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For the known bit string $y$, define the Pauli gate product $T_y={⨂}_{j=0}^{N-1}{Z}_j^{y_j}$. Its diagonal action is $T_y|x⟩=(-1{)}^{x\cdot y}|x⟩$, where the binary inner product is taken modulo two. The Hadamard transform produces $|{\varphi }_y⟩=H^{\otimes N}|y⟩=T_y|\varphi ⟩$. Thus the required vectors for Grover search with a nonzero reflection label areThey remain orthonormal and $|{\varphi }_y⟩=\sin \theta |{\tilde{\Psi }}_g⟩+\cos \theta |{\tilde{\Psi }}_b⟩$. The new iterate is $G_y=(2|{\varphi }_y⟩⟨{\varphi }_y|-I)U_f$. Since $T_y$ commutes with the diagonal marked-state phase oracle and $T_y^2=I$,Consequently its matrix on the signed basis is exactly the rotation matrix in part (a), proving the claimed equivalence of the two evolutions.

Choose an orthonormal eigenbasis of $V$, with eigenphases ${\varphi }_0,{\varphi }_1$, counting multiplicity. The maximally mixed state is basis independent, soLinearity means that the experiment is the equal mixture of the two eigenstate experiments. After the inverse quantum Fourier transform, the joint state is $\frac{1}{2}{\sum }_{j=0}^1|{\chi }_j⟩⟨{\chi }_j|\otimes |v_j⟩⟨v_j|$, where $|{\chi }_j⟩={\sum }_m{a}_m({\varphi }_j)|m⟩$ andThus the exact output distribution for quantum phase estimation with a maximally mixed target isFor a nonzero phase mismatch, the squared probability amplitude is ${\sin }^2[L(\varphi -2\pi m/L)/2]/\{L^2{\sin }^2[(\varphi -2\pi m/L)/2]\}$, with value one at an exact match. If both eigenphases are exactly representable on the $L$-point grid, their two labels occur with probability $1/2$ each; coincident labels have their probabilities added. For general eigenphases there can be nonzero probabilities at other labels, so it would not be exact to list only the two best approximations with probability $1/2$ each. Each eigenphase is sampled with spectral weight $1/2$, and its nearest-label event has conditional probability at least $4/{\pi }^2$. In particular a nearest label for one eigenphase has unconditional probability at least $2/{\pi }^2$, with additional contributions possible from the other phase.

Choose $a_1,\dots ,a_d\in \mathfrak{m}$ whose classes form a basis of the cotangent space of a local ring $\mathfrak{m}/{\mathfrak{m}}^2$, and put $J=(a_1,\dots ,a_d)$. Spanning gives $\mathfrak{m}=J+{\mathfrak{m}}^2$. Thus the quotient module $N=\mathfrak{m}/J$ satisfies $N=\mathfrak{m}N$. It is a finitely generated module, since $A$ is a Noetherian ring.

Here is the needed form of the Nakayama lemma, with proof. Choose generators $n_1,\dots ,n_r$ of $N$ and write $n_i={\sum }_j{c}_{ij}{n}_j$ with $c_{ij}\in \mathfrak{m}$. For the column vector $n$, $(I-C)n=0$. Multiply by the adjugate to obtain $\det (I-C)n=0$. Since $\det (I-C)\equiv 1(\mathrm{mod}\mathfrak{m})$, this determinant is a unit in the local ring $A$, so every $n_i=0$. Therefore $N=0$, proving $\mathfrak{m}=J$. Conversely, any generators of $\mathfrak{m}$ must span its cotangent space, so at least $d$ are needed. HenceHere $\mu (M)$ denotes the minimal number of generators of a module $M$.

For the additional minimum, the maximal ideal $\mathfrak{m}$ is itself $\mathfrak{m}$-primary, so it supplies an eligible ideal generated by $d$ elements. ConsequentlyThe minimum here is over primary ideals. A bound $\mu (Q)\le d$ for every $\mathfrak{m}$-primary ideal would be false. Indeed, for $A=k[[u,v]]$ and $\mathfrak{m}=(u,v)$, $d=2$ but $Q={\mathfrak{m}}^2=(u^2,uv,v^2)$ is primary and needs three generators. To verify primaryness, if $ab\in Q$ and $a\notin Q$, then $b$ cannot be a unit, so $b\in \mathfrak{m}$ and $b^2\in Q$. The three displayed monomials form a basis of $Q/\mathfrak{m}Q={\mathfrak{m}}^2/{\mathfrak{m}}^3$, proving the lower bound of three, while they already generate $Q$. This illustrates that primary ideals can need more generators than the maximal ideal.

