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State Preparation, Isometry Synthesis, and Limits
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Approximate Universality and Solovay–Kitaev
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Circuit Syntax, Denotational Semantics, and Measurement
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Coordinate and Path Transport
- Complex Differentiability and the Cauchy–Riemann Equations
- Complex Norms and Realification
- Complex Power Series and Analytic Functions
- Complex Subspace Realification
- Complexification, Realification and Real Structures
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Connectedness
- 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
- Countability Axioms and Cardinal Functions
- Cyclic Groups and Direct Products
- Darboux, L'Hôpital, and Taylor's Theorem
- 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
- 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 Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- 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
- Partitions of Unity and Paracompactness
- 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
- 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
- Sard Theorem and Transversality
- Separation Axioms: the Hierarchy
- 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
- Smooth Manifolds and Smooth Maps
- Splitting Fields
- Strongly Continuous Semigroups and Hille Yosida
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Tensor Products of Modules
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- 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 Inverse and Implicit Function Theorems
- The Inverse Function Theorem Completed
- The Logarithm and General Powers
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
Overview
This page treats state and isometry preparation as finite mathematical input and circuit problems. It defines amplitude-table, workspace, width, gate, precision and error conventions; constructs an exact prefix-rotation preparation circuit; and proves continuous-parameter and finite-alphabet limits under their stated models. State rays and projective charts remain distinct from vectors, and exact continuous-angle synthesis remains distinct from finite-alphabet approximation. Classical table input, preprocessing, coherent gates, workspace and precision are charged separately. No QRAM or free data loading is assumed.
3 · Logical flowchart
4 · Definitions, theorems and proofs
A Nonempty Euclidean Open Set Is Not Null
Statement
For every integer , every nonempty open subset is not null in the closed-cube-cover sense of Measure zero and content zero in by countable and finite cube covers. The proof uses no choice principle.
Facts & Assumptions
Given: An integer and a nonempty Euclidean open set .
A set is null when, for every , a sequence of closed cubes covers it with total volume at most ; nullity passes to subsets (Measure zero and content zero in by countable and finite cube covers).
Every closed Euclidean box is compact, and the proof of Heine–Borel in uses no choice principle (Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line).
A closed rectangle has volume equal to the product of its side lengths; finite coordinate cuts partition it into finitely many subrectangles whose volumes sum to its volume (Axis-parallel rectangles in and their volume).
Finite sums are additive and monotone, and a finite subfamily of nonnegative terms is bounded by a partial sum through its largest index (Finite sums and finite products, by recursion, Laws of finite sums and finite products).
A nonnegative series whose partial sums are bounded by converges with sum at most , and every partial sum is at most the series sum (A series of nonnegative terms converges iff its partial sums are bounded, and then the sum is their supremum).
For reals and natural , where is the canonical embedding of naturals into (The binomial theorem in : ).
Proof
Given: As in Facts & Assumptions.
Choose and with the Euclidean ball . Put and let . For every , , so and .
Suppose were null. By [F1], with there is a sequence of closed cubes covering whose side lengths have . For each , set and enlarge concentrically to an open cube of side . By [F6] and , . For every , induction using the finite-sum recursion gives . Thus every partial sum of the nonnegative enlarged-cost sequence satisfies , by [F4] and [F5]. A second application of [F5] gives a convergent total enlarged-cost series with sum at most .
The open cubes cover the closed box , so [F2] gives a finite subcover . Order its indices increasingly and take larger than the largest one. By [F4], the subcover's total cost is at most , which is at most by step 2.1.
Write for the closed cube with the same center and side as , so . Cut and these finitely many closed covering cubes at all their coordinate endpoints. The positive-side grid cells inside partition it; the interior of each cell lies in one covering cube because no covering-cube boundary crosses the cell. Assign each cell to the least index of a covering cube containing its interior. By finite cut-additivity from [F3], the assigned cells for each cube have total volume at most that closed cube's volume, while all cells sum to . Thus , a contradiction. Therefore is not null.
QC24 Projective Chord Distance
Definition
For an integer and unit vectors , write and for their pure-state rays and define the projective chord distance by
Here is the norm induced by the root inner product, which is linear in its first argument. The infimum is over unit complex scalars, so it removes only the common phase of a state vector. The companion lemma Projective Chord Distance Formula ↗ evaluates this infimum, proves it is attained, gives the closed formula, and proves independence from the chosen unit representatives.
QC24 Complex Projective Space and Its Standard Patches
Definition
For an integer , let be the quotient of by when for a nonzero complex scalar . Give its usual Euclidean topology and the quotient the topology in which a subset is open exactly when its inverse image is open. Every nonzero ray has a unit representative, and two unit representatives of one ray differ by a unit scalar, so this is the pure-state ray space of Pure state and ray.
For define the standard patch and the candidate coordinate map , with the coordinates kept in increasing original index order. Its candidate inverse sends to the ray of the vector with th coordinate and other coordinates given by in that same order. QC24 Projective Chart Dimension ↗ proves these are smooth charts, computes their real dimension, and notes that a single standard patch omits part of projective space when .
Isometry Unitary Completion
Definition
Input. Integers , ordered orthonormal bases of and , and an exact matrix whose columns are orthonormal, so represents a linear isometry in the convention of Linear isometries, and orthogonal or unitary operators on finite-dimensional inner product spaces. Arithmetic is ideal exact complex arithmetic with conjugation, norms, square roots, and exact zero tests; no bit-time bound is part of this procedure.
Procedure. Initialize an ordered list with the columns of . For , compute
If , leave unchanged. Otherwise append to . After the finite loop, return the square matrix whose columns are the final list, and return INVALID if the input dimensions or orthonormal-column condition fail.
