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Qubitization and Near-Optimal Hamiltonian Simulation
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Block Encodings
- Boolean Circuits and Nonuniform Complexity
- Bounded Linear Operators and Quotient Spaces
- Channels, Kraus, Choi, and Stinespring Representations
- Circuit Syntax, Denotational Semantics, and Measurement
- Classical and Quantum Error-Correcting Codes
- Clifford+T Exact and Approximate Synthesis
- 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
- 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
- Continuous-Time Quantum Walks
- 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
- Equivalent Forms of Completeness
- 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
- Further Trigonometric Identities and Inverse Functions
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hamiltonian Simulation and Product Formulas
- 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 Combinations of Unitaries and Taylor Simulation
- 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
- 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
- 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
- Quantum Signal Processing
- Quantum Singular-Value Transformation
- 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
- 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
- Splitting Fields
- State Preparation, Isometry Synthesis, and Limits
- 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
- Symmetric Polynomials and the Fundamental Theorem of Symmetric Functions
- 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 Polynomial Hierarchy and Relativization
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- 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
- Turing Machines, Configurations, and Computation
- Uniform Circuit Families, Promise Problems, and Complexity
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
An exact Hermitian block encoding supplies the normalized compression , while the access record separately charges , , controlled calls, projector reflections, state preparation, phase synthesis, and circuit costs. The signal-subspace lemma derives the paired two-dimensional actions, including the zero signal value and the collapsed endpoints. The qubiterate is a unitary rotation with eigenphases ; alternating signal calls and projector phases implement the corresponding scalar QSP sequence.
For , the locally proved Jacobi–Anger expansion splits into real even and real odd Chebyshev polynomials. The binding OP4 procedure certifies the factorial tail, rounded coefficients, complementary-polynomial phase lists, controlled relative phase, and finite LCU/OAA circuit. Its correctness guarantee is an operator-norm bound on the clean-input isometry. Auxiliary output registers may retain leakage bounded by that guarantee; tracing them out gives the stated reference-system trace-distance bound. A small-time wrapper returns the identity when , which closes the uniform degree claim near .
The supplied-permutation sparse construction requires explicit reversible in-place row and column location permutations, their inverses, a costed uniform state preparation, and value rotations. It accepts any certified entry bound and uses normalization ; choosing gives the tight normalization. Standard XOR row-location access alone does not supply an in-place permutation at constant cost. The no-fast-forwarding theorem is scoped to the sparse row/value oracle: its path family reduces simulation to bounded-error parity and yields an query lower bound there, without asserting a lower bound for arbitrary block-encoding access.
The optional sorted-XOR sparse-access construction charges a logarithmic number of location queries, including clean slot erasure, inverses and coherent controls. Its support lists may include known zero-valued entries without requiring a real-zero test. The separate clean-subspace hybrid proves how simulator dilations compose and reverse on ideal clean-work trajectories while actual work may be dirty. The original supplied-permutation sparse mode and its sharper count remain available.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Clean-input isometry error bounds output trace distance
Statement
Let be linear isometries between finite-dimensional complex inner-product spaces, meaning , and assume and . For every nonzero finite-dimensional reference space and density matrix on (a positive semidefinite matrix of trace one), define and on the ordered product bases. Define the partial trace by and define as the sum of singular values and . The output density matrices satisfy . Tensor products use the ordered product orthonormal basis and product inner product; the displayed matrix formula fixes the partial-trace convention used here. For column vectors, denotes conjugate transpose and the rank-one matrix with entries .
Facts & Assumptions
Given: The finite spaces, maps , and density matrix in the Statement.
Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition: finite maps have orthonormal singular bases; their operator norm is the largest singular value.
Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: a finite self-adjoint endomorphism has an orthonormal eigenbasis with real eigenvalues.
Proof
Given: As in Facts & Assumptions.
Put , and let be an orthonormal basis of . By [F1], diagonalizing in a right singular basis shows that has the same largest eigenvalue as : its eigenvectors are the products of right singular vectors of with the , and its eigenvalues are the corresponding squared singular values. Therefore . For every unit vector , the vectors and have norm one and satisfy .
For , positivity follows from for every ; the same holds with . For a vector , the entry-sum formula gives , and likewise for ; hence both reduced outputs are positive. That formula also gives . Since , cyclicity of finite matrix trace gives for both outputs. Thus are density matrices.
In an orthonormal basis of , write , where , , and is unit if . The matrix of on this span is Hermitian, has trace zero and determinant , so its eigenvalues are ; outside the span it is zero. Thus . Also , because the difference of the right side and left side is . This proves for every pure input, including .
Let be Hermitian on , and set . For a matrix on , define on by applying to the coordinates and leaving the coordinate fixed. The entry-sum definition of partial trace gives . Reordering the product basis from to conjugates to , so by the same singular-basis argument as in step 1.1. For any square matrix , [F1] gives its singular value decomposition, and since every diagonal entry of a contraction has modulus at most one. Taking the supremum over , and using the equality case in a singular value decomposition of , proves . This dual formula also gives the triangle inequality and homogeneity for by applying it to a sum and scalar multiple.
For a pure input , step 2.1 bounds the trace distance before discarding , and step 2.2 shows the partial trace cannot increase the trace norm of the Hermitian difference. Hence the reduced output distance is at most . For general positive trace-one , [F2] gives a spectral decomposition with and . The maps and partial trace are linear; the triangle inequality and homogeneity from step 2.2 therefore bound the reduced output distance by .
Sparse parity-path transfer and norm
Statement
Let be the complex inner-product space with orthonormal basis . For , define , and put . For , let be the normalized uniform sum of basis vectors of Hamming weight ; define . On a separate path space with orthonormal basis , define to have off-diagonal entries between and for every 1≤i≤n, and all other matrix entries zero.
Let , , and . On the basis , put Write . The basis permutation conjugates to two copies of . The counting identity proved below gives , under the isometry . Consequently and
Facts & Assumptions
Given: A positive integer , a bit string , the finite matrix dynamics of Finite Hermitian matrix dynamics, and the root convention of Dirac Notation Conversion.
Finite Hermitian matrix dynamics and , and the complex exponential extends the real exponential: finite Hermitian evolution is by spectral functional calculus, and the scalar exponential satisfies .
Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: a finite Hermitian matrix has an orthonormal eigenbasis.
, , and : , with the stated quarter-turn values.
Proof
Given: As in Facts & Assumptions.
The map permutes the finite orthonormal basis and satisfies , so it is unitary. In a term indexed by , the endpoints become and . Since , the second bit is ; the edge is therefore an ordinary adjacent path edge with the spectator bit unchanged. Thus is two identical path matrices , one for each bit , and fixes while sending to .
