is the rank normal formThe two changes of basis give invertible $P,Q$ with the displayed matrix equal to $Q^{-1}XP$.

For the block upper triangular matrix $A=\left(\begin{array}{cc}B& D\\ 0& C\end{array}\right)$, every nonzero term in the Leibniz formula for determinants sends the rows belonging to $C$ into the columns belonging to $C$; bijectivity then sends the remaining rows into the $B$ columns. The permutation sum consequently factors into the determinant sums for $B$ and $C$, proving

Finally suppose $L(X)=0$, so $AX=XB$: the map $X:{\mathbb{C}}^m\to {\mathbb{C}}^n$ is an intertwining operator. If $v$ lies in the generalized eigenspace of $B$ for $\lambda$, then for some $k$,Because $A$ and $B$ have no common eigenvalue, $A-\lambda I$ is invertible, hence $Xv=0$. The generalized eigenspaces of $B$ span ${\mathbb{C}}^m$, so $X=0$. Thus the Sylvester equation operator $L$ is injective.

An $m$ by $n$ matrix has matrix rank $m$ exactly when at least one of its $m$ by $m$ minors has nonzero determinant. For each multi-index $I$, the setis open in the vector space of all real $m$ by $n$ matrices, because the determinant is continuous. Their union is $X_{m,n}$, so $X_{m,n}$ is itself open in ${\mathbb{R}}^{mn}$. It therefore inherits the standard smooth manifold structure and has dimension $mn$. This is the full-row-rank matrix manifold.

A polynomial regression of degree two is linear in its unknown regression coefficients despite being curved in the predictor. Its design matrix has columns $1,x,x^2$; three distinct predictor values give full column matrix rank.

If $\rho$ is a density operator with decreasing eigenvalues ${\lambda }_j$ and $T$ has matrix rank at most $k$, let $\Pi$ project onto the image of $T$. The Cauchy-Schwarz inequality for the Hilbert-Schmidt inner product givesThe Hermitian effect variational principle bounds $\mathrm{Tr}(\rho \Pi )$ by ${\sum }_{j<k}{\lambda }_j$, proving the claimed estimate without assuming $\rho$ is invertible.

For coefficient column $q=(q_0,\dots ,q_d{)}^T$, $M_{ij}=q_i\overline{q_j}$ is a Hermitian matrix and a positive semidefinite matrix because $x^{\ast }Mx=|q^{\ast }x{|}^2$. Expanding $|q(z){|}^2$ on the unit circle gives coefficient $p_k={\sum }_{i-j=k}{M}_{ij}$. Thus $M$ has matrix rank one when $q\ne 0$ and zero otherwise. General feasible Gram matrices need not have matrix rank one.

The image of the linear map $A+B$ is contained in the sum of the images of $A$ and $B$. Since the dimension of a vector space of this sum is at most the sum of their dimensions, matrix rank is subadditive. Iteration shows that a sum of $r$ matrices of matrix rank at most one has matrix rank at most $r$, over any field.