For integer widths , put , , and . The input data and clean workspace use the ordered tensor-register convention of Ordered tensor registers, their inner product, and wire permutations and the big-endian basis encoding of Big-endian encoding of ordered bit strings. A clean workspace register starts at under Clean and dirty ancillas. Let be the ordered wire permutation that swaps the -wire data block with the -wire zero-workspace block, so . This sends the declared clean-input basis to the first standard basis vectors. The matrix is the full-unitary extension interface used by Isometry Completion and Synthesis ↗.
Projective Chord Distance Formula
Statement
For unit vectors ,
In particular, this value is independent of the chosen unit representatives, and it is zero exactly when the two vectors represent the same ray.
Facts & Assumptions
Given: Unit vectors and the projective chord distance of QC24 Projective Chord Distance.
For unit representatives, (QC24 Projective Chord Distance).
The root pairing is linear in the first argument, conjugate-linear in the second, conjugate-symmetric, and induces (Real and complex inner-product spaces and their induced length).
Two nonzero vectors represent the same pure-state ray exactly when one is a nonzero complex scalar multiple of the other; for unit vectors that scalar has modulus one (Pure state and ray).
Cauchy–Schwarz gives for unit vectors (Cauchy–Schwarz: , with equality exactly for linearly dependent vectors).
Proof
Given: As in Facts & Assumptions.
Put . For every , expand by [F2]: .
If , the real part is zero for every unit ; if , choose , giving . Since for every unit , this phase attains the infimum in [F1]; the value is , a real nonnegative number by [F4].
Replacing and by unit scalar multiples changes by a unit scalar, so its modulus and the displayed value do not change. Since the infimum is attained, it is zero exactly when a minimizing phase satisfies , which is exactly equality of the represented rays by [F3].
QC24 Projective Chart Dimension
Statement
For every integer , the standard patches of QC24 Complex Projective Space and Its Standard Patches are smooth charts with coordinate space . If , each fixed patch is proper, so a single standard coordinate patch does not cover all of .
Facts & Assumptions
Given: An integer , the quotient ray space , standard patches , and candidate coordinate maps and inverses from QC24 Complex Projective Space and Its Standard Patches.
and ; the candidate inverse uses the representative with th coordinate (QC24 Complex Projective Space and Its Standard Patches).
Proof
Given: As in Facts & Assumptions.
If , multiplying by any nonzero complex scalar leaves every ratio unchanged, so is well-defined on rays. The representative has th coordinate and all other coordinates equal to those ratios, so the stated inverse sends back to .
Conversely, applying to the inverse representative with th coordinate returns each entry of ; hence the two maps are inverse bijections. Let be the quotient map and . Since is open, is open. The restriction is a quotient map: for , if is open in , it is open in , so is open in the quotient and hence in ; the reverse implication follows from continuity of . The composite consists of continuous coordinate ratios on , so is continuous. The inverse is the quotient map composed with the continuous representative map , hence is continuous.
Let be the quotient map. Then is Euclidean open, so is open by the quotient topology. On with , write . Then , the transition coordinates satisfy and for . Conversely, and for . These rational maps are smooth as real maps on their open coordinate domains, so the charts are compatible and form a smooth atlas.
The patches cover every ray because a nonzero vector has at least one nonzero coordinate. If , choose ; the ray of the basis vector has th coordinate zero and is not in , so each fixed standard patch is proper. Finally, has complex, or real, coordinates.
QC24 State Preparation Cost Model
Definition
Fix an integer and . An explicit amplitude-table input is a list of exact complex numbers in the ordered basis convention of Big-endian encoding of ordered bit strings; it is valid when . Its target is the pure-state ray under Pure state and ray: nonzero scalar multiples represent the same ray, and is a unit representative when the table is normalized. A claim must state whether its output error is projective chord distance QC24 Projective Chord Distance or a representative-level vector error.
The cost record keeps these quantities separate: (1) the classical encoding length and table-reading/preprocessing work; (2) continuous-angle gate occurrences, or finite-alphabet gate occurrences when that model is specified; (3) width, clean-workspace and serial-depth usage; and (4) the declared output-error metric. If an encoding assigns exactly bits to each real coordinate and bits to each imaginary coordinate, with basis order implicit, the dense table contains exactly input bits. Those bits must decode to the input table in an exact-table claim; finite-precision approximations are separate inputs with separate error hypotheses. An explicit table is classical input: this definition grants no coherent table oracle, QRAM, or free loading operation. Ideal exact complex arithmetic and finite-precision bit cost are distinct models.
QC24 Signed Ray Packing Code
Statement
Let be an even integer and let satisfy . There is a family of at least unit sign vectors in , with their first coordinates fixed at , such that the projective chord distance between distinct resulting rays is at least . Here and .
Facts & Assumptions
Given: Even , , and the distance formula below.
For unit representatives, (Projective Chord Distance Formula).
A real function continuous on a closed interval and differentiable on its interior satisfies the mean value theorem (The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with ).
The natural logarithm is strictly increasing, has , and satisfies and for positive arguments (Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm); hence and for .
Product and chain rules hold for differentiable real functions, and differentiability implies continuity (Sums, scalar multiples, products and quotients: , , , and when , The chain rule, in one line from Carathéodory: if is differentiable at and is differentiable at , then is differentiable at with , A function differentiable at is continuous at ). The identity has derivative and constants have derivative , as their difference quotients are constant (The derivative of at a point that is a limit point of , and differentiability on a set, The - limit of at a limit point of ).
The real binomial expansion uses the coefficients , where embeds naturals into (The binomial theorem in : ).
Proof
Given: As in Facts & Assumptions.