Let be the normalized uniform superposition of -bit strings of Hamming weight , and set . Counting the downward flips and upward flips gives for every . Thus has the stated path weights on the symmetric subspace.
Each permutes an orthonormal basis, so for one has , and hence . The vector triangle inequality gives , which yields , so ; unitary conjugation in step 1.1 gives .
For each sign string , put . Factorizing the finite inner-product sum gives , so these vectors form an orthonormal basis. Flipping bit gives . By [F1]–[F2], acts on by . The scalar addition law in [F1] factors this as , and Euler's formula [F3] gives each factor as . Hence . This maps to . The path basis identifies with ; step 1.1 then sends the endpoint to . Since , evolution for is , which is the transfer established above.
Qubitization access model
Definition
Let be a finite-dimensional complex inner-product space, let on , and let satisfy . Put . Let be a unitary on an ancilla-system space , let be the isometry , and let . An exact Hermitian block encoding is the data with . The matrix is Hermitian; itself need not be Hermitian.
The signal-register reflection is . The access record lists, separately, each supplied call to , , controlled-, controlled-, and , together with their circuit, query, gate, width, ancilla, precision, and classical-generation costs from Block-encoding oracle and implementation cost model and Circuit-family and computation cost model. A state description or block matrix alone supplies none of these calls. A controlled reflection is also separate. Any PREPARE, SELECT, sparse-location, value, or coefficient operation used by a construction is a separately supplied, reversible interface with its inverse and cost recorded.
Here is the standard-form qubiterate for this access instance. Add a flag qubit and define , , , , and . Implementing requires the separately declared controlled- and controlled- calls; , the preparation of , and the reflection about also have declared costs. Qubitization lifts scalar QSP ↗ proves its compressed block and spectral relation.
For the QSVT simulation sequence, the signal operations are the alternating calls interleaved with the projector phases from QSP signal matrix and ordered phase sequence. Their exact phase list, parity, real-polynomial extraction, controls, inverse calls, and phase-synthesis precision are part of the supplied circuit description. The scalar polynomial pairs must satisfy Admissible polynomial pair for Hermitian-reflection QSP; singular-value transformations use the convention of Odd and even singular-value transforms.
Hamiltonian-simulation polynomial target
Definition
For a finite Hermitian with exact block-encoding normalization , , and real time , put . On the scalar target is ; if , then , the eigenvalue of the finite matrix evolution Finite Hermitian matrix dynamics.
Define the Chebyshev polynomials by , , and for . For , define the Bessel coefficients used here by the series For , the ratio of consecutive absolute summands is , so the series converges absolutely. If , at most its summand is nonzero, so it also converges absolutely. Thus the series defines before any estimate is used. The exponential uses the series convention of The complex exponential by its power series, whose absolute convergence is supplied by The complex exponential series converges absolutely for every complex argument. This definition specifies the target and coefficient series; it does not assert a Jacobi–Anger expansion or a truncation bound. A real QSP or QSVT theorem alone does not implement this full complex, non-parity target; the finite even/odd combination, complementary-polynomial certificates, normalization, and controlled relative phase are separate operations in the binding simulator.
Hermitian block-encoding signal subspaces
Statement
Let be an exact Hermitian block encoding from Qubitization access model, and choose an orthonormal eigenbasis . Put and . For , define The are orthonormal in , as are the . In the paired input basis and output basis , has matrix The unitary maps in the reverse direction by the same matrix. If , the complement is absent and ; no division by zero is used. The value is an ordinary two-dimensional signal plane.
Facts & Assumptions
Given: A finite-dimensional self-adjoint , an exact block encoding, and the associated signal projector .
Qubitization access model: and ; taking adjoints and using gives .
PUE singular-space invariant decomposition: the positive singular pairs of a projected unitary give orthogonal paired two-dimensional subspaces; their endpoint complements are absent.
Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: a finite-dimensional self-adjoint endomorphism has an orthonormal eigenbasis with real eigenvalues.
The adjoint is characterised by : , so a unitary and its adjoint preserve inner products.
Finite block encoding: the normalized compression has operator norm at most one, hence .
Proof
Given: As in Facts & Assumptions.
Fix an eigenvector with unit norm and write . From [F1], and , because . Since preserve norm, orthogonal decomposition by gives . For , the displayed are therefore unit vectors in .
If , orthogonality of eigenvectors and [F4] give , while . Subtracting the projected components shows ; the same calculation for proves orthogonality of the .
Decomposing into its and parts gives . Also Division by yields , which is the second column of the displayed matrix. Taking adjoints gives the reverse-basis action of .
If , the orthogonal complement of has squared norm , so ; the same argument for gives . Thus the endpoint plane collapses. At , step 1.1 has , so both normalized vectors are well-defined. The mutually orthogonal paired spaces and endpoint lines exhaust the clean input and output signal spaces by the SVD decomposition in [F2]; their orthogonal residual complements stay in and cannot contribute to the clean compression.
Qubitization lifts scalar QSP
Statement
Let be an exact Hermitian block encoding of , with and . The qubiterate of Qubitization access model has, on each eigenvalue , a two-dimensional invariant signal space on which it is a rotation with eigenvalues ; at the space collapses to the corresponding eigenline.
For a phase list , alternating calls and projector phases acts on each paired signal space as the scalar Hermitian-reflection QSP sequence of QSP signal matrix and ordered phase sequence. If is its upper-left polynomial, the clean compressed block is on all eigenspaces simultaneously, with the endpoint and null directions handled by the same polynomial evaluation. Admissible phases are supplied by Constructive factorization of an admissible QSP polynomial pair; translating to the SU(2) convention carries the scalar , which remains a relative phase under control.
Facts & Assumptions
Given: An exact Hermitian block encoding, its declared controlled/inverse access, and a scalar QSP phase list.
Qubitization access model: , and uses , , and .
Hermitian block-encoding signal subspaces: on eigenvalue , the paired signal spaces have matrix , with endpoint and residual cases specified.
QSP signal matrix and ordered phase sequence: , where .
QSVT action on singular subspaces: the alternating sequence with the left/right projector phases acts on each signal plane by its scalar QSP matrix; its compressed block is the corresponding singular-space polynomial, including null and endpoint spaces.
Constructive factorization of an admissible QSP polynomial pair: every admissible polynomial pair has an exact real phase list for the fixed Hermitian-reflection signal convention.
Projector Reflections Are Hermitian Unitary Involutions: is on and on .
