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.
BBBV hybrid lower bound for unstructured search
Statement
For the promise of All-zero versus exactly-one-marked search promise, every bounded-error quantum algorithm deciding whether the input is zero or one-marked uses at least standard bit queries. Every bounded-error finder that outputs a marked coordinate on each and none on also uses at least standard bit queries. The decision part satisfies the stronger bound .
Facts & Assumptions
Given: The promise task, pure query normal form, final acceptance gap, aggregate hybrid bound, and worst-case query cost convention.
All-zero versus exactly-one-marked search promise: the no input is , each yes input is , and finding succeeds exactly when a valid marked coordinate is returned on a yes input or none on the no input.
Coherent normal form for finite quantum query algorithms: finite-branch algorithms with worst-case standard XOR queries have equivalent pure-state circuits with exactly ordinary queries and the same output law.
Acceptance-gap lower bound on final-state distance: a common final measurement with probabilities at least and at most forces final vector distance at least .
Aggregate hybrid distance budget over all marked indices: the squared final-state distances over all singleton inputs sum to at most .
Worst-case bounded-error correctness: bounded-error decision correctness is per promised input with success probability at least .
Quantum query complexity: query cost is the maximum number of oracle calls on any promised input and branch.
Proof
Let a bounded-error decision algorithm use queries. Apply [F2] and use the common final acceptance event. For each , correctness [F5] gives acceptance at least on and at most on . By [F3], .
Summing the squares of these inequalities gives . By [F4], the right side is at most , so .
Now let a bounded-error finder use queries. Append a decision step that measures its output and, if it is a coordinate , makes one standard bit query at address with response initialized to ; accept exactly when that response is . If the output is none or invalid, reject without a query. On this decision never accepts; on it accepts whenever the finder returns , which has probability at least by [F1]. This is a bounded-error decision procedure with at most queries, so [F6] and step 2.1 give .
If , then , and step 3.1 gives . If , a zero-query finder has the same output law on and , yet success would require probability at least on the disjoint outcomes none and under this one common law. Their probabilities would sum to at least , impossible; hence .
Depends on
- All-zero versus exactly-one-marked search promise
- Cumulative hybrid distance for a T-query algorithm
- Aggregate hybrid distance budget over all marked indices
- Acceptance-gap lower bound on final-state distance
- Coherent normal form for finite quantum query algorithms
- Worst-case bounded-error correctness
- Quantum query complexity
Used by
Dependency tree · two levels
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Sources
- Charles H. Bennett, Ethan Bernstein, Gilles Brassard, and Umesh Vazirani, Strengths and Weaknesses of Quantum Computing (standard reference, not scraped)
- Andrew M. Childs, Lecture Notes on Quantum Algorithms (2025) (standard reference, not scraped)
- Ronald de Wolf, Quantum Computing: Lecture Notes, arXiv:1907.09415v6 (standard reference, not scraped)