Put , and . Greedily scan the finite set in lexicographic order: select the least remaining word and delete all words at Hamming distance at most , repeating until none remain. Distinct selected words have distance , and each selection deletes at most words, so the selected code has at least words.
For , the ratio gives . The binomial identity [F6], therefore yields . Put for , which is by [F4]. The product and chain rules [F5] give . For , the ratio exceeds , so [F4] gives . The same rules show is differentiable on , and [F5] makes it continuous there; in particular it is continuous on . By [F4], , so . The mean value theorem [F3] now gives for some . Thus and the code size is at least .
Map each selected bit word to the sign word ; this is a bijection preserving Hamming distance. Form . It is unit because it has entries of modulus . If two selected words differ in places, then . Since and , this lies in , so [F1] gives . The first half of each vector is fixed and nonzero, so scalar-proportional unit vectors must have scalar and identical last-half signs; distinct code words therefore give distinct rays.
QC24 Clean Ancilla Output Projection
Definition
Let be integers and . Let the data and workspace registers have ordered computational spaces and , with the ordered tensor factors and product basis of Ordered tensor registers, their inner product, and wire permutations. For a circuit unitary on their tensor product, its all-zero-workspace projection on a fixed input is
The projected joint vector is . An exact clean-workspace output on this input means that the full, unconditional output equals for a unit data vector and a unit scalar ; then . A merely nonzero defines only the zero-workspace projection branch and does not assert that the workspace is deterministically clean.
QC24 Finite Width Topology Count
Statement
Fix integers , , , and , and an ordered-wire circuit model with a finite gate alphabet of size and maximum arity . A serial length- description is an ordered list of gate labels and ordered tuples of at most distinct wire positions; its denotation is the ordered product of the corresponding local gate embeddings. Each parallel layer consists of pairwise wire-disjoint occurrences and denotes their product. With , the number of length- serial descriptions is at most . Any parallel circuit with total occurrences has a serial ordering with the same gate count and denotation.
Facts & Assumptions
Given: The finite alphabet, maximum arity, ordered wire positions and width specified in the Statement.
Ordered tensor registers, their inner product, and wire permutations fixes the ordered tensor factors, product basis and wire permutations.
A tensor map acts factorwise on elementary tensors, so an operator embedded on selected factors and an operator on disjoint factors commute (Tensor products of finitely many linear maps).
A circuit layer denotes the ordered product of its local gate embeddings; its layers are applied chronologically (Denotational semantics of a finite layered circuit).
Proof
Given: As in Facts & Assumptions.
A gate of arity has at most ordered wire tuples. Summing over possible arities gives at most tuples because ; including its label gives at most choices for one occurrence.
A length- serial description is an ordered list of such choices, so there are at most descriptions. For two gates on disjoint supports, reorder the tensor factors as the first support, the second support, and the remaining wires. Their embeddings are then and . By [F2], both compositions act as on elementary tensors, which span the register; thus they commute. By [F3], serially listing each layer therefore preserves its operator; listing the layers in circuit order preserves the full denotation and adds no occurrence.
Countable Union of Compact Null Sets Is Null
Statement
Let and let be a sequence of compact null subsets of , where nullity is in the closed-cube-cover sense of Measure zero and content zero in by countable and finite cube covers. Then is null. The proof selects finite rational-cube covers by their least codes and uses no choice principle.
Facts & Assumptions
Given: An integer and a sequence of compact null subsets of .
A null set has a countable closed-cube cover of arbitrarily small total volume; nullity passes to subsets (Measure zero and content zero in by countable and finite cube covers).
A compact subset of Euclidean space has a finite subcover from every open cover (Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line).
The rationals admit a fixed enumeration and has a fixed pairing enumeration ( is countably infinite, ).
Between any two distinct real numbers there is a rational number (The rationals embed densely in the reals).
A closed cube of side has volume (Axis-parallel rectangles in and their volume).
Every nonempty subset of has a least element (The well-ordering principle).
A series of nonnegative reals whose partial sums are bounded by converges with sum at most (A series of nonnegative terms converges iff its partial sums are bounded, and then the sum is their supremum).
Recursion defines the cumulative block lengths from , ; natural order is linear and because (The recursion theorem, Addition of natural numbers, Order on the natural numbers, is a linear order on ).
The finite-sum recursion is ; equivalently, an inclusive sum satisfies (Finite sums and finite products, by recursion).
Finite sums are additive and monotone; termwise inequalities between nonnegative sequences give the same inequality for each finite partial sum (Laws of finite sums and finite products).
For reals and natural , where is the canonical embedding of naturals into (The binomial theorem in : ).
Proof
Given: As in Facts & Assumptions.
Let be compact and null, and let . If , the empty family covers it. Otherwise [F1] gives a countable closed-cube cover with total volume at most . Write and let be its center coordinates. Set and let , an open concentric enlargement. By [F11], , because , and . Define the covering cost of an open cube of side to be ; this agrees with [F5] for its closed counterpart. Put and , where . For every , induction using [F9] gives : the formula is at , and the successor step adds . By [F7], the original nonnegative volume series has every partial sum at most . Finite-sum additivity and monotonicity [F10] therefore give for every . Since , [F7] gives a convergent total covering-cost series with sum at most . The open cubes cover , and compactness [F2] gives a finite subcover.
Order the finite subcover by its original indices and write and for the side and center of its th open cube. Its finite total cost is at most the partial sum of through its largest original index, hence at most by [F7, F10, step 1.1]. The finite polynomial sum is continuous at , so choose for which it is less than . By [F4], choose rational with and a rational center whose coordinates differ from by less than . The closed rational cube of side centered at contains the open cube of side centered at , since each coordinate displacement is less than . These finitely many rational cubes cover , and their total volume is less than by [F5].