Hermitian-reflection and SU(2) QSP convention conversion: the SU(2) phase list conversion contributes the explicit scalar ; under a control this is a branch-relative phase.
Proof
Given: As in Facts & Assumptions.
In control-first order, so and . Also because is self-adjoint. For a unit eigenvector , put , , and . The compression identity gives for a unit when . Applying then gives . The reflection has matrix in this basis, so has matrix . Its characteristic polynomial is , whose roots are . If , the orthogonal component has norm zero and the clean eigenline has eigenvalue .
On a paired signal plane from [F2], is diagonal with entries , so is . The signal calls switch between the input and output bases of [F2]; therefore their chronological product, written in matrix order, is by [F3]. The determinant and polynomial-entry conventions are those proved by the scalar QSP supplier in [F5].
Every clean system vector is a finite linear combination of eigenvectors of . Step 1.2 acts on each eigenvector by the same scalar polynomial , so linearity gives the compressed operator . Repeated eigenvalues cause no ambiguity because the same scalar is applied to every vector in that eigenspace; zero signal and endpoint spaces have the actions specified in [F2] and [F4].
The reflected convention in step 1.2 is the one used by [F3], [F4], and [F5]. If the implementation instead uses the SU(2) signal, [F7] supplies the scalar ; applying it to only the active control branch changes a relative phase, so it must be implemented as stated rather than discarded as an uncontrolled global scalar.
Bounded-error parity query lower bound
Statement
Any quantum query algorithm that, on every , outputs with error at most using the coherent XOR oracle for makes at least oracle queries.
Facts & Assumptions
Given: The finite parity promise task and the coherent XOR oracle of Coherent XOR function oracle.
Promise problem: correctness is required on every input in the declared promise.
Query-model convention: the finite register starts in a fixed input-independent state, each inter-query unitary is independent of , and the output is a designated computational-basis bit measured after the last unitary. A -query algorithm has at most oracle calls on every input. These are conventions of the query model, not consequences of the circuit-description encoding in Full-list uniform quantum circuit family.
Coherent XOR function oracle: on every basis string, with a query charged only for the named oracle.
A total Boolean XOR oracle is unitary and involutive: the total XOR oracle permutes the finite basis and is a unitary involution even when the queried function is noninjective.
Proof
Given: A -query algorithm with the stated worst-case error.
Put . On a basis query address , the matrix entries of the XOR target flip are either or ; the input address and all other bits are unchanged. Thus one oracle call raises the degree of every amplitude polynomial in by at most one. The inter-query unitaries are input-independent by [F2] and take fixed linear combinations of amplitudes. Induction over the queries shows each final amplitude is a multilinear polynomial of degree at most , replacing any by without increasing degree.
The signed output expectation is a sum of squared moduli of final amplitudes with fixed signs, so it is a real multilinear polynomial of degree at most . If , the error bound gives pointwise. Therefore the uniform average of is positive.
If , every monomial of is for a proper subset . Under independent uniform signs, , since at least one independent sign appears to the first power and has mean zero. Linearity gives , contradicting step 2.1. Hence .
Qubitized Hamiltonian simulator
Definition
Inputs. A finite Hermitian , with the certified norm promise , rational time , rational , and either (i) the exact block encoding with clean injection and , or (ii) a separately selected bounded-block-error mode with the same declared ancilla embedding and a unitary whose clean block satisfies for a supplied certified rational upper bound . The latter mode is accepted only if it passes the explicit post-degree error-budget test in steps 3 and 5. For either mode, declare the access and cost for controlled-, controlled-, both projector-phase circuits, coefficient preparation, SELECT, OAA reflections, phase synthesis, data access, and classical arithmetic. The input description also supplies terminating certified comparisons and rational-interval evaluations for the scalar arithmetic below.
Output. Return the finite circuit list and error certificate for a unitary dilation . Its success claim is on the clean-input isometry ; auxiliary output registers are approximate and are not measured to clean an unknown system input. On a failed polynomial or phase certificate, return the phase routine's declared failure status and do not report a simulator.
Generation and execution. Set and . For , return the identity circuit and zero query count. Otherwise:
-
Starting at , increase until and Evaluate each strict comparison by the certified interval routine named in OP0 of the binding operational appendix; an undecided equality is not rounded toward success.
-
For each , compute from the series in Hamiltonian-simulation polynomial target. Stop its alternating-series evaluation only after its ratio is below and its geometric remainder is at most . Store a rational coefficient and its error interval. Form . Set Thus is the rounded Jacobi–Anger truncation and its certified scalar error is ; the sign is literal.
-
Require a certified combined polynomial error at most . Then on . Put , , and , and set . Define since . In exact-block mode put . In bounded-block-error mode require the supplied rational upper bound to satisfy ; otherwise return
CERTIFICATE_ERROR. This is the explicit block-error allocation for the selected degree. Call Real bounded-polynomial QSP phase synthesis separately on the rational real targets inefficient-numericalmode with tolerance , their actual degree, and its parity. If either target is identically zero, declare degree and even parity. Retain each complementary-polynomial identity, phase list, per-phase precision and optional compiler certificates. A successful call certifies , where is the exact phase-list polynomial error and are the implemented phase-slot errors, by Correctness of QSP phase synthesis. Execute each real QSVT extraction using Quantum singular-value transformation circuit and the supplied forward/inverse signal calls of Qubitization access model, with block-compression error in exact mode or at most in bounded mode. For the bounded mode, compare each exact and singular-value transform on the perturbed and target block using Operator perturbation bound for QSVT, then average the two bounds. The phase-list polynomial mismatch and implemented phase-slot errors together contribute at most , while the averaged block perturbation is at most . Thus each extracted clean block differs from its target by at most . Since is even and odd, these singular-value transforms on Hermitian equal the ordinary polynomials and : the even values agree at , while for the odd transform a negative eigenvalue has left singular vector and therefore supplies the sign; also . Propagate every non-success status from either synthesis or extraction unchanged; do not report a simulator on a failed certificate. -
Pad both extraction workspaces to the same ordered register space. Prepare a clean coefficient qubit in . SELECT applies the block in branch and times the block in branch . Unprepare the coefficient qubit. The ideal target block is , where . Let be twice the clean block of this phase-certified when its remaining operations are exact. By the step-3 synthesis certificates and branchwise robustness from Operator perturbation bound for QSVT, In exact-block mode this bound uses . Since , the ideal target polynomial error satisfies here by finite spectral calculus and . The factor is a relative phase under any outer control and is not discarded.
