How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Classical marked-set hitting bound from a spectral gap
Statement
Under the hypotheses of Spectral bound for a symmetric lazy walk killed on a marked set, suppose and start uniformly on . Then
so and steps give constant success probability. If , the hitting time is .
Facts & Assumptions
Given: The finite symmetric lazy chain and marked set from Spectral bound for a symmetric lazy walk killed on a marked set, and the survival-law definition of Finite-chain hitting time and survival law.
Spectral bound for a symmetric lazy walk killed on a marked set: , and the compression is positive semidefinite.
Cauchy–Schwarz: , with equality exactly for linearly dependent vectors: in a finite-dimensional inner-product space.
Proof
Let . For the uniform initial law on , [F2] gives . Since is positive semidefinite, it has eigenvalues in by [F1]; its th power therefore has operator norm at most . [F3] and prove the survival bound, including .
Put . For , the nonnegative-integer tail formula in [F2] and the finite geometric partial sums give . For the constant-success bound choose . Since , we have ; the finite binomial expansion gives . Hence . If , step 1.1 gives zero no-hit probability for every , so one step suffices. The case is immediate from the definition.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
14 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, Chapter 18, §18.4 (standard reference, not scraped)
- Ronald de Wolf, Quantum Computing: Lecture Notes, Chapter 8, §8.1 (standard reference, not scraped)
- Frédéric Magniez, Ashwin Nayak, Jérémie Roland, and Miklos Santha, Search via Quantum Walk, Proposition 1 (standard reference, not scraped)