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Clean-input isometry error bounds output trace distance

Statement

Let V,W:H→K⊗E be linear isometries between finite-dimensional complex inner-product spaces, meaning V∗V=W∗W=IH, and assume δ≥0 and ∥V−W∥op≤δ. For every nonzero finite-dimensional reference space R and density matrix ρ on H⊗R (a positive semidefinite matrix of trace one), define VR=V⊗IR and WR=W⊗IR on the ordered product bases. Define the partial trace by (Tr⁡EX)(k,r),(k′,r′)=∑eX(k,e,r),(k′,e,r′), and define ∥X∥1 as the sum of singular values and D(σ,ω)=12∥σ−ω∥1. The output density matrices ρV=(Tr⁡E⊗IR)(VRρVR∗),ρW=(Tr⁡E⊗IR)(WRρWR∗) satisfy D(ρV,ρW)≤δ. Tensor products use the ordered product orthonormal basis and product inner product; the displayed matrix formula fixes the partial-trace convention used here. For column vectors, a∗ denotes conjugate transpose and aa∗ the rank-one matrix with entries aiaj‾.

Facts & Assumptions

Given: The finite spaces, maps V,W, and density matrix ρ in the Statement.

[F1]

Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition: finite maps have orthonormal singular bases; their operator norm is the largest singular value.

Proof

Given: As in Facts & Assumptions.

1.1F1givenalgebra

Put A=V−W, and let (rj) be an orthonormal basis of R. By [F1], diagonalizing A∗A in a right singular basis shows that (A⊗IR)∗(A⊗IR)=A∗A⊗IR has the same largest eigenvalue as A∗A: its eigenvectors are the products of right singular vectors of A with the rj, and its eigenvalues are the corresponding squared singular values. Therefore ∥VR−WR∥op=∥A⊗IR∥op=∥A∥op≤δ. For every unit vector ψ∈H⊗R, the vectors a=VRψ and b=WRψ have norm one and satisfy ∥a−b∥≤δ.

1.2givenalgebra

For Z=VRρVR∗, positivity follows from y∗Zy=(VR∗y)∗ρ(VR∗y)≥0 for every y; the same holds with WR. For a vector z∈K⊗R, the entry-sum formula gives z∗(Tr⁡E⊗IR)Zz=∑e(IK⊗∣e⟩⊗IR z)∗Z(IK⊗∣e⟩⊗IR z)≥0, and likewise for WR; hence both reduced outputs are positive. That formula also gives tr⁡((Tr⁡E⊗IR)Z)=tr⁡Z. Since VR∗VR=WR∗WR=I, cyclicity of finite matrix trace gives tr⁡Z=tr⁡ρ=1 for both outputs. Thus ρV,ρW are density matrices.

2.1F1F2step 1.1algebra

In an orthonormal basis of span⁡{a,b}, write b=za+se2, where z=a∗b, s=1−∣z∣2, and e2⊥a is unit if s>0. The matrix of aa∗−bb∗ on this span is Hermitian, has trace zero and determinant −s2, so its eigenvalues are s,−s; outside the span it is zero. Thus 12∥aa∗−bb∗∥1=s. Also s2=1−∣z∣2≤2−2Re⁡z=∥a−b∥2, because the difference of the right side and left side is ∣1−z∣2. This proves D(aa∗,bb∗)≤δ for every pure input, including s=0.

2.2F1givenstep 1.1algebra

Let X be Hermitian on K⊗E⊗R, and set Y=(Tr⁡E⊗IR)X. For a matrix B on K⊗R, define B~ on K⊗E⊗R by applying B to the K,R coordinates and leaving the E coordinate fixed. The entry-sum definition of partial trace gives tr⁡(B∗Y)=tr⁡(B~∗X). Reordering the product basis from K,E,R to K,R,E conjugates B~ to B⊗IE, so ∥B~∥op=∥B∥op by the same singular-basis argument as in step 1.1. For any square matrix X=UΣV∗, [F1] gives its singular value decomposition, and ∣tr⁡(C∗X)∣=∣tr⁡(V∗C∗UΣ)∣≤∥C∥op∑jΣjj=∥C∥op∥X∥1, since every diagonal entry of a contraction has modulus at most one. Taking the supremum over ∥B∥op≤1, and using the equality case B=UYVY∗ in a singular value decomposition of Y, proves ∥Y∥1≤∥X∥1. This dual formula also gives the triangle inequality and homogeneity for ∥⋅∥1 by applying it to a sum and scalar multiple.

3.1F1F2step 2.1step 1.2step 2.2algebra∎

For a pure input ρ=ψψ∗, step 2.1 bounds the trace distance before discarding E, and step 2.2 shows the partial trace cannot increase the trace norm of the Hermitian difference. Hence the reduced output distance is at most δ. For general positive trace-one ρ, [F2] gives a spectral decomposition ρ=∑jpjψjψj∗ with pj≥0 and ∑jpj=1. The maps and partial trace are linear; the triangle inequality and homogeneity from step 2.2 therefore bound the reduced output distance by ∑jpjδ=δ.

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