Fix a bijection and a pairing bijection from [F3]. A list of rational cubes is determined by rationals: its side and its center coordinates for each cube. Encode a tuple recursively by and . Since and are injective, pairing the list length with the code of its rational coordinates gives an injection from all finite rational cube lists into . For each natural code, use its unique list preimage if it has one, and otherwise use the empty list; this defines an enumeration that includes every list. By step 2.1, every compact null has at least one enumerated list covering it with total volume less than . The set of qualifying codes is nonempty, so it has a least member by [F6]. The least-code rule assigns a unique cover to each pair without choosing from an arbitrary family.
Fix . For each , apply the unique rule of step 3.1 to with . Let be the finite length of its selected list, whose total volume is less than . Define and recursively as in [F8]. For each , if for some , take the cube at position in the th list; otherwise take the degenerate zero-side cube at the origin. The half-open index intervals are disjoint: if , then , so no lies in both. Each cube of the th list, at position , occurs at index ; thus every selected list occurs in the sequence.
Any finite initial segment meets only finitely many blocks. If it meets none, its total volume is zero. Otherwise, its finite set of block indices has a largest member : scan the finite list and retain the larger at each comparison using the linear order in [F8]. Each partial block costs at most its full-list bound from step 4.1. By [F9], the budget sum satisfies and . So induction gives . Thus every partial sum of the nonnegative volume series is at most , and [F7] gives convergence with total at most . The sequence covers , so [F1] shows that this union is null. Since every selected list is assigned by the unique least-code rule of step 3.1, the proof uses no choice principle.
QC24 Continuous-Parameter State Preparation Lower Bound
Statement
Let integer , , integer workspace count , and total width . Fix integers and and a countable library of gate families of arity at most , each represented by a countable atlas of parameter charts that are open subsets of with integer satisfying . For a nonnegative integer gate budget , each circuit topology selects at most such gates and wire placements; its gate matrices depend smoothly on their parameters. For each fixed topology and parameter-chart tuple, its denotation is the ordered product of the corresponding fixed local gate embeddings, with each embedding acting on its assigned ordered tensor factors and as the identity on the remaining wires, as specified by Denotational semantics of a finite layered circuit. Circuits act on the fixed input and exactly prepare a target unit data vector with declared workspace output for a unit scalar . In every standard chart of , the exactly preparable rays obtainable with at most gates form a null set whenever . Thus they cannot cover all rays. For fixed , in each standard chart all rays outside a null coordinate subset require at least gates. Thus almost every target in chart coordinates has exact preparation cost .
Facts & Assumptions
Given: , fixed finite width , a fixed countable gate-family library and countable smooth parameter atlases as in the Statement, with each family using at most real parameters per gate. The considered circuits have exact clean-workspace output.
The standard projective patches have smooth coordinates in (QC24 Projective Chart Dimension).
If is with , its critical value set is null (Morse-Sard for Euclidean maps).
A countable union of compact null subsets of Euclidean space is null; the proof selects finite rational-cube covers by their least codes without choice (Countable Union of Compact Null Sets Is Null).
A nonempty Euclidean open set is not null in the closed-cube-cover sense (A Nonempty Euclidean Open Set Is Not Null).
Projecting the circuit output onto the all-zero workspace branch yields the data vector used for its projective chart coordinates (QC24 Clean Ancilla Output Projection).
Nullity in the closed-cube-cover definition passes to subsets (Measure zero and content zero in by countable and finite cube covers).
A tensor map acts factorwise on elementary tensors, and the fixed register order determines each local gate embedding (Tensor products of finitely many linear maps).
A finite circuit denotes the ordered product of its local gate embeddings in chronological layer order (Denotational semantics of a finite layered circuit).
Rational numbers are countable and dense in the reals, so for each integer rational open cubes form a countable basis for , and every open subset is covered by countably many rational closed cubes contained in it ( is countably infinite, The rationals embed densely in the reals).
A singleton in , , is compact and null: every open cover has a one-set subcover, and the singleton itself is a degenerate closed cube of zero volume (Measure zero and content zero in by countable and finite cube covers).
Every closed cube in , , is compact by Heine–Borel (Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line).
Proof
Given: As in Facts & Assumptions.
Fix one gate topology, one choice of its gate-family parameter charts, and one standard target patch from [F1]. The product of the finitely many selected open parameter charts is open in . By [F8] and [F9], each local embedding has matrix entries that are fixed finite linear combinations of the local gate entries, and the circuit matrix is a finite ordered product of those embeddings. Its entries are therefore finite sums of products of smooth gate-matrix entries. The projected data vector from [F6] depends smoothly on . The set is open. Under the fixed real/imaginary identification , the chart map is on .
The parameter dimension is at most , so the derivative of has rank at most at every point; every point is critical. With target dimension and , the hypothesis holds, and [F2] makes null.
The gate-family choices and parameter charts are countable, and at fixed there are finitely many wire placements. By [F11], finite tuples of their natural codes, with their tuple length, form a countable set. For a fixed tuple, if its parameter dimension is , then is empty or a singleton, whose image is compact null by [F12]; these images are indexed by the countable topology/chart-tuple codes. If , rational coordinates are coded by [F11], and [F10] gives a countable cover of the open set by rational closed cubes whose closures lie in . By [F13], each such cube is compact, so its image is compact by [F4] and null as a subset of the Sard-null image in step 2.1 by [F7]. Pairing each topology/chart-tuple code with each rational-cube code by [F11] makes this part of the entire family countable as well. Together with the singleton images, these form a countable family of compact null sets. Applying [F3] to this family shows that all exactly preparable rays in the patch have a null coordinate image.
