Alphabeta Math
AlgorithmAlgorithm: AI-adaptedProof: Not applicablePipeline-generated
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.

Finite amplification from a supplied success lower bound

Definition

Input a finite-register coherent preparation U with good projection P, as in Prepared unitary and success subspace for amplitude amplification, and represented rational parameters 0<p0≤1 and 0<β<1. The success promise is p:=∥PU∣0⟩∥2≥p0. Supply U† under Inverse Unitary Access, the reflections S0=I−2∣0⟩⟨0∣ and Sg=I−2P under Orthogonal-Projector Reflection and Access Contract, and the controlled versions of all operations used under Controlled Unitary Access. The good/bad instrument is the projective measurement {P,I−P} of Finite projective measurements. It may be supplied directly or implemented by preparing an ancillary control in ∣+⟩, applying controlled Sg, applying a Hadamard to the control and measuring it, with outcome 1 designated good. These implementations and register initialization have their actual declared costs.

By exact rational comparisons compute the least power of two M satisfying M2p0≥4, and the least positive integer R satisfying (3/4)R≤β. Set b=log⁡2M. More general exact parameter representations are allowed only when they support these finite comparisons.

For trials 1,…,R, carry out the following instructions, stopping at the first return.

  1. Prepare U∣0⟩ on a fresh register and perform the good/bad instrument. On a good outcome, return that register. On a bad outcome, discard it.
  2. Draw exactly b fresh independent unbiased random bits, interpret them as an integer j∈{0,…,M−1}, and prepare U∣0⟩ on a fresh register. Apply exactly j iterates Q=−US0U†Sg of Amplitude amplification with a fixed integer iteration count, then perform the good/bad instrument. On a good outcome, return that register. On a bad outcome, discard it and proceed to the next trial.

If all R trials fail, return an explicit failure symbol. Preparations, bits and instruments are fresh between attempts. A classical-output specialization may additionally measure and verify the returned output, charging this work separately; it does not replace the good/bad projective instrument when a conditional quantum state is requested.

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