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Diamond Norm Ancilla Dimension Bound
Statement
Let be nonzero finite-dimensional complex Hilbert spaces, , and let be linear. Then and the latter induced norm is attained on some rank-one operator with unit vectors .
Facts & Assumptions
Given: Nonzero finite-dimensional and a linear map .
The trace norm is the sum of singular values; SVD writes , where , , and (Trace Norm, Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition). The QC01 trace-norm definition is present as a draft and agrees with the root first-argument-linear convention.
Tensor maps act on elementary tensors factorwise, factor permutations reorder product bases, and tensor-map adjoints are factorwise (Tensor products of finitely many linear maps, Ordered tensor registers, their inner product, and wire permutations, Tensor maps preserve composition, identities, and adjoints). Products of operator matrix units form a basis of the full tensor-product operator space, so checking the amplification identity on those elementary operators proves it for every input (Tensor products of operator matrix units form a basis). The QC02 carriers are present as drafts; step 1.2 proves the singular-value invariance of the two separate isometric embeddings.
Every vector has a Schmidt decomposition with at most terms and orthonormal families in both factors (Schmidt decomposition). The QC03 theorem body is present as a draft; this proof uses only its finite-rank decomposition.
The finite trace norm is a norm and satisfies the triangle inequality (Trace Norm Duality And Norm Properties). For a matrix of fixed shape , let . Its SVD gives and . Since each singular value is at most , . The norm triangle inequality therefore gives, for matrices of the same shape, . This finite-coordinate bound proves the continuity used below; the singular-value and trace-norm formulas are Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition and Trace Norm.
A nonempty closed bounded subset of is compact (Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line); every finite-dimensional complex inner-product space has an orthonormal basis (Every finite-dimensional real or complex inner product space has an orthonormal basis).
Proof
For fixed , let be the maximum of over unit vectors . Their unit spheres are compact by [F1]. In orthonormal coordinates each entry of is a finite product of coordinates of and conjugates of coordinates of ; applying the finite linear map remains continuous, and [L4] makes the output trace norm continuous. Hence the maximum exists. By [L1], every with has an SVD with unit , , and . Linearity and the trace-norm triangle inequality give . For unit , the adjoint calculation gives , a rank-one orthogonal projection, so its singular values are one and the remaining zeros; hence . Thus the induced norm equals and is attained.
Now take any finite ancilla and unit vectors . By [L3], their Schmidt ranks are at most . Let and pad each Schmidt list to an orthonormal -tuple, obtaining by replacing the respective ancilla Schmidt vectors by the standard basis vectors and retaining the Schmidt coefficients and -vectors. Define to send that basis to the respective padded orthonormal ancilla families; thus and . The tensor-map definition and product pairing show each map and is an isometry. Factor permutations are unitary because they carry an orthonormal product basis bijectively to another such basis. Thus To verify the equality, expand operators in elementary tensors : sends this to , and the two isometries send each ancilla matrix unit to . For isometries , . The positive eigenvalues of this operator are those of : an eigenvector with positive eigenvalue maps to , and any eigenvector with positive eigenvalue must lie in the range of because the operator vanishes on its orthogonal complement. Hence left/right isometric embeddings preserve the nonzero singular values and trace norm. Embedding isometrically into on both sides is the same case, so the original output has trace norm at most .
By step 1.1, the induced norm for each is attained by a rank-one unit input, so step 1.2 gives . Taking the supremum over all finite gives . The reverse inequality holds because is one of the ancilla dimensions in the supremum. Step 1.1 at supplies an attaining rank-one operator.
Depends on
- Diamond Norm
- Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition
- The adjoint $T^*:W\to V$ is characterised by $\langle Tv,w\rangle_W=\langle v,T^*w\rangle_V$
- Rectangular matrix multiplication and the identity matrix $I_n$, including zero-sized shapes
- The basis-independent trace of an endomorphism of a finite-dimensional vector space
- Ordered tensor registers, their inner product, and wire permutations
- Tensor products of finitely many linear maps
- Tensor maps preserve composition, identities, and adjoints
- Tensor products of operator matrix units form a basis
- Heine-Borel in $\mathbb{R}^n$: with the Euclidean metric a subset of $\mathbb{R}^n$ is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line
- Schmidt decomposition
- Trace Norm Duality And Norm Properties
- Every finite-dimensional real or complex inner product space has an orthonormal basis
- Trace Norm
Used by
- Channel Composition Hybrid Bound Theorem
- Channel Diamond Norm One Theorem
- Channel Tensor Error Bound Theorem
- Hybrid bound for a composed channel circuit Theorem
Cited to discharge well-definedness by Diamond Norm.
Dependency tree · two levels
72 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- John Watrous, The Theory of Quantum Information (standard reference, not scraped)