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Qubitization and Near-Optimal Hamiltonian Simulation

1 · Prerequisites

2 · Summary

An exact Hermitian block encoding supplies the normalized compression H/α, while the access record separately charges U, U∗, 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 ∣λ∣=α endpoints. The qubiterate is a unitary rotation with eigenphases ±arccos⁡(λ/α); alternating signal calls and projector phases implement the corresponding scalar QSP sequence.

For fτ(x)=e−iτx, 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 α∣t∣≤ϵ, which closes the uniform degree claim near t=0.

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 h≥hmax⁡ and uses normalization dHh; choosing h=hmax⁡ 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 Ω(t) 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

LemmaStatement: AI-adaptedProof: AI-generatedjudge pass (gpt-6-luna)Open item page →

Clean-input isometry error bounds output trace distance

Statement

Let V,W:H→K⊗E be linear isometries between finite-dimensional complex inner-product spaces, meaning V∗V=W∗W=IH, and assume δ≥0 and ∥V−W∥op≤δ. For every nonzero finite-dimensional reference space R and density matrix ρ on H⊗R (a positive semidefinite matrix of trace one), define VR=V⊗IR and WR=W⊗IR on the ordered product bases. Define the partial trace by (Tr⁡EX)(k,r),(k′,r′)=∑eX(k,e,r),(k′,e,r′), and define ∥X∥1 as the sum of singular values and D(σ,ω)=12∥σ−ω∥1. The output density matrices ρV=(Tr⁡E⊗IR)(VRρVR∗),ρW=(Tr⁡E⊗IR)(WRρWR∗) satisfy D(ρV,ρW)≤δ. 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, a∗ denotes conjugate transpose and aa∗ the rank-one matrix with entries aiaj‾.

Facts & Assumptions

Given: The finite spaces, maps V,W, and density matrix ρ in the Statement.

[F1]

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.

Proof

Given: As in Facts & Assumptions.

1.1F1givenalgebra

Put A=V−W, and let (rj) be an orthonormal basis of R. By [F1], diagonalizing A∗A in a right singular basis shows that (A⊗IR)∗(A⊗IR)=A∗A⊗IR has the same largest eigenvalue as A∗A: its eigenvectors are the products of right singular vectors of A with the rj, and its eigenvalues are the corresponding squared singular values. Therefore ∥VR−WR∥op=∥A⊗IR∥op=∥A∥op≤δ. For every unit vector ψ∈H⊗R, the vectors a=VRψ and b=WRψ have norm one and satisfy ∥a−b∥≤δ.

1.2givenalgebra

For Z=VRρVR∗, positivity follows from y∗Zy=(VR∗y)∗ρ(VR∗y)≥0 for every y; the same holds with WR. For a vector z∈K⊗R, the entry-sum formula gives z∗(Tr⁡E⊗IR)Zz=∑e(IK⊗∣e⟩⊗IR z)∗Z(IK⊗∣e⟩⊗IR z)≥0, and likewise for WR; hence both reduced outputs are positive. That formula also gives tr⁡((Tr⁡E⊗IR)Z)=tr⁡Z. Since VR∗VR=WR∗WR=I, cyclicity of finite matrix trace gives tr⁡Z=tr⁡ρ=1 for both outputs. Thus ρV,ρW are density matrices.

2.1F1F2step 1.1algebra

In an orthonormal basis of span⁡{a,b}, write b=za+se2, where z=a∗b, s=1−∣z∣2, and e2⊥a is unit if s>0. The matrix of aa∗−bb∗ on this span is Hermitian, has trace zero and determinant −s2, so its eigenvalues are s,−s; outside the span it is zero. Thus 12∥aa∗−bb∗∥1=s. Also s2=1−∣z∣2≤2−2Re⁡z=∥a−b∥2, because the difference of the right side and left side is ∣1−z∣2. This proves D(aa∗,bb∗)≤δ for every pure input, including s=0.