If every ray were exactly preparable with gates, then the coordinate image of all rays in the chosen standard patch would be and hence would contain a nonempty open cube. Step 3.1 makes that image null, while [F5] says a nonempty open cube is not null. This contradiction proves the noncoverage claim for each patch.
Set . Then , so steps 1.1–2.1 with budget show that the targets exactly preparable with at most gates form a null set in each chart. Every target outside this null coordinate subset therefore requires at least gates. For fixed , this is .
QC24 Discrete Alphabet Approximate Preparation Lower Bound
Statement
Fix real numbers and . Let integers , , , and be fixed, with , gate-alphabet size , and maximum arity . For an integer gate budget , every circuit starts on and must return the workspace exactly to zero, with its data output within projective chord distance of the target ray. If a circuit family prepares every -dimensional pure-state ray and each circuit in the family has at most gates in this model, then as , with constants depending on . In particular, if , then .
Facts & Assumptions
Given: The fixed finite-alphabet width model with exact clean-workspace output, fixed integers , , and fixed in the Statement, and the ray-packing and circuit-count bounds below.
The page contains at least rays pairwise separated by at least in (QC24 Signed Ray Packing Code).
At width , a length- circuit has at most descriptions, with (QC24 Finite Width Topology Count).
and the phase-minimum is attained (Projective Chord Distance Formula).
The Euclidean norm induced by the root inner product satisfies the triangle inequality (The inner-product norm is definite, homogeneous, and satisfies the triangle inequality).
Proof
Given: As in Facts & Assumptions.
A single output ray cannot be within of two distinct packed rays. Indeed, if and , [F3] gives unit phases with and ; then [F4] gives , contradicting [F1]. Thus each circuit covers at most one packed ray.
Put . By [F2], the number of descriptions of length at most is . If these circuits approximate all packed rays, step 1.1 implies , so .
Since are fixed and , there is a constant with . Put . Step 2.1 gives . If , then . If , a family with has only the single zero-gate output ray, whereas the packing in [F1] contains at least two rays for ; hence . Thus . If , then and also , so , giving .
QC24 Finite Alphabet Exact Nonuniversality
Statement
Fix integers , , , and , with , a gate alphabet of size and maximum arity . Each circuit starts on and is counted as an exact state-preparation circuit only when its unconditional output is for a unit scalar and a unit data vector . The set of data rays exactly reachable by finite circuits is countable, so it is not the full ray space.
Facts & Assumptions
Given: A finite gate alphabet of size and maximum arity , fixed finite width , , and the stated clean-workspace output convention.
If , at fixed width and finite alphabet there are finitely many circuit descriptions of each length (QC24 Finite Width Topology Count).
The real numbers are uncountable, so an injective copy of in the ray space witnesses its uncountability ( is uncountable (Cantor's nested intervals, 1874)).
Pure-state rays identify nonzero vectors exactly up to nonzero scalar multiplication (Pure state and ray).
Proof
Given: As in Facts & Assumptions.
If , the empty alphabet allows only the empty circuit, so at most one ray is reachable. If , put . Fix a finite listing of the alphabet and order wire tuples lexicographically; then [F1] gives at most descriptions of length . Code each description by its length and its base- index, then apply the fixed pairing bijection of [F4] to these two natural numbers; this gives an injection of all finite descriptions into . In either case the reachable rays form a countable set without a choice principle.
The map from into the rays of is injective: if , comparison of the coordinate gives , then comparison of the coordinate gives . These nonzero vectors define rays by [F3]. By [F2], the ray space is uncountable and cannot equal the countable reachable set.
QC24 State Preparation
Definition
Input and output. Input an integer and an explicit exact table in the ordered basis convention of Big-endian encoding of ordered bit strings. If the table length is not or , return INVALID. Otherwise return a finite continuous-gate circuit which prepares the ray from , together with its classical angle table. When the table is supplied as a dense fixed-width bit encoding, read all bits of each real and imaginary coordinate before computation. Arithmetic is ideal and exact: it supplies complex arithmetic, absolute value, exact zero tests and square roots; for not both zero, returns the unique with and . For , is the unique principal angle in the polar form of Every nonzero complex number has a unique polar form with and , so by , , and . No Turing bit-time bound is asserted.
Exact gate-family operation model. Interpret this procedure's output in the ideal continuous-angle model with exact one-qubit gates and for arbitrary real , the two-qubit gate with the control first, and the fixed gates (The Pauli matrices) and Toffoli (Toffoli and Fredkin gates). Each occurrence of any of these gates costs one gate occurrence; the operation model explicitly grants controlled access to every (Controlled Unitary Access). The phase gate is unitary since (, , and ). This is an ideal parameterized gate-family model, distinct from a finite fixed-matrix alphabet. It does not assert that arbitrary-angle gates are exactly available from a finite alphabet or give their finite-alphabet cost.
Prefix norms and rotations. For every binary prefix of length at most , set
For define
in basis . This is the -axis rotation in the QC07 exponent convention; Pauli formula for an axis rotation gives this matrix and its unitarity. Define with the control as the first wire, following Controlled Unitary Access. For each and each length- prefix in lexicographic order, skip the operation if ; otherwise set and apply to bit , controlled on the first bits equaling . Length zero means no control. Thus no division is performed on a zero-norm branch.
Exact bounded-arity control construction. For a prefix rotation with control bits, conjugate each zero-control bit by so the requested condition becomes all ones. Allocate clean work bits . Compute with one Toffoli; for , compute with one Toffoli. Apply to the flag and target bits, with the flag as the control, then apply the Toffolis in reverse order and undo the conjugations. For , apply directly when the requested pattern is ; for pattern , conjugate that controlled gate by on its control wire. For , apply directly to the target bit. For a leaf phase with , use the same flag construction and apply to the final work bit instead of a controlled rotation. A one-bit leaf phase uses directly for pattern and for pattern . The work bits return to zero after each operation and are reused; no multi-control gate is assumed as a primitive.