Use the standard nonnegative partition-normalized B-splines $N_j$; their sum is at most one, and is exactly one for the complete clamped basis on $[0,1]$. The printed question introduces $M_i$ only in its last matrix formula without defining it. The intended normalization is the unit-integral normalization of a B-spline:For supports wholly inside the interval, $c_i=(t_{i+k}-t_i)/k$. The positivity and partition convention, sometimes called $L_{\infty }$ normalization, should not be confused with individually rescaling every spline to have maximum exactly one.

The characterization in part 1(a) gives the normal equations for the orthogonal projection:Dividing each equation by $c_i$ yieldsThis is the mixed-normalization spline Gram matrix. It need not be symmetric, but $G=D^{-1}H$ with $D=\mathrm{diag}(c_i)$ and $H_{ij}=(N_i,N_j{)}_{L_2}$. The ordinary Gram matrix $H$ is positive definite because the splines are linearly independent, so $G$ is invertible and $a=G^{-1}d$.

Since each $M_i$ is nonnegative and has integral one,The induced matrix operator norm is the maximum absolute row sum, soFinally, nonnegativity and the subpartition of unity for B-splines giveTaking the supremum over $t$ and then over $\Vert f{\Vert }_{\infty }\le 1$ proves the maximum-norm bound for spline projection:The formulas also establish linearity and boundedness of the projection on continuous inputs. Its range is exactly the finite-dimensional spline space $\mathcal{S}$, since it fixes every spline in that space. Thus, when the splines are continuous, it is an operator $C[0,1]\to C[0,1]$ with range $\mathcal{S}$; it is not onto all of $C[0,1]$ as the printed wording says. For example, $e^t$ cannot belong to a fixed finite-degree spline space because it is not a polynomial on any nonempty knot interval. The missing $M_i$ normalization and this range correction are needed for a precise reading of the result.

For $\ell \in V^{\ast }$ define $(g\ell )(v)=\ell (g^{-1}v)$. The representing matrix is the transpose of the inverse matrix on $V$, so the dual character isThe last equality follows by averaging a Hermitian inner product to make the finite-group group representation unitary. Complex finite-group representations are isomorphic exactly when their characters agree. Hence $V$ is self-dual exactly when its character is real-valued.

The invariant tensor identification $(V\otimes V{)}^G\simeq {\mathrm{Hom}}_G(V^{\ast },V)$ and Schur lemma show that, for irreducible $V$, the trivial multiplicity is zero unless $V^{\ast }\simeq V$, and one in that case. Dually, a self-duality isomorphism gives a nonzero invariant bilinear form $B$ on $V$. Its radical is an invariant subspace, so irreducibility makes $B$ nondegenerate. The space of invariant bilinear forms is one dimensional. Since the transpose $B^T$ is another such form, $B^T=cB$; transposing again gives $c^2=1$. Therefore $B$ is symmetric or alternating: in the negative-transpose case, $B(v,v)=-B(v,v)$ forces $B(v,v)=0$. Both types cannot coexist, since proportional nonzero forms cannot have both transpose signs.

In odd dimension an alternating matrix has determinant zero: $\det B=\det (-B^T)=(-1{)}^n\det B=-\det B$. Thus only the symmetric case is possible. Over $\mathbb{C}$ a nondegenerate symmetric bilinear form has a basis with matrix $I$: diagonalize it by congruence and rescale each nonzero diagonal entry. In that basis invariance says $\rho (g{)}^T\rho (g)=I$. Consequently

The nonzero leading coefficients and distinct degrees make $P_0,\dots ,P_{n-1}$ a basis of the polynomials of degree at most $n-1$. Consequently the given orthogonality implies ${\int }_a^{b}f(x)Q(x)dx=0$ for every such polynomial $Q$.

If $f$ has infinitely many distinct zeros in $(a,b)$, there is nothing to prove. Otherwise suppose it has fewer than $n$. Among those zeros, list the sign-changing ones as $t_1<\cdots <t_m$, with $m<n$, and put $Q(x)={\prod }_{j=1}^m(x-t_j)$, using $Q=1$ when $m=0$. On each interval between zeros, $f$ has a fixed sign by continuity. Crossing a sign-changing zero flips both $f$ and $Q$; crossing any other zero flips neither sign. Thus $fQ$ has one constant sign wherever nonzero. Since $f$ is not identically zero, continuity gives an open interval on which $fQ$ is strictly of that sign. Hence its integral is nonzero, contrary to orthogonality to $Q$. Therefore $f$ has at least $n$ distinct zeros in $(a,b)$. This is the reusable orthogonality forces many interior zeros argument.

