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.
Amplitude-estimation expectation estimator
Definition
Input. A finite expectation-estimation task with an exact encoded observable , a coherent sampler for its law, rational target , and rational failure probability . For , require . The supplied access model must include every forward, inverse, controlled, value, predicate, and reflection operation that the chosen QA13 amplitude-estimation circuit invokes.
If , return . Otherwise compute the exact affine value Append a good flag to the coherent sampler. Reversibly compute in the declared value workspace, apply the supplied one-qubit rotation controlled by the sample/value registers, and uncompute all scratch used only for . Let denote the resulting state-preparation unitary, including and the controlled rotation; let project onto flag . Run the unchanged median-confidence estimator Median confidence amplification for amplitude estimation on with amplitude tolerance and failure probability , and call its estimate . Return
For , the exact-return branch has no quantum call. When , the input is a costed finite circuit/oracle description. Charge each use of , , controlled versions, the good and initial-state reflections, value decoding, reversible arithmetic and uncomputation, the controlled rotation and its precision, phase estimation, state preparation, gates, width, ancillas, and classical output processing according to Circuit-family and computation cost model. No operation is free because it is described by the law or observable .
Depends on
- Coherent finite sampler and observable value
- Measurement law of a coherent sampler
- Finite expectation-estimation task
- QFT-based amplitude estimation
- Amplitude-estimation additive error and success probability
- Amplitude-estimation query and precision complexity
- Median confidence amplification for amplitude estimation
- Explicit median confidence guarantee for amplitude estimation
- Median amplitude-estimation query cost
- Controlled Unitary Access
- Inverse Unitary Access
- Dirac Notation Conversion
- Circuit-family and computation cost model
Used by
- Coherent SDP matrix-expectation oracle Algorithm
- Variance-aware quantum Monte Carlo Algorithm
- Bernoulli mean as a good-state probability Example
- Dyadic positive-mean estimator under an L2 bound Lemma
- Coherent query-to-circuit cost translation Theorem
- Quantum bounded-expectation guarantee and query cost Theorem
- Variance-aware quantum Monte Carlo guarantee Theorem
Dependency tree · two levels
86 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
- Ashley Montanaro, Quantum speedup of Monte Carlo methods, Theorem 3 (standard reference, not scraped)
- Gilles Brassard, Peter Høyer, Michele Mosca, and Alain Tapp, Quantum Amplitude Amplification and Estimation, Section 4 (standard reference, not scraped)
- Andrew M. Childs, Lecture Notes on Quantum Algorithms, Section 19.3 (standard reference, not scraped)