Leaf phases. For a basis string , define . Let be the lexicographically first index with . Put . For each other with , put and, unless , apply using the control construction above. The reference leaf needs no phase, and zero entries and zero relative phases require no gate. Every nonzero output amplitude is then , including the reference leaf, so the output is the target ray. When , the valid table has one unit-modulus entry and the empty circuit already prepares its unique ray.
Isometry Completion and Synthesis
Statement
For integers , every exact isometry is completed by Isometry Unitary Completion to a unitary matrix whose first columns are those of . If integers satisfy and , the full unitary acts as on the clean-input subspace . By the exact QSD theorem Exact QSD CNOT Upper Bound, for this uses at most CNOT gates plus one-qubit gates; for it uses no CNOT gates. When , necessarily and is a scalar on . The matrix completion remains valid, but the stated circuit syntax has no zero-arity gates, so the empty circuit realizes only the identity; exact circuit synthesis at width zero is asserted only when . For , QSD is applied directly to , without synthesizing and an extra permutation circuit separately. The output coordinates are retained: extra output wires may contain part of the encoded state and are not required to return to zero. No classical synthesis bit-time bound is asserted.
Facts & Assumptions
Given: An exact matrix with orthonormal columns, ordered qubit bases when and are powers of two, ideal exact arithmetic, and the exact QSD theorem described below.
Isometry Unitary Completion initializes its list with the columns of and appends each nonzero residual after normalization.
The root inner product is linear in its first argument, conjugate-linear in its second, and induces the norm (Real and complex inner-product spaces and their induced length).
The ordered bit-string encoding is big-endian: has index and the word has that same index at width (Big-endian encoding of ordered bit strings).
A finite-dimensional unitary is an invertible linear isometry, and a square norm-preserving operator is unitary (Linear isometries, and orthogonal or unitary operators on finite-dimensional inner product spaces).
An injective endomorphism of a finite-dimensional vector space is surjective (Rank-nullity: ).
Every gate has positive arity, so every layer on zero wires is empty and every zero-wire circuit denotes the identity (Finite circuit syntax and ordered wire labels, Denotational semantics of a finite layered circuit).
Exact QSD CNOT Upper Bound proves the exact no-ancilla QSD CNOT bound, including terminal diagonal extraction and migration, and treats widths zero and one separately.
Proof
Given: As in Facts & Assumptions.
The initial list is orthonormal by the input hypothesis. If the current list is orthonormal, then for each the residual satisfies by [F2] and orthonormality. If it is nonzero, division by its positive norm gives a unit vector orthogonal to every previous column; if it is zero, already lies in their span. Thus each update preserves orthonormality and puts in the span of the updated list.
After the finite loop, every standard basis vector lies in the final span by step 1.1. The standard basis spans , so the final orthonormal list spans and has exactly vectors. If is its expansion in that list, orthonormality and [F2] give ; the same coordinate calculation for the standard basis gives . Thus the column map preserves norms and is injective by [F4]. By [F5] it is surjective in finite dimension, hence invertible; as a square norm-preserving map it is unitary by [F4]. Its first columns are the original list, namely .
For , , [F3] identifies with the standard basis vector among the first vectors. The declared permutation obeys . The first columns of then give for every computational basis input ; linearity gives the identity on the entire clean-input subspace.
For , is a unitary on a positive-width register, so applying the QSD interface [A1] directly to this matrix gives the stated CNOT bound, with its one-qubit case at . The permutation is absorbed into the target matrix before synthesis, so no separate permutation gate count is added. If , then and is a scalar on . By [F6], the only zero-wire circuit denotes , so a circuit realizes this scalar exactly only when ; the matrix completion of steps 1.1–3.1 does not itself claim a general zero-width synthesis. The construction places no condition on the output coordinates beyond the isometry equation, so it makes no claim that the extra output wires are clean.
QC24 Prefix Norm Rotation Identities
Statement
For a prefix with and , the angle in QC24 State Preparation satisfies
Facts & Assumptions
Given: A valid normalized amplitude table and a prefix with and .
The procedure defines as the square root of the sum of squared amplitudes over strings extending , and uses (QC24 State Preparation).
Proof
Given: As in Facts & Assumptions.
Every string extending extends exactly one of or , so splitting the finite sum in the definition of the prefix radii gives . Both child radii are nonnegative and , so the pair is not . By the declared convention, lies in and has cosine and sine .
Because , substituting gives both displayed identities, including the cases where exactly one child radius is zero.
QC24 State Preparation Rotation Level Upper Bound
Statement
For an integer , , and a normalized explicit amplitude table, QC24 State Preparation exactly prepares the target ray using at most prefix rotations and at most leaf phases. The specified Toffoli flag chain expands each operation into bounded-arity gates using at most reusable clean work bits, so the complete circuit has gates and serial depth, in addition to the data qubits. For , the ray is trivial and no gate is needed.
Facts & Assumptions
Given: The exact algorithm, a normalized table , and .
The algorithm applies prefix rotations with angles determined by the child subtree norms, uses the stated clean Toffoli flag chain, and applies each nonzero relative leaf phase after subtracting the reference argument (QC24 State Preparation).
At a positive-norm prefix, the rotation half-angle has cosine and sine equal to the child-radius ratios (QC24 Prefix Norm Rotation Identities).
Toffoli maps to and preserves its two control bits (Toffoli and Fredkin gates).
A controlled operation on a control-first register applies its target unitary exactly on the control- subspace and the identity on the control- subspace (Controlled Unitary Access).
