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Clean-input isometry error bounds output trace distance
Statement
Let be linear isometries between finite-dimensional complex inner-product spaces, meaning , and assume and . For every nonzero finite-dimensional reference space and density matrix on (a positive semidefinite matrix of trace one), define and on the ordered product bases. Define the partial trace by and define as the sum of singular values and . The output density matrices satisfy . 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, denotes conjugate transpose and the rank-one matrix with entries .
Facts & Assumptions
Given: The finite spaces, maps , and density matrix in the Statement.
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.
Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: a finite self-adjoint endomorphism has an orthonormal eigenbasis with real eigenvalues.
Proof
Given: As in Facts & Assumptions.
Put , and let be an orthonormal basis of . By [F1], diagonalizing in a right singular basis shows that has the same largest eigenvalue as : its eigenvectors are the products of right singular vectors of with the , and its eigenvalues are the corresponding squared singular values. Therefore . For every unit vector , the vectors and have norm one and satisfy .
For , positivity follows from for every ; the same holds with . For a vector , the entry-sum formula gives , and likewise for ; hence both reduced outputs are positive. That formula also gives . Since , cyclicity of finite matrix trace gives for both outputs. Thus are density matrices.
In an orthonormal basis of , write , where , , and is unit if . The matrix of on this span is Hermitian, has trace zero and determinant , so its eigenvalues are ; outside the span it is zero. Thus . Also , because the difference of the right side and left side is . This proves for every pure input, including .
Let be Hermitian on , and set . For a matrix on , define on by applying to the coordinates and leaving the coordinate fixed. The entry-sum definition of partial trace gives . Reordering the product basis from to conjugates to , so by the same singular-basis argument as in step 1.1. For any square matrix , [F1] gives its singular value decomposition, and since every diagonal entry of a contraction has modulus at most one. Taking the supremum over , and using the equality case in a singular value decomposition of , proves . This dual formula also gives the triangle inequality and homogeneity for by applying it to a sum and scalar multiple.
For a pure input , step 2.1 bounds the trace distance before discarding , 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 . For general positive trace-one , [F2] gives a spectral decomposition with and . The maps and partial trace are linear; the triangle inequality and homogeneity from step 2.2 therefore bound the reduced output distance by .
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Sources
- Sheldon Axler, Linear Algebra Done Right, fourth edition, §7E, Theorem 7.70 (standard reference, not scraped)