Use the conventionfor the characteristic polynomial. Choosing $\det (A-tI)$ instead multiplies it by $(-1{)}^n$ and leaves the requested zero identity unchanged. For each eigenvector $v_j$, induction gives $A^r{v}_j={\lambda }_j^r{v}_j$, including $r=0$. Thusbecause ${\lambda }_{j}I-A$ has the nonzero kernel vector $v_j$ and therefore has zero determinant. Part (a) supplies a basis consisting of these $n$ eigenvectors. A linear map vanishing on every basis vector vanishes everywhere. ConsequentlyThis proves the Cayley-Hamilton theorem identity directly in the distinct-eigenvalue case assumed here.

Preserving the Euclidean scalar product means $(Ax{)}^T(Ay)=x^{T}y$ for every pair of real vectors $x,y$. Since these bilinear expressions agree on all pairs of basis vectors, this is equivalent to $A^{T}A=I$: the columns of $A$ form an orthonormal basis.

The first column is a unit vector, so it is $(\cos \theta ,\sin \theta {)}^T$ for some real $\theta$. A perpendicular unit vector is one of the two choices $\epsilon (-\sin \theta ,\cos \theta {)}^T$, where $\epsilon \in \{1,-1\}$. Hence all the required orthogonal matrices areHere $R(\theta )$ is a planar rotation, and $\theta$ is determined modulo $2\pi$. To check the group laws explicitly, $D_{\epsilon }R(\varphi )=R(\epsilon \varphi )D_{\epsilon }$, soThis proves closure. The identity is $R(0)D_1=I$, the inverse of $R(\theta )D_{\epsilon }$ is $R(-\epsilon \theta )D_{\epsilon }$, and associativity is inherited from matrix multiplication. Thus the set is the orthogonal group $O(2)$.

The ascending chain $\ker T\subseteq \ker T^2\subseteq \cdots$ must stabilize because $V$ is finite-dimensional. Once $\ker T^l=\ker T^{l+1}$, it stays stable: if $T^{l+2}v=0$, then $Tv\in \ker T^{l+1}=\ker T^l$, so $v\in \ker T^{l+1}=\ker T^l$; induction repeats the argument. Choose a positive such $l$, giving $\ker T^{2l}=\ker T^l$.

If $v\in \ker T^l\cap \mathrm{im}{T}^l$, write $v=T^{l}w$. Then $T^{2l}w=0$, so $w\in \ker T^l$ and $v=0$. By the rank-nullity theorem, the dimensions of those two subspaces sum to $\dim V$. HenceThis proves the finite-dimensional form of the Fitting lemma.

If $\det T=0$, the kernel contains a nonzero vector $u$, and $\mathrm{im}T$ is a proper vector subspace. Extend a basis of that image of a linear map to a basis of $V$ and choose a nonzero linear functional $\lambda$ which vanishes on the former basis. Define $S(v)=\lambda (v)u$. It is a nonzero endomorphism, since $\lambda$ takes a nonzero value somewhere, andThus a singular endomorphism has a nonzero two-sided annihilator, as required.

For an idempotent linear map $T$, the decomposition $v=(v-Tv)+Tv$ shows $V=\ker T\oplus \mathrm{im}T$: the first term is killed by $T$, the second is fixed, and a vector both fixed and killed is zero. An adapted basis makes its matrix $\mathrm{diag}(0,I_r)$, with $r=\mathrm{rank}T$. Consequently equal ranks give identical adapted matrices and therefore a change of basis relating $T_1,T_2$. Conversely, similar matrices have the same rank because pre- and postmultiplication by invertible matrices preserve rank. Hence $T_1$ and $T_2$ are similar exactly when their ranks agree, proving the similarity classification of idempotent linear maps.

The monomials $x^i{y}^j$ with $i,j\ge 0$ and $i+j\le n$ form a basis of the given vector space. There are $k+1$ of total degree $k$, soOn that basis the linear operator is diagonal:Both summands of the eigenvalue are nonnegative integers. Their sum is zero precisely when $i,j\in \{0,1\}$. Thus the kernel is the span of those members of $1,x,y,xy$ whose total degree is at most $n$. In particular it is $\mathrm{span}\{1,x,y\}$ for $n=1$ and $\mathrm{span}\{1,x,y,xy\}$ for $n\ge 2$; for $n=0$ it is the constants. The image of a linear map is the span of all the other permitted monomials, since each has a nonzero diagonal eigenvalue and hence is the image of its reciprocal multiple. No excluded monomial can occur in an image because its diagonal coefficient is zero.

The trace is the sum of these eigenvalues. At degrees zero and one all contributions vanish. At degree two the contributions are $2,0,2$, summing to four; at degree three they are $6,2,2,6$, summing to sixteen; at degree four they are $12,6,4,6,12$, summing to forty. ThereforeThis is the diagonal monomial action of a second-order Euler operator.