-
Apply the explicit phase-corrected oblivious-amplitude-amplification unitary , where and is the complete even/odd block-combination circuit. Retain its work registers. By step 4, with , the certified OAA input error is The exact and bounded-block variants use the same OAA route; Robust one-step OAA including ancilla leakage certifies phase/block clean-input isometry error at most , including the retained-work leakage.
For the remaining implementation error, unroll the complete post-amplification circuit, including every occurrence within , both OAA reflections, the known correction, each and controlled call, both SELECT branches, coefficient preparation/unpreparation, projector-reflection implementation, and all inverses. Exclude only phase-rotation and optional phase-compiler errors already covered by the two step-3 certificates. Let be the number of remaining primitive-unitary occurrences in this unrolled list. For , require each finite-basis implementation of its ideal unitary to have a certified full-space bound ; exact supplied calls have zero error. Count repeated and controlled occurrences separately. If any implementation lacks a unitary certificate at its allocation, return
IMPLEMENTATION_ERROR. The full-space telescoping product estimate, using The inner-product norm is definite, homogeneous, and satisfies the triangle inequality and unitary norm one, gives remaining post-amplification error at most (zero if ). The complete clean-input isometry certificate is therefore at most ; returnCERTIFICATE_ERRORif a certified comparison does not verify this bound. A controlled simulator controls every whole block and the known correction, using the separately supplied controlled access and counting its implementation in the same way.
The arithmetic, coefficient series, two phase-generation calls, controlled QSVT sequences, coefficient register, SELECT, and OAA list are finite outputs; their gate, query, precision, workspace, description-bit, and classical computation costs are separate coordinates. The phase-generation call is the finite procedure of Real bounded-polynomial QSP phase synthesis, not a free source routine. A sparse input uses its actually supplied block-encoding circuit and its exact row-location, value, precision, and state-preparation costs; standard XOR sparse access alone does not imply an in-place reversible location permutation.
Unitary hybrids on a preserved clean-work subspace
Statement
Let be an isometry of nonzero finite-dimensional complex Hilbert spaces, a unitary on , and a unitary on . If , then . The same inequalities hold after adjoining an arbitrary finite reference space or coherently controlling and against identity.
More generally, let be actual unitaries on and ideal unitaries on , with . Then The empty products give zero error. Each bound concerns the clean input subspace ; none assumes that an actual intermediate state belongs to that subspace or claims a full-space approximation on arbitrary dirty-work inputs.
Facts & Assumptions
Given: The finite spaces, isometry, unitaries and error bounds in the Statement.
The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2 gives the operator-norm product inequality. Unitary multiplication preserves vector norms and hence preserves the operator norm of any compatible matrix.
Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition diagonalizes the squared norm of a finite rectangular map in orthonormal bases.
Proof
The exact identity , together with unitary invariance, gives . Thus reversing the actual circuit supplies the inverse clean-input bound, without applying a forward error promise to dirty work.
If has singular values , [F2] gives orthonormal bases in which . Tensor those bases with any orthonormal reference basis; for , . A top singular vector tensored with any unit reference vector attains equality, so . If the reference space is zero, the amplified map is zero and its bound is immediate. For coherent control against identity, the difference between the actual and ideal clean-input maps is zero on the control-zero summand and on the orthogonal control-one summand; its norm is at most . These statements apply to step 1.1 as well as to the original forward bound.
For , expansion and cancellation give Every factor outside the local difference is unitary, so each summand has norm at most . The triangle inequality yields the claimed sum. On the right of each local difference are only ideal system unitaries followed by , so this expansion evaluates that difference on clean ideal inputs. The actual later unitaries on the left propagate the error without any cleanliness assumption. For , both products are identity maps, so the difference is zero.
Clean sparse-state preparation from sorted XOR locations
Statement
Let have the exact sorted -sparse Hermitian XOR-location/value interface of Sparse Hamiltonian row and value oracles, with . An optional support-list variant is also admissible: each row lists at most distinct columns in strictly increasing order, contains every nonzero column exactly once, and may additionally list zero-valued columns. Its row and column support lists agree under transposition; in the Hermitian case the row list at supplies the column list at . The total XOR value oracle gives the exact decoded matrix, including any listed zeros. Missing slots use the same distinguished sentinel. No zero test on the matrix values is needed to form a supplied support union. An approximation to a different target matrix requires a separate operator-error bound. Put and on the -label register. Given a positive certified bound , an exact costed preparation , and costed exact value-decoding and flag-rotation arithmetic, one can construct a unitary whose clean block is . It and its actual adjoint and coherent controlled versions use QA20 location calls and value calls each. Index bits, reversible workspace, search arithmetic, comparisons, uniform preparation, flags and gate precision remain separately charged. In particular is admissible when , without computing the global maximum entry. The stronger separately supplied in-place-location interface keeps its original constant location-call count.
Facts & Assumptions
Given: The exact finite input interfaces and separately costed operations of the Statement; no gate-count or bit-time bound for the supplied exact arithmetic is assumed.
Sparse Hamiltonian row and value oracles supplies the strict nonzero-list case with total XOR maps and zero values on invalid labels. The optional support-list data in the Statement retain distinct sorted indices, all nonzero entries, a missing-slot sentinel, the transposed column support, and exact decoded values; listed zero entries are allowed explicitly as additional input data.
Coherent XOR function oracle describes reversible total-function XOR evaluation. A classical finite reversible computation may retain its history, copy its answer and uncompute the history; each named oracle call and its reversal is charged.
Finite block encoding defines the clean auxiliary compression of a unitary. Dirac Notation Conversion fixes matrix coordinates: with the root pairing linear in its first argument, the matrix entry of is .
QC24 State Preparation Cost Model charges the actual uniform-state preparation, inverse, arithmetic and controlled implementations separately.
Proof
For and , let be the listed column if and that slot exists, and otherwise; set on . This is a total function of finite bitstrings. On it is strictly increasing: actual indices are distinct, sorted and below , followed by distinct pads . All outputs are below . For an invalid , use a valid dummy query input and mask its answer, giving exactly the padded branch. The same masking handles padded physical slot strings. Every masked or externally controlled XOR evaluation can use two ordinary calls: compute the function to clean work, conditionally XOR-copy it to the desired target, and uncompute. Thus no controlled oracle is assumed free.
For each , compute a total inverse lookup by lower_bound search of the strictly increasing list . Start ; for rounds, if , set and update if , otherwise . In inactive rounds use a masked dummy call. Each active interval shrinks to at most the ceiling of half its old width, and width one becomes zero at the next round, so the stated round count suffices. If the final and , output ; otherwise output zero. Retain and then reverse the fixed finite search history after copying its answer, so all history and oracle workspace are clean. There are forward/reverse location calls including the final equality test. Strict increase gives for every , including ; off the image no inversion is asserted.
