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Filtered γ₂ factorization norm for query mismatch masks
Definition
Let be a nonempty finite domain with , and let be its input-mismatch masks from Input-mismatch masks and entrywise filtering. For any complex matrix indexed by , define
where the infimum is over finite-dimensional complex inner-product spaces and vector families satisfying Set the value to if no such families exist. Every feasible family may be taken in dimension at most for each i.
Facts & Assumptions
Given: A finite matrix A and the mismatch masks Δ on the same nonempty finite Boolean input set D.
Input-mismatch masks and entrywise filtering: Δ_i(x,y)=1 exactly when x_i≠y_i; J−F vanishes on the diagonal and on equal-output pairs.
Real and complex inner product spaces, with the inner product linear in the first argument: finite real or complex vector spaces have a positive-definite inner product.
Dirac Notation Conversion: the Dirac pairing is linear in the ket (second argument) and conjugate-linear in the bra (first argument).
Proof
For each i, encode two finite vector families u_{x,i},v_{y,i} in a common finite-dimensional complex inner-product space. Their filtered factorization represents A exactly when A_{xy}=Σ_i(Δ_i){xy}⟨u{x,i}|v_{y,i}⟩_D for every ordered pair (x,y). The Dirac convention [F3] makes this expression conjugate-linear in each u factor and linear in each v factor.
The cost of a representation is max{max_xΣ_i||u_{x,i}||²,max_yΣ_i||v_{y,i}||²}; take the infimum over all finite-dimensional representations. If A_{xx}≠0 for some x, all Δ_i(x,x)=0 and no representation exists. If A has zero diagonal, each off-diagonal pair differs in some coordinate; assigning its entry to the first differing mask and writing for each resulting matrix, take and in . Then , so this is a feasible representation.
Thus the value is +∞ exactly when the diagonal support obstruction occurs, and otherwise the infimum ranges over a nonempty finite-dimensional class with a finite nonnegative cost. For any feasible family, restricting the ambient space for each i to the span of its 2|D| vectors preserves all inner products and norms and gives dimension at most 2|D|. Nonemptiness of D ensures that both maxima in the cost are defined.
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Dependency tree · two levels
11 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
- Andrew M. Childs, Lecture Notes on Quantum Algorithms (17 April 2025) (standard reference, not scraped)
- Ronald de Wolf, Quantum Computing: Lecture Notes, arXiv:1907.09415v6 (standard reference, not scraped)