2.2F1givenstep 1.1algebra

Let X be Hermitian on K⊗E⊗R, and set Y=(Tr⁡E⊗IR)X. For a matrix B on K⊗R, define B~ on K⊗E⊗R by applying B to the K,R coordinates and leaving the E coordinate fixed. The entry-sum definition of partial trace gives tr⁡(B∗Y)=tr⁡(B~∗X). Reordering the product basis from K,E,R to K,R,E conjugates B~ to B⊗IE, so ∥B~∥op=∥B∥op by the same singular-basis argument as in step 1.1. For any square matrix X=UΣV∗, [F1] gives its singular value decomposition, and ∣tr⁡(C∗X)∣=∣tr⁡(V∗C∗UΣ)∣≤∥C∥op∑jΣjj=∥C∥op∥X∥1, since every diagonal entry of a contraction has modulus at most one. Taking the supremum over ∥B∥op≤1, and using the equality case B=UYVY∗ in a singular value decomposition of Y, proves ∥Y∥1≤∥X∥1. This dual formula also gives the triangle inequality and homogeneity for ∥⋅∥1 by applying it to a sum and scalar multiple.

3.1F1F2step 2.1step 1.2step 2.2algebra∎

For a pure input ρ=ψψ∗, step 2.1 bounds the trace distance before discarding E, 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 δ. For general positive trace-one ρ, [F2] gives a spectral decomposition ρ=∑jpjψjψj∗ with pj≥0 and ∑jpj=1. The maps and partial trace are linear; the triangle inequality and homogeneity from step 2.2 therefore bound the reduced output distance by ∑jpjδ=δ.

LemmaStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Sparse parity-path transfer and norm

Statement

Let Bn be the complex inner-product space with orthonormal basis {∣y⟩:y∈{0,1}n}. For 1≤r≤n, define Xr∣y1⋯yn⟩=∣y1⋯(yr⊕1)⋯yn⟩, and put J=12∑r=1nXr. For 0≤i≤n, let ∣Din⟩ be the normalized uniform sum of basis vectors of Hamming weight i; define Sn=span⁡{∣Din⟩:0≤i≤n}. On a separate path space with orthonormal basis {∣i⟩:0≤i≤n}, define H0 to have off-diagonal entries wi=i(n+1−i)/n between ∣i−1⟩ and ∣i⟩ for every 1≤i≤n, and all other matrix entries zero.

Let n≥1, x∈{0,1}n, and p0=0, pi=x1⊕⋯⊕xi. On the basis {∣i,b⟩:0≤i≤n, b∈{0,1}}, put Hx=∑i=1nwi∑b=01(∣i−1,b⟩⟨i,b⊕xi∣+∣i,b⊕xi⟩⟨i−1,b∣). Write parity⁡(x)=pn=x1⊕⋯⊕xn. The basis permutation Vx∣i,b⟩=∣i,b⊕pi⟩ conjugates Hx to two copies of H0. The counting identity proved below gives H0=(2/n)J∣Sn, under the isometry ∣i⟩↦∣Din⟩. Consequently ∥Hx∥op≤1 and e−iHxπn/2∣0,0⟩=(−i)n∣n,parity⁡(x)⟩.

Facts & Assumptions

Given: A positive integer n, a bit string x, the finite matrix dynamics of Finite Hermitian matrix dynamics, and the root convention of Dirac Notation Conversion.

[F1]

Finite Hermitian matrix dynamics and exp⁡(z+w)=exp⁡z exp⁡w, and the complex exponential extends the real exponential: finite Hermitian evolution is e−itA by spectral functional calculus, and the scalar exponential satisfies exp⁡(z+w)=exp⁡(z)exp⁡(w).

[F3]

exp⁡(x+iy)=ex(cos⁡y+isin⁡y), ∣exp⁡(x+iy)∣=ex, and eiπ+1=0: exp⁡(iθ)=cos⁡θ+isin⁡θ, with the stated quarter-turn values.

Proof

Given: As in Facts & Assumptions.

1.1givenconstructalgebra

The map Vx permutes the finite orthonormal basis and satisfies Vx2=I, so it is unitary. In a term indexed by i,b, the endpoints become ∣i−1,b⊕pi−1⟩ and ∣i,b⊕xi⊕pi⟩. Since pi=pi−1⊕xi, the second bit is b⊕pi−1; the edge is therefore an ordinary adjacent path edge with the spectator bit unchanged. Thus VxHxVx is two identical path matrices H0, one for each bit b, and Vx fixes ∣0,0⟩ while sending ∣n,0⟩ to ∣n,pn⟩.

1.2givenalgebra

Let ∣Din⟩ be the normalized uniform superposition of n-bit strings of Hamming weight i, and set ∣D−1n⟩=∣Dn+1n⟩=0. Counting the i downward flips and n−i upward flips gives J∣Din⟩=12i(n+1−i)∣Di−1n⟩+12(i+1)(n−i)∣Di+1n⟩ for every 0≤i≤n. Thus (2/n)J has the stated path weights on the symmetric subspace.