On two -bit registers apply the total permutation circuit , where the middle map is XOR evaluation of into the first register and the final map swaps the registers. The displayed clean formula is asserted only on and follows from step 2.1. Each constituent is a total reversible gate sequence on every bitstring, so its actual inverse exists with the same query order. Coherent row labels and references are untouched. Applying the separately supplied uniform preparation first gives with all additional work zero. This constructs the required row-neighbor and column-neighbor isometries; Hermiticity identifies the column list at with the row list at .
Let and , including the common zero work registers from step 3.1. Put . After , compute and apply the flag unitary with first column and second column , then uncompute values and arithmetic. Its columns are orthonormal because , so this defines . Only inserts the value amplitude; has good-flag amplitude one.
Each row and, by Hermiticity, each column has at most nonzero entries, each of magnitude at most . For any vector , by Cauchy–Schwarz. Summing over and using the row and column bounds gives , so . The zero extension has the same operator norm, so the normalization is valid for the block encoding in [F3]. By [F3], the clean matrix entry of is . For a valid nonzero , the ordered pair occurs once on each side, with coefficients and , respectively. Their root pairing is . A zero or invalid entry contributes zero even if its padded pair happens to match, since its good-flag value amplitude is zero. Every value workspace has been uncomputed, and each location workspace is zero by step 3.1, so no unmatched work suppresses the desired overlap. This proves the exact compressed block, including zero rows, , , , , padded labels and repeated sentinel replies.
Each clean location circuit uses QA20 location calls by step 1.1 and step 2.1 and step 3.1, and computing/uncomputing its value uses a constant number of value calls. Reversing the actual circuit has identical counts. Coherently controlling it controls all known gates, and step 1.1 replaces each controlled XOR query by a constant number of ordinary XOR queries; hence the same asymptotic holds. All other operations remain in [F4]'s separate cost coordinates. Finally , so is a valid supplied bound in the normalized HHL mode.
Sparse Hermitian block encoding
Definition
Input. A finite -row-sparse Hermitian matrix , with . Put and supply a certified nonnegative entry bound . Supply exact row and column enumerations. Write their neighbor labels as and , including distinct labels per row or column: every nonzero entry occurs once, and padded labels are sentinels. Supply reversible in-place permutations whose action on every valid row/slot and column/slot basis state is with their full reversible extensions, inverses, and actual query and gate costs. These permutations are explicit inputs; the row/column lists alone do not imply them. Supply an exact unitary preparing , its inverse, and their costs. A classical table is not a free state-preparation circuit. If , then ; use one flag qubit and return with normalization , whose all-zero flag compression is . Otherwise set and supply a total reversible value-decoding query that writes a finite exact encoding of on valid pairs and on invalid or sentinel pairs, together with its inverse and any controlled forms used, with their costs. Classical matrix entries alone do not provide this coherent access.
Instructions when . Put , so every queried entry obeys . On the paired row/column index registers define Let . Define by first applying , then querying , applying to the flag and unquerying the value and arithmetic workspace. The square root is the nonnegative real root. Invalid sentinel pairs use . Return with the all-zero auxiliary block designated as the encoded block. Every index, flag, value, state-preparation, inverse, controlled, arithmetic, and finite-precision operation is supplied or charged separately, including both register SWAPs and their controlled forms.
The supplied-permutation mode requires the in-place location permutations as explicit input. The total XOR row-location oracle of Sparse Hamiltonian row and value oracles does not itself supply an -query in-place permutation or its inverse. Any scan or inverse-location construction must be specified and charged before claiming such a sparse-oracle cost.
The block identity and its exact query accounting are proved in Correctness and access cost of sparse block encoding ↗.
Sorted-XOR specialization. As an alternative input mode, supply the exact sorted XOR location/value interface of Sparse Hamiltonian row and value oracles, or the sorted distinct support-superset variant allowing listed zero entries in Clean sparse-state preparation from sorted XOR locations, a certified positive bound , and the same separately charged uniform preparation and value/flag arithmetic. Execute the explicit compute-location, binary-search inverse, slot-erasure and row/column-overlap construction of Clean sparse-state preparation from sorted XOR locations. It pads the system to and returns a clean block encoding of , with normalization . The location workspace is exactly clean on every prepared slot state. Each encoding, actual adjoint or coherent controlled encoding costs original location calls and value calls; its sorting assumption is part of this mode's input. If , choose without computing a maximum. Keep the one-sided value amplitude on exactly as above. The original supplied-permutation mode and its sharper call count remain available.
Sparse Hamiltonian no-fast-forwarding
Statement
For , let be the -dimensional, at-most-two-sparse Hermitian path matrix of Sparse parity-path transfer and norm for , with weights . Put , choose , and round each nonzero weight to the nearest dyadic , with ties rounded upward. Let be the same path matrix with weights , and encode its entries exactly in the row-location/value XOR-oracle model of Sparse Hamiltonian row and value oracles, using value bits for the finite set . Then and the sparse normalization lies between positive absolute constants.
Suppose a uniform sparse-oracle query algorithm uses total row-location and value-oracle calls and returns a clean-input isometry satisfying where appends its work registers in zero. Then . Thus this sparse row/value model requires queries for this family. The result does not assert a lower bound for arbitrary block-encoding access.
Facts & Assumptions
Given: An integer , an input string , the path family, and the coherent query model in the Statement.
Sparse Hamiltonian row and value oracles: row-location and entry-value calls are exact coherent XOR oracles for a finite Hermitian matrix's encoded entries; each named call is charged.
Unitary and differential identities for finite matrix dynamics: finite Hermitian matrix evolutions are unitary and differentiable with derivative .
The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: operator norm is submultiplicative and satisfies the finite-dimensional adjoint norm identity.
Coherent XOR function oracle: the parity-bit oracle maps .
A total Boolean XOR oracle is unitary and involutive: every total XOR-function oracle is a unitary involution.
Bounded-error parity query lower bound: computing parity on all -bit inputs with error at most needs at least coherent bit queries.
Full-list uniform quantum circuit family and Promise problem: the query algorithm is input-independent between oracle calls and must meet its error guarantee on every input in the full Boolean-cube promise.
Circuit-family and computation cost model: named oracle calls are counted separately from ordinary gates and classical computation.
Proof
Given: As in Facts & Assumptions.