The Pauli matrix interchanges computational basis vectors, for (The Pauli matrices).
The ordered bitstring encoding places table entry at basis vector (Big-endian encoding of ordered bit strings).
In the algorithm's convention, has first column and is unitary (Pauli formula for an axis rotation).
The ordered computational basis is orthonormal (The computational basis is orthonormal).
A finite layered circuit denotes the chronological product of its local gate embeddings (Denotational semantics of a finite layered circuit).
Every nonzero satisfies for the principal argument, and for real (Every nonzero complex number has a unique polar form with and , , , and ).
Proof
Given: As in Facts & Assumptions.
Before any rotation the only nonzero prefix amplitude is the empty-prefix amplitude because the table is normalized.
Consider a computational-basis control word . Conjugating each zero-control by turns the requested pattern into all ones by [F5]. Starting from zero work bits, [F3] first sets and then, successively, ; induction on shows the final flag is exactly on the requested control pattern. The controlled operation in [F4] therefore acts only on that branch and changes neither the controls nor the flag. The basis action [F3] shows each Toffoli is its own inverse, and [F5] shows is its own inverse, so reversing the Toffoli list and undoing the conjugations restores all work bits to zero. For a leaf phase, the phase gate is diagonal and also leaves the flag's bit value unchanged. For one control the direct controlled block has the same branch action, and for no controls the one-qubit block acts on every branch. The Toffoli and are basis permutations, the rotations are unitary by [F7], and controlled blocks are unitary by [F4]; the phase gates are unitary by [F10]. The basis calculation extends linearly to superpositions under [F8] and [F9].
Suppose at depth every prefix has amplitude on under the basis order in [F6]. If , [F1], [F2], and [F7] split that amplitude into the nonnegative real amplitudes and on the two child prefixes. Step 1.2 shows the rotation is confined to the selected prefix branch; distinct length- prefixes therefore leave each other's amplitudes unchanged. If , both children have radius zero and the zero branch remains zero without division. Thus induction gives the nonnegative real leaf amplitude at every leaf.
Put for the first nonzero leaf. For every other nonzero leaf, [F10] gives . The reference leaf has unchanged amplitude , so the same scalar multiplies every nonzero coordinate. A zero relative phase needs no gate. Its flag ladder restores the work bits by step 1.2, and a diagonal phase leaves the flag's bit value unchanged. Zero leaves stay zero. Thus the joint output is with the reused workspace zero, and its data ray is exactly .
The number of prefixes at depths through is , and there are at most leaf phases. A control of bits uses Toffolis, at most gates for zero-control conjugations, and one controlled one-qubit block; it uses clean work bits. Prefix rotations have controls and leaf phases have , so the maximum work count is ; the cases use constant resources. Since , each of at most operations costs gates and serial depth, and step 1.2 shows the same work bits are restored and reused. For , both operation lists are empty and the normalized one-entry table has a single ray.
Amplitude Table Precision and Compilation
Statement
Let integer , , unit vector , and integer be given. Let have each real and imaginary coordinate within of the corresponding coordinate of . Put and assume . Then and . For , the unique data ray is prepared by the empty circuit with zero error. For , prepare the normalized table by QC24 State Preparation, and suppose its exact continuous circuit has an integer of unitary blocks on the data and clean work register, each acting on at most a fixed integer wires independent of , with an integer clean work qubits and . For any , choose a rational internal budget with and compile each complete, unconditional block with projective operator-norm error at most . Then the compiled joint output ray is within projective chord distance of .
For fixed block arity, the declared compiler interface yields finite-alphabet gates, with . If the rational table is supplied in a dense fixed-width encoding with bits per real/imaginary coordinate, it has input bits; the preprocessing reads this encoding and computes the exact normalization internally. Table input and preprocessing are separate from coherent gate cost. The statement asserts no uniform bit-time for angle computation or SK compilation; the separate proposed supplier Finite-Precision Bit Complexity of the SU(2) Solovay–Kitaev Compiler and certified angle evaluation remain held obligations.
Facts & Assumptions
Given: A unit vector , a rational table , the error and block-compiler hypotheses in the Statement, an unconditional preparation circuit on data qubits and its clean work register, and a fixed maximum block arity independent of .
The preparation algorithm acts on an explicit table and produces the target ray by prefix rotations and leaf phases (QC24 State Preparation).
Its exact circuit has bounded-arity gates (QC24 State Preparation Rotation Level Upper Bound).
Projective chord distance is the infimum Euclidean distance over unit phases by definition, and this infimum is attained (QC24 Projective Chord Distance, Projective Chord Distance Formula).
A rational table with a fixed-width encoding of bits per real/imaginary coordinate occupies bits, and the model separates input scanning from quantum gate cost (QC24 State Preparation Cost Model).
The Euclidean norm induced by the root inner product satisfies the triangle inequality (The inner-product norm is definite, homogeneous, and satisfies the triangle inequality).
The operator norm is the maximum of over unit input vectors, and a unitary therefore has operator norm (The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||).
The operator norm is submultiplicative (The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2).
Between any two distinct real numbers lies a rational number (The rationals embed densely in the reals).
The ordered tensor factors give an orthonormal product basis, and wire permutations permute that basis (Ordered tensor registers, their inner product, and wire permutations).
Every block on wires, , has an exact finite no-ancilla circuit over one-qubit gates and CNOTs; the stated QSD bound is at most CNOTs for , and none is needed for (Exact QSD CNOT Upper Bound). Put and . Step 1.2 derives a uniform one-qubit factor bound from this CNOT bound by combining gates on each wire.
The fixed-arity supplier Fixed-Arity Unitary Error Budget proves whole-block projective error and gate-count bounds for a supplied exact one-qubit/CNOT factorization. It accepts a real target error and uses a rational dyadic tolerance internally. No improved QSD CNOT count or compiler bit-time is inferred here.