2.1givenstep 1.1step 1.2algebra

Each Xr permutes an orthonormal basis, so for v=∑ycy∣y⟩ one has ∥Xrv∥2=∑y∣cy∣2=∥v∥2, and hence ∥Xr∥op=1. The vector triangle inequality gives ∥Jv∥≤12∑r∥Xrv∥=(n/2)∥v∥, which yields ∥J∥op≤n/2, so ∥H0∥op≤1; unitary conjugation in step 1.1 gives ∥Hx∥op≤1.

3.1F1F2F3step 1.1step 1.2algebra∎

For each sign string σ∈{−1,1}n, put uσ=2−n/2∑y∈{0,1}n(∏rσryr)∣y⟩. Factorizing the finite inner-product sum gives ⟨uσ,uτ⟩=2−n∏r(1+σrτr), so these 2n vectors form an orthonormal basis. Flipping bit r gives Xruσ=σruσ. By [F1]–[F2], e−i(π/2)∑rXr acts on uσ by exp⁡(−iπ∑rσr/2). The scalar addition law in [F1] factors this as ∏rexp⁡(−iπσr/2), and Euler's formula [F3] gives each factor as −iσr. Hence e−i(π/2)∑rXr=(−i)nX1⋯Xn. This maps ∣D0n⟩=∣0n⟩ to (−i)n∣Dnn⟩=(−i)n∣1n⟩. The path basis identifies ∣D0n⟩,∣Dnn⟩ with ∣0⟩,∣n⟩; step 1.1 then sends the endpoint to ∣n,pn⟩. Since H0=(2/n)J, evolution for t=πn/2 is e−itH0=e−iπJ, which is the transfer established above.

DefinitionDefinition: AI-adaptedProof: Not applicableOpen item page →

Qubitization access model

Definition

Let H be a finite-dimensional complex inner-product space, let K=K∗ on H, and let α>0 satisfy ∥K∥op≤α. Put A=K/α. Let U be a unitary on an ancilla-system space HA⊗H, let J:H→HA⊗H be the isometry Jv=∣0a⟩⊗v, and let Π=JJ∗. An exact Hermitian block encoding is the data (U,J,α) with J∗UJ=A. The matrix K is Hermitian; U itself need not be Hermitian.

The signal-register reflection is RΠ=2Π−I. The access record lists, separately, each supplied call to U, U∗, controlled-U, controlled-U∗, and RΠ, 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 U~=∣0⟩⟨0∣⊗U+∣1⟩⟨1∣⊗U∗, J′v=∣+⟩⊗Jv, S=X⊗I, F=SU~, and Wq=(2J′J′∗−I)F. Implementing U~ requires the separately declared controlled-U and controlled-U∗ calls; S, the preparation of ∣+⟩, and the reflection about ran⁡(J′) 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 U,U∗ calls interleaved with the projector phases eiϕ(2Π−I) 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.

DefinitionDefinition: Literature-sourcedProof: Not applicableOpen item page →

Hamiltonian-simulation polynomial target

Definition

For a finite Hermitian H with exact block-encoding normalization α>0, ∥H∥op≤α, and real time t, put τ=αt. On x∈[−1,1] the scalar target is fτ(x)=exp⁡(−iτx); if Hψλ=λψλ, then fτ(λ/α)=exp⁡(−itλ), the eigenvalue of the finite matrix evolution Finite Hermitian matrix dynamics.

Define the Chebyshev polynomials by T0(x)=1, T1(x)=x, and Tk+1(x)=2xTk(x)−Tk−1(x) for k≥1. For k≥0, define the Bessel coefficients used here by the series Jk(τ)=∑j=0∞(−1)j(τ/2)2j+kj!(j+k)!. For τ≠0, the ratio of consecutive absolute summands is τ2/[4(j+1)(j+k+1)]→0, so the series converges absolutely. If τ=0, at most its j=0,k=0 summand is nonzero, so it also converges absolutely. Thus the series defines Jk(τ) 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.

LemmaStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)Open item page →

Hermitian block-encoding signal subspaces

Statement

Let (U,J,α) be an exact Hermitian block encoding from Qubitization access model, and choose an orthonormal eigenbasis Kψj=λjψj. Put xj=λj/α and sj=1−xj2. For ∣xj∣<1, define ηj=(I−Π)UJψj/sj,η~j=(I−Π)U∗Jψj/sj. The ηj are orthonormal in ker⁡Π, as are the η~j. In the paired input basis (Jψj,η~j) and output basis (Jψj,ηj), U has matrix (xjsjsj−xj). The unitary U∗ maps in the reverse direction by the same matrix. If ∣xj∣=1, the complement is absent and UJψj=xjJψj=U∗Jψj; no division by zero is used. The value xj=0 is an ordinary two-dimensional signal plane.

Facts & Assumptions

Given: A finite-dimensional self-adjoint K, an exact block encoding, and the associated signal projector Π.

[F1]

Qubitization access model: J∗UJ=K/α and Π=JJ∗; taking adjoints and using K=K∗ gives J∗U∗J=K/α.

[F2]

PUE singular-space invariant decomposition: the positive singular pairs of a projected unitary give orthogonal paired two-dimensional subspaces; their endpoint complements are absent.

[F3]

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.

[F4]

The adjoint T∗:W→V is characterised by ⟨Tv,w⟩W=⟨v,T∗w⟩V: ⟨Tx,y⟩=⟨x,T∗y⟩, so a unitary and its adjoint preserve inner products.

[F5]

Finite block encoding: the normalized compression has operator norm at most one, hence ∣λj/α∣≤1.

Proof

Given: As in Facts & Assumptions.

1.1givenF1F4F5algebra

Fix an eigenvector ψj with unit norm and write vj=Jψj. From [F1], ΠUvj=xjvj and ΠU∗vj=xjvj, because K=K∗. Since U,U∗ preserve norm, orthogonal decomposition by Π gives ∥(I−Π)Uvj∥2=∥(I−Π)U∗vj∥2=1−xj2=sj2. For ∣xj∣<1, the displayed ηj,η~j are therefore unit vectors in ker⁡Π.

2.1F1F3F4step 1.1algebra

If i≠j, orthogonality of eigenvectors and [F4] give ⟨Uvi,Uvj⟩=⟨vi,vj⟩=0, while ⟨ΠUvi,ΠUvj⟩=xixj⟨vi,vj⟩=0. Subtracting the projected components shows ⟨ηi,ηj⟩=0; the same calculation for U∗ proves orthogonality of the η~j.

2.2F1F4step 1.1algebra

Decomposing Uvj into its Π and I−Π parts gives Uvj=xjvj+sjηj. Also sjUη~j=U(I−Π)U∗vj=vj−UΠU∗vj=vj−xjUvj=sj2vj−xjsjηj. Division by sj>0 yields Uη~j=sjvj−xjηj, which is the second column of the displayed matrix. Taking adjoints gives the reverse-basis action of U∗.

3.1F1F2F3step 1.1algebra∎

If ∣xj∣=1, the orthogonal complement of ΠUvj=xjvj has squared norm 1−∣xj∣2=0, so Uvj=xjvj; the same argument for U∗ gives U∗vj=xjvj. Thus the endpoint plane collapses. At xj=0, step 1.1 has sj=1, 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 ker⁡Π and cannot contribute to the clean compression.

TheoremStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Qubitization lifts scalar QSP

Statement

Let (U,J,α) be an exact Hermitian block encoding of K, with K=K∗ and ∥K∥op≤α. The qubiterate Wq of Qubitization access model has, on each eigenvalue x=λ/α, a two-dimensional invariant signal space on which it is a rotation with eigenvalues e±iarccos⁡x; at x=±1 the space collapses to the corresponding eigenline.

For a phase list Φ=(ϕ0,…,ϕd), alternating U,U∗ calls and projector phases eiϕ(2Π−I) acts on each paired signal space as the scalar Hermitian-reflection QSP sequence UΦ(x) of QSP signal matrix and ordered phase sequence. If PΦ is its upper-left polynomial, the clean compressed block is PΦ(K/α) 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 id, 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.

[F1]

Qubitization access model: J∗UJ=K/α, and Wq=RJ′SU~ uses U~=∣0⟩⟨0∣⊗U+∣1⟩⟨1∣⊗U∗, S=X⊗I, and RJ′=2J′J′∗−I.

[F2]

Hermitian block-encoding signal subspaces: on eigenvalue x, the paired U,U∗ signal spaces have matrix (x1−x21−x2−x), with endpoint and residual cases specified.