The maximum weight satisfies : gives the upper bound, and a central integer has for the lower bound. Every edge satisfies , so . The grid spacing , hence nearest rounding gives and ; the support is unchanged. It follows that each row and column of has at most two nonzero entries, each of magnitude at most , and ; in particular is bounded above and below by positive constants independent of .
Order path vertices lexicographically by . A location query's predecessor slot, when present, uses only , and its successor slot, when present, uses only ; their order is fixed by the path index. For an arbitrary candidate pair of vertices, the value query tests the single relevant edge bit and returns the known dyadic or zero. Each oracle function can thus be computed reversibly using one parity-bit query into scratch, an XOR into the requested target, and a second parity-bit query to erase scratch. The same construction controls the target XOR by any query-control bit, so each controlled or uncontrolled sparse-oracle call costs at most two calls to the parity-bit oracle. The x-independent table is generated by integer arithmetic: binary search finds the nearest grid point by comparing squared integers with , using -bit numbers and polynomially many steps in . The location and value encodings are total, including sentinels, and this x-independent arithmetic is part of the fixed inter-query circuit.
For any vector , Cauchy–Schwarz in each row gives Summing over , each row sum and each column sum of is at most . Hence , proving .
For , differentiate . By [F3], The two outside factors are unitary, so [F4] gives . Integrating from to and using step 2.1 yields
Apply the assumed simulator to the initial state with clean work registers. The triangle inequality, step 3.1, and [F2] show that its output vector is within of . The orthogonal projection onto outcomes whose second path bit is not annihilates this target vector. Its norm on the simulator output is therefore at most , so measuring the path bit computes parity with error at most . This is a valid bounded-error parity algorithm on every .
Replacing each of the algorithm's sparse-oracle calls by this reversible simulation yields a parity algorithm using at most bit queries and error at most . By [F7], , hence . From step 1.1 the sparse normalization is , and , so the lower bound is equivalently on this family.
Small-time-aware qubitized simulator
Definition
Input. An exact Hermitian block encoding of , , a rational time , rational , and a terminating certified comparison of with , charged under the access model. For the branch, supply all exact-block-mode inputs of Qubitized Hamiltonian simulator: the certified norm promise , clean embedding, declared forward, inverse and controlled signal access, projector-phase circuits, coefficient preparation, SELECT, OAA reflections, phase synthesis, data access and classical arithmetic with their costs, and terminating certified scalar comparisons and rational-interval evaluations for every arithmetic test in that procedure. These capabilities must be supplied, not inferred from the block matrix or the single comparison of with .
Instructions.
- If , return the identity circuit on the system, with every work register left at zero. The query, gate, and bit costs of this branch are zero.
- If , invoke Qubitized Hamiltonian simulator with , and return its circuit and error certificate. Propagate any INVALID, CERTIFICATE_ERROR, or IMPLEMENTATION_ERROR result without labeling it a successful simulation.
The comparison cost is recorded separately. The second branch is exactly the OP4 procedure; this wrapper does not modify OP4 or infer inverse, controlled, state-preparation, or phase-synthesis access.
Correctness and access cost of sparse block encoding
Statement
For a -row-sparse Hermitian , put and supply a certified bound . In the supplied-permutation mode, if then and the returned has zero all-zero-flag compression and normalization ; if , the designated all-zero auxiliary compression of from Sparse Hermitian block encoding is . One such block-encoding call uses one supplied inverse row-location permutation, one supplied column-location permutation, two entry-value queries (compute and uncompute), and the declared , inverse, flag-rotation, and arithmetic implementations. In the optional sorted-XOR mode with , the encoded matrix is with normalization , and each encoding, actual adjoint or coherent controlled call costs original location calls and value calls. No constant-cost conversion from XOR locations to in-place permutations is asserted.
Facts & Assumptions
Given: One declared sparse-algorithm input mode. The supplied-permutation mode has a certified nonnegative bound , exact reversible location permutations and costed uniform preparation; when , it also has costed exact value and flag arithmetic. The optional sorted-XOR branch has its separately declared positive entry bound and sorted support interface; the algorithm's branch is common to both modes.
Sparse Hermitian block encoding: prepares the uniform row-neighbor state, the uniform column-neighbor state, and the supplied-permutation mode's flag-zero value amplitude is for its certified bound .
Qubitization access model: an all-zero compression is ; its normalization and access costs are separate.
Finite block encoding: a projected block of a supplied unitary is the encoded matrix divided by its declared normalization.
Sparse Hamiltonian row and value oracles: valid row/value access describes a Hermitian matrix and counts each named oracle call separately.
Dirac Notation Conversion: contributes the matrix entry in the stated basis convention.
Qubitization access model: an exact block encoding is a unitary dilation whose all-zero compression is the normalized target matrix.
Clean sparse-state preparation from sorted XOR locations constructs the optional sorted-XOR mode with clean workspace, normalization , and the stated ordinary, inverse and controlled query counts.
Proof
Given: As in Facts & Assumptions.
First consider the supplied-permutation mode. If , the promised bound implies every entry of is zero. The returned is unitary and its compression against the clean flag is , so normalization encodes . Now suppose . Each row and, by Hermiticity, each column has at most nonzero entries, each of magnitude at most . For any vector , Cauchy–Schwarz gives . Summing over and using the row and column bounds gives , so and the stated normalization is valid in [F3]. For each system label , , where is its listed column or a distinct sentinel. For each system label , ; the register SWAP sends this to , so the supplied action of gives . Each is normalized because its terms are orthonormal and have coefficient .
For , the two columns of the stated flag matrix have unit norm and inner product ; hence it is unitary and its flag-zero output amplitude from is . The value oracle is uncomputed after this rotation, so the good-flag overlap retains no value workspace.
With the root inner product linear in its first argument, the matrix entry of is , by the adjoint identity and the ordered-basis coordinate convention in [F5]. The row and column lists contain the pair once each exactly when , by Hermiticity and the nonzero-entry enumeration. In that case the unique matching basis pair contributes : the amplitude is first in the root pairing and equals , while the row-state amplitude is . If , there is no valid matching pair, and the sentinel branch has flag-zero amplitude . Thus every clean matrix entry is the stated one, proving the compressed block identity.
The displayed circuit for applies the inverse row-location permutation once through , the column-location permutation once through , and the entry-value oracle twice to compute and uncompute the value. Its state preparations, inverses, arithmetic, flag rotations, controlled operations, and gate synthesis are separate declared costs, so no additional sparse-access operation has been counted as free.