Proof
Given: As in Facts & Assumptions.
Each of the real coordinates differs by at most , so and . The reverse triangle inequality gives , hence .
Use [F8] to choose a rational with . Let the exact normalized-table circuit be . Each block has positive arity and, by [A1], an exact circuit with at most CNOTs. Scan that circuit chronologically, maintaining one pending one-qubit product on each wire. At a one-qubit gate, compose it into its wire's pending product. Before each CNOT, emit the two pending products on its control and target and reset them to identity; pending products on other wires commute with that CNOT and may remain pending. At the end emit all pending products. This preserves the exact circuit product and uses at most one-qubit factors, counting identities if necessary, so the one-qubit count is positive. Apply [A2] to this factorization with real target error ; it gives a compiled block and a unit scalar with . Each block includes all of its controls and targets before compilation; thus is a scalar on its entire support and becomes one global scalar when tensored with identities on the other wires.
Split the normalization error through : by [F5], .
Insert the intermediate products between and to obtain the telescoping sum with one block difference in each term. Each support-level block difference, when extended by identity to the full ordered register, has operator norm at most its support norm: after permuting the product basis to group support and unused wires, write a vector as in an orthonormal basis of the unused wires, and use . The wire permutation preserves norm by [F9]. For any unit input vector, [F5] bounds the norm of the telescoping sum by the sum of term norms; [F6] gives norm for every surrounding unitary factor, so [F7] bounds each term by . Taking the maximum over unit inputs with [F6] gives whole-circuit operator error at most . Applying this to the fixed initial vector gives projective chord error at most from the exact normalized- output.
By [F3], choose a phase aligning the compiled output to the exact normalized- joint output within , and a phase aligning that exact output to within by step 2.1. Multiplying the second phase by the first and applying [F5] gives .
For fixed maximum arity , [A2] uses finite-alphabet gates per block at accuracy . Since are constants, , and , this is per block; hence blocks use gates. If the rational input table has an encoding as in [F4], it contains bits, and [F1] reads every real/imaginary coordinate in its preprocessing pass before exact normalization; this input scan is separate from quantum gate count. No angle-computation or compiler bit-time follows from these bounds.
Remarks
The compilation error is projective for each complete unconditional block. The exact QSD construction supplies the factorization needed by the fixed-arity compiler for every block in the stated arity bound. Rephasing a one-qubit gate before adding its control is not justified: that scalar becomes a relative phase between control branches. The separate bit-complexity supplier is not used as a premise here because no uniform bit-time claim is made.
QC24 Explicit State-Preparation Cost Guarantee
Statement
For an integer , , and target amplitudes supplied by the explicit normalized amplitude-table model of QC24 State Preparation Cost Model, QC24 State Preparation reads the dense table and produces an exact target ray using at most variable-control rotations and at most phases. The local clean Toffoli ladder yields bounded-arity gates and serial depth using at most clean work bits in addition to the data register. If an integer specifies a dense fixed-width table with bits for each real and imaginary coordinate that decodes exactly to the input amplitudes, it contains input bits; this procedure's explicit full-table preprocessing scan reads those bits. The statement assumes neither QRAM nor exact arbitrary-angle implementation in a fixed finite gate alphabet. It gives no uniform bit-time bound.
Facts & Assumptions
Given: The declared cost model, a valid dense table with entries, and its fixed-width encoding when a bit representation is specified.
The model separates table length and classical preprocessing from continuous or finite-alphabet gates, width, ancillas and error metric; an encoding with bits per real/imaginary coordinate has bits when it decodes exactly to the table (QC24 State Preparation Cost Model).
The exact procedure reads an explicit table, computes all prefix norms and phases, and uses the specified bounded-arity flag construction (QC24 State Preparation).
The exact target ray is prepared using at most prefix rotations and at most leaf phases, with bounded-arity gates and serial depth and at most clean work bits (QC24 State Preparation Rotation Level Upper Bound).
Proof
Given: As in Facts & Assumptions.
By [F2] the table is a finite explicit classical input, and by [F3] its prefix procedure prepares the target ray. There are prefix nodes before zero-norm branches are skipped, so the circuit uses at most rotations; there are at most leaf phases. The same theorem supplies the bounded-arity gate, serial-depth and workspace counts.
If the exact table is supplied in the fixed-width encoding of [F1], each of entries contains bits for its real coordinate and for its imaginary coordinate, with no per-entry index because the order is fixed. The procedure reads each encoded coordinate in its full-table preprocessing scan to form subtree norms and phases, so it reads exactly input bits in this bit-read model. This is an input and preprocessing charge separate from coherent gate cost; no alternate oracle or compressed-input lower bound is asserted.
The gate construction uses continuous-angle operations under ideal exact arithmetic. A fixed finite alphabet only supplies the separate approximation claim once its compiler hypotheses are verified; the present exact gate-count statement makes no arbitrary-angle finite-set or uniform Turing-time claim.
5 · Examples, counterexamples and false statements
None yet.
Sources
- Jiří Lebl, Basic Analysis II, Section 10.3, Outer Measure and Null Sets
- Jiří Lebl, Basic Analysis II, Section 10.1, Riemann Integral over Rectangles
- John Preskill, Quantum Computation, Chapter 5, Classical and Quantum Circuits
- John Watrous, The Theory of Quantum Information
- John Watrous, The Theory of Quantum Information, Chapter 1
- Vivek V. Shende, Stephen S. Bullock, and Igor L. Markov, Synthesis of Quantum Logic Circuits
- Christopher M. Dawson and Michael A. Nielsen, The Solovay–Kitaev Algorithm