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Amplitude Amplification — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Amplitude Amplification
- Binary Operations, Monoids, Groups and Subgroups
- Boolean Circuits and Nonuniform Complexity
- Bounded Linear Operators and Quotient Spaces
- Circuit Syntax, Denotational Semantics, and Measurement
- Clifford+T Exact and Approximate Synthesis
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Norms and Realification
- Complex Power Series and Analytic Functions
- Complexification, Realification and Real Structures
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Computational Problems, Promises, and Cost Models
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Continuous-Gate Universality and Exact Synthesis
- Controlled Gates, Phase, and Entanglement
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Dimensional Normed Spaces and Riesz Lemma
- Finite-Dimensional Complex Inner-Product Spaces and Matrix Interfaces
- Formal Languages, Encodings, and Decision Problems
- Foundations of the Real Numbers for Analysis
- Free Modules, Exact Sequences, Projective and Injective Modules
- Fundamental Trigonometric Identities
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Recurrences and Rational Generating Functions
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Measurements, POVMs, and Instruments
- Metric Spaces
- Modules, Submodules, Quotient Modules and the Isomorphism Theorems
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Properties of the Integral and the Working FTC
- Pure and Mixed States, Ensembles, and Schmidt Decomposition
- Qubits, Bloch Sphere, Pauli Algebra, and One-Qubit Gates
- Registers, Tensor Products, and Partial Trace
- Relations, Functions, and Quotients
- Resource Bounds and Machine Invariance
- Reversible Computation, Ancillas, and Coherent Oracles
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Simple Field Extensions and the Construction of the Complex Numbers
- Sine, Cosine, and the Definition of Pi
- Splitting Fields
- Strongly Continuous Semigroups and Hille Yosida
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Tensor Products of Modules
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Polynomial Hierarchy and Relativization
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Turing Machines, Configurations, and Computation
- Uniform Circuit Families, Promise Problems, and Complexity
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
The worked two-dimensional example computes the success probability at three iteration counts, including the overshoot after an exact peak. The counterexample shows that a known-amplitude maximizing count can give zero success for a different success weight.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
A prepared state with success amplitude one half
Example
Take an orthonormal pair and the unit prepared state with . Its success amplitude is , so and . Compute the good probabilities after standard iterations.
Facts & Assumptions
Given: The state, projection, and iteration counts in the Example.
Exact success probability after amplitude amplification: after iterations, .
Verification
Put by [F2]. Since , [F2] gives . Also [F2] gives , while [F2] gives . Hence ; positivity excludes , so . The vector is normalized because , and .
By [F1], [F3], and step 1.1, , , and . Each value lies in ; the second iteration overshoots the exact peak attained after one iteration.
A fixed Grover iteration count can fail when a is unknown
Statement refuted
For every pair of initial success weights , an iteration count maximizing success for over gives success probability at least for when reused unchanged.
Facts & Assumptions
Given: Two prepared success weights and a common fixed iteration count.
Exact success probability after amplitude amplification: after iterations, success is when .
Cofunction, supplementary, quarter-turn, and reflection identities for the six trigonometric functions gives and ; Double-angle and quadratic power-reduction identities gives ; Pi is the first positive zero of sine gives for ; Parity and the Pythagorean identity for sine and cosine gives ; Quarter-turn values and shifts by pi/2 and pi gives and .
Counterexample
Put by [F2]. Since , [F2] gives . Also [F2] gives , while [F2] gives . Hence , and positivity gives . By [F2], . For , take ; the allowed range is . The probabilities for are , respectively, so is the unique maximizer.
For , take . Reusing gives , while direct sampling at succeeds with probability . Thus the first weight's maximizing count violates the claimed lower bound at the second weight.