In the optional sorted-XOR mode, apply [F7] to the supplied sorted QA20 interface and positive entry bound . Its clean isometries insert only on the column side, give the padded block , and clear the extra location and value work exactly on the prepared input subspaces. Its actual inverses and coherent controls have the same logarithmic location-query order. The supplied-permutation proof and counts in steps 1.1–3.1 are unchanged.
Jacobi–Anger Chebyshev truncation
Statement
With defined in Hamiltonian-simulation polynomial target, for every real and integer , whenever . In the OP4 simulator, and the factorial-tail stopping test therefore certifies truncation error at most ; rounding each coefficient to error at most keeps the combined polynomial error below . For the nontrivial simulation regime , an integer passing OP4's test exists with For , the constant polynomial approximates the target within ; the wrapper algorithm uses the exact identity circuit in that branch.
Facts & Assumptions
Given: Real , the coefficient series and Chebyshev recurrence in Hamiltonian-simulation polynomial target, and rational .
Hamiltonian-simulation polynomial target: , , and .
The complex exponential series converges absolutely for every complex argument: the exponential series converges absolutely at every complex argument.
, , and : for real .
The complex exponential is entire and its complex derivative is itself: the complex exponential is differentiable with derivative equal to itself.
, and the complex exponential extends the real exponential: the complex exponential satisfies for all complex .
Qubitized Hamiltonian simulator: OP4 sets , starts the degree loop at , accepts a factorial-tail bound at most , and rounds coefficients within each.
Small-time-aware qubitized simulator: if , the wrapper returns the exact identity circuit.
Proof
Given: As in Facts & Assumptions.
The Chebyshev recurrence and show by induction that for all . Every is for some real , so .
If , the ratio of consecutive absolute terms in the defining series of is , which tends to zero, so the series converges absolutely; for the series is immediate. For the integral , splitting and integrating the derivative of gives . Iterating yields . Expanding uniformly on , substituting , and using this moment ratio gives The weight is nonnegative and its normalized integral is one; [F4] therefore gives .
Put , , and . If , then . If , put , so . The function is increasing for , since its derivative has numerator ; the first term exceeds and the second is below . Also for (the difference has positive derivative and is positive at ), and , so . Therefore . In all cases . Set ; then , , and , so this is an eligible candidate in the OP4 loop starting rule [F7]. The real parameter is defined in [F1].
For , [F6] writes as the product of the absolutely convergent exponential series for and . Absolute convergence permits grouping terms by the exponent difference ; for each the coefficient of is , and the coefficient of is the same. Pairing these terms and using [F1] gives By [F1], [F3], [F4], and [F6], the defining product is absolutely and uniformly convergent in on bounded intervals, so the regrouping is valid.
Subtracting the degree- partial sum in step 2.1, applying the triangle inequality, and using from step 1.1 gives error at most . With , the ratio of consecutive factorial majorants for is at most . The geometric-series bound from step 1.2 now yields the stated factorial tail.
By [F7], each of the OP4 coefficient errors is at most . Since a nonconstant Chebyshev term has coefficient multiplier , their total uniform contribution is at most ; adding the tail from step 3.1 gives a combined error . The coefficients and multiplier are those of the expansion in [F1].
Let . If , then . Since for and , we have . Thus , because gives ; hence . If , gives , hence , since implies . For , these bounds give , because . For , they give , because . Also , so , and the factorial tail expression is bounded by the estimate of step 3.1. The real is defined in [F1]. so . The factorial-tail test at is consequently at most The last inequality holds because : for , the function is positive (its derivative is as increases to , and its value at is zero), and . Hence ; the numerical comparison follows from , which gives . Since using , the passing degree is . The factorial tail is the estimate of step 3.1 and the OP4 threshold is [F7].
If , the derivative fact [F5] and from [F4] give for every . Thus the wrapper [F8] has the stated small-time error, including .
Qubitized simulation correctness
Statement
For the exact access model of Qubitization access model, or its bounded-block-error variant satisfying the post-degree budget in Qubitized Hamiltonian simulator, rational , and , a successful execution of Qubitized Hamiltonian simulator returns a unitary dilation satisfying on the clean-input isometry , where appends every work register in zero. Output work registers are approximate; no full-space error on arbitrary dirty work inputs or exact cleanup is asserted. For , the declared identity branch is exact. For every reference system, tracing out work gives the corresponding system-output trace-distance bound at most .
Facts & Assumptions
Given: A finite Hermitian with , either the exact or budgeted bounded-error normalized block encoding, and a successful certified OP4 execution.
Qubitized Hamiltonian simulator: OP4 gives combined scalar error at most , sets each real phase-synthesis tolerance to , requires the bounded-block mode to satisfy , and allocates the remaining post-amplification primitive-implementation error at most .
Jacobi–Anger Chebyshev truncation: the factorial-tail polynomial and its certified coefficient rounding give the stated scalar error on .
Correctness of QSP phase synthesis: on a successful numerical phase-synthesis call, the exact phase-list polynomial error plus the sum of its implemented phase-slot errors is at most .
QSVT action on singular subspaces: the alternating QSVT sequence has the stated polynomial compressed block on each singular space, including endpoints and null spaces.
Clean-input isometry error bounds output trace distance: clean-input isometry error bounds the reference-system trace distance after work is traced out.
Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: a finite Hermitian has an orthonormal eigenbasis.
Qubitization access model and Qubitized Hamiltonian simulator: the exact and bounded-error modes both satisfy .
Robust one-step OAA including ancilla leakage: if has clean block and for a unitary , the phase-corrected OAA circuit has clean-input isometry error at most , including workspace leakage.
The inner-product norm is definite, homogeneous, and satisfies the triangle inequality: the vector triangle inequality induces the operator-norm triangle inequality used in spectral and circuit-product error sums.
Operator perturbation bound for QSVT: for any fixed exact degree- QSVT phase sequence, changing the encoded contraction by changes its singular-value-transform block by at most .
Proof
Given: A successful output of the simulator in Facts & Assumptions; when , all OP4 certificates succeed. Put and .
If , [F1] returns the identity circuit with no signal query, and ; hence its clean-input isometry error is zero. If , continue with the certified OP4 output.
On an eigenvector of with eigenvalue , has eigenvalue , by [F8] and [F9]. Put and . The OP4 certificate gives by [F1]–[F2], and . Since , In the orthonormal eigenbasis, this error is diagonal; for any vector with coordinates , its squared output norm is the sum of times the squared scalar errors. Thus the uniform scalar bound gives . For each real QSVT extraction, [F3] bounds the exact phase-list polynomial mismatch plus the implemented phase-slot errors by uniformly on . For the bounded mode, apply [F12] to the exact and phase sequences on the supplied block and on the target block; each branch changes by at most , so their average has the same block-perturbation bound. The coefficient-qubit average therefore gives a complete block with For even , ; for odd , the singular vector of a negative eigenvalue has left vector , so , and . Thus these transforms are the ordinary polynomial blocks on . The certified error is consequently
Since , . Apply [F10] to the unitary whose clean block is , with target unitary . The phase-corrected OAA circuit therefore has clean-input isometry error at most , including its retained-work leakage.
The per-occurrence certificates in [F1] cover every remaining unitary primitive in the fully unrolled post-amplification circuit, counting repeated, inverse and controlled calls separately. If there are such occurrences, each contributes at most in full operator norm. The telescoping product identity, [F11], and unitary norm one bound the difference between the actual and phase-certified OAA unitaries by ; for the difference is zero. Thus the actual clean-input isometry error is at most .
If , [F1] returns the exact identity and the conclusion holds with zero error. If , step 3.1 gives the clean-input isometry bound. Apply [F7] in either case; it includes arbitrary reference systems and discarding the work registers, so the trace distance is at most .
Correctness of the small-time-aware simulator
Statement
For every promised input of Small-time-aware qubitized simulator, a successful output satisfies the clean-input isometry error bound . It also satisfies the corresponding reference-system trace-distance bound after tracing the work registers.
Facts & Assumptions
Given: The exact normalized block encoding, a rational , and .
Small-time-aware qubitized simulator: the small branch returns identity when ; otherwise it invokes OP4.
Qubitized simulation correctness: on a successful OP4 output, the clean-input isometry error is at most , and the traced output has the same trace-distance bound.
Finite Hermitian matrix dynamics: is defined by the finite spectral functional calculus.
Unitary and differential identities for finite matrix dynamics: and is unitary.
Clean-input isometry error bounds output trace distance: clean-input isometry error bounds the reference-system trace distance after tracing work by .
Qubitization access model: the block-encoding normalization satisfies .
Proof
Given: As in Facts & Assumptions.
If , the algorithm invokes OP4, and [F2] gives the clean-input isometry bound.
Suppose . For a unit vector , the differential identity [F4] and the fundamental theorem of calculus give . Each integrand has norm at most , because preserves norm. The triangle inequality for Riemann sums and continuity yield , using [F7]. Taking the maximum over unit proves the clean-input operator bound for the identity circuit. The work registers are exactly zero, so the same isometry bound holds.
In either branch, the implementation on clean inputs and the target are isometries whose operator-norm difference is at most , by steps 1.1 and 1.2. Apply [F6] with to obtain the reference-system trace-distance conclusion after tracing work.
Qubitization query and gate resources
Statement
Let and , with . In the nontrivial branch , put . The small-time-aware simulator of Small-time-aware qubitized simulator uses zero signal queries when . When , the OP4 degree satisfies . The complete OAA circuit uses at most ordinary signal calls, where are the even and odd QSP degrees. It uses projector-phase slots and therefore controlled-reflection calls, by QSVT signal and reflection query counts. Coherent selection of each real sequence against identity uses exactly one controlled signal call when that sequence degree is odd and none when it is even; because the selection occurs in each of the three OAA blocks , the total is controlled signal calls. The branch itself controls phases, not signal calls. Any external control of the complete simulator controls and charges the whole block.
Gate, depth, width, ancilla, classical bit, precision, PREPARE/SELECT, phase-list-generation, and sparse-location/value costs remain separate coordinates. For a -row-sparse input with supplied-permutation bound , the branch encodes with normalization and uses no location or value calls. If , Correctness and access cost of sparse block encoding gives normalization ; choosing the tight bound gives . Multiply the signal-query count by the actual supplied sparse-encoding cost, including any location-permutation completion. The optional sorted-XOR mode has the same normalization for a supplied positive entry bound and multiplies the signal count by location calls and value calls per signal, including inverses and coherent controls. No cost-free conversion from XOR row-location access is claimed. If is bounded above and below by positive constants as , the query bound specializes to . This is an upper bound; no matching lower bound for arbitrary block-encoding access or for the additive precision term is asserted.
Facts & Assumptions
Given: An exact block encoding, the certified OP4 procedure, rational , and all separately charged access operations.
Small-time-aware qubitized simulator: use the identity branch for , otherwise execute OP4.
Jacobi–Anger Chebyshev truncation: for , the OP4 factorial-tail loop terminates at degree .
QSVT signal and reflection query counts: a degree- signal sequence has signal calls, projector phases, and at most one controlled signal call when controlled against identity if is odd.
QSP signal-query and phase-rotation count: a scalar QSP sequence of degree has signal factors and phase factors; signal-query and phase-gate costs are separate.
QSVT robustness, approximation error, and resources: every sequence access, phase, controlled operation, synthesis, and precision budget is a separate declared cost.
Correctness and access cost of sparse block encoding: the supplied-permutation sparse signal block uses normalization and no sparse calls when ; for its normalization is for certified , including the tight choice . The sorted-XOR branch with positive has normalization and costs location and value calls per signal, including inverses and controls.
Qubitized simulation correctness: on successful execution, the declared circuit satisfies the operator-isometry error guarantee.
Proof
Given: As in Facts & Assumptions.
If , [F1] returns the exact identity and makes no signal call. If , the degree test of [F2] has a finite passing candidate and its least accepted degree obeys the stated bound.
In one OAA application, contains one even and one odd real QSVT extraction. By [F3], they use signal calls and projector-phase slots. OAA uses , so the total is exactly signal calls and phase slots, bounded above by and , respectively.
By [F3], controlling each selected real extraction against identity needs one controlled signal call exactly when its degree is odd. OAA contains the three signal blocks , so the even and odd extractions together use exactly such calls. By [F4], the control selecting or acts only on phase rotations and adds no signal query. Any further external control invokes the separately supplied controlled- or controlled- operations by [F5].
The lists in OP4 are generated once, while the OAA circuit reuses the declared phase lists, coefficient preparation, and SELECT circuits in its three block calls and their inverses. Every implementation cost named in the Statement is retained from [F5]; the sparse branch multiplies these signal counts by the supplied block-encoding operations and normalization in [F6]. In the optional sorted-XOR branch this explicitly multiplies the signal count by location and value calls; the supplied-permutation branch retains its original sharper count. Thus no sparse location/value, phase-synthesis, or classical bit cost is absorbed into an oracle count.
When and , and , since . Substituting this comparison into gives , the stated standard-regime form. Correctness remains the separate theorem [F7].
5 · Examples, counterexamples and false statements
None yet.
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