[F3]

QSP signal matrix and ordered phase sequence: UΦ(x)=Z(ϕ0)∏j=1d(WR(x)Z(ϕj)), where WR(x)=(x1−x21−x2−x).

[F4]

QSVT action on singular subspaces: the alternating U,U∗ 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.

[F5]

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.

[F6]

Projector Reflections Are Hermitian Unitary Involutions: RΠ=2Π−I is +I on ran⁡Π and −I on ker⁡Π.

[F7]

Hermitian-reflection and SU(2) QSP convention conversion: the SU(2) phase list conversion contributes the explicit scalar id; under a control this is a branch-relative phase.

Proof

Given: As in Facts & Assumptions.

1.1F1F6algebra

In control-first order, F:=SU~=(0U∗U0), so F∗=F and F2=I. Also J′∗FJ′=12(J∗U∗J+J∗UJ)=K/α because J∗UJ=K/α is self-adjoint. For a unit eigenvector Kψλ=λψλ, put gλ=J′ψλ, x=λ/α, and s=1−x2. The compression identity gives Fgλ=xgλ+shλ for a unit hλ⊥ran⁡J′ when ∣x∣<1. Applying F2=I then gives Fhλ=sgλ−xhλ. The reflection RJ′ has matrix diag⁡(1,−1) in this basis, so Wq=RJ′F has matrix (xs−sx). Its characteristic polynomial is z2−2xz+1, whose roots are x±i1−x2=e±iarccos⁡x. If ∣x∣=1, the orthogonal component has norm zero and the clean eigenline has eigenvalue x.

1.2F2F3F5F6algebra

On a paired signal plane from [F2], 2Π−I is diagonal with entries +1,−1, so eiϕ(2Π−I) is Z(ϕ). The signal calls switch between the input and output bases of [F2]; therefore their chronological product, written in matrix order, is Z(ϕ0)WR(x)Z(ϕ1)⋯WR(x)Z(ϕd)=UΦ(x) by [F3]. The determinant and polynomial-entry conventions are those proved by the scalar QSP supplier in [F5].

2.1F2F4F5step 1.2algebra

Every clean system vector is a finite linear combination of eigenvectors of K. Step 1.2 acts on each eigenvector by the same scalar polynomial PΦ(λ/α), so linearity gives the compressed operator PΦ(K/α). 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].

3.1F3F4F5F7step 1.2algebra∎

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 id; 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.

TheoremStatement: Literature-sourcedProof: AI-adaptedOpen item page →

Bounded-error parity query lower bound

Statement

Any quantum query algorithm that, on every x∈{0,1}n, outputs parity⁡(x) with error at most 1/3 using the coherent XOR oracle for x makes at least n/2 oracle queries.

Facts & Assumptions

Given: The finite parity promise task and the coherent XOR oracle of Coherent XOR function oracle.

[F1]

Promise problem: correctness is required on every input in the declared promise.

[F2]

Query-model convention: the finite register starts in a fixed input-independent state, each inter-query unitary is independent of x, and the output is a designated computational-basis bit measured after the last unitary. A q-query algorithm has at most q 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.

[F3]

Coherent XOR function oracle: Ox∣i,y⟩=∣i,y⊕xi⟩ on every basis string, with a query charged only for the named oracle.

[F4]

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 q-query algorithm with the stated worst-case error.

1.1givenF2F3F4algebrainduction

Put zi=(−1)xi∈{−1,1}. On a basis query address i, the matrix entries of the XOR target flip are either (1+zi)/2 or (1−zi)/2; the input address and all other bits are unchanged. Thus one oracle call raises the degree of every amplitude polynomial in z1,…,zn by at most one. The inter-query unitaries are input-independent by [F2] and take fixed linear combinations of amplitudes. Induction over the q queries shows each final amplitude is a multilinear polynomial of degree at most q, replacing any zi2 by 1 without increasing degree.

2.1F1F2step 1.1algebra

The signed output expectation p(z)=Pr⁡[y=0]−Pr⁡[y=1] is a sum of squared moduli of final amplitudes with fixed signs, so it is a real multilinear polynomial of degree at most 2q. If χ(z)=∏i=1nzi=(−1)parity⁡(x), the error bound gives χ(z)p(z)≥1/3 pointwise. Therefore the uniform average of χp is positive.

3.1step 2.1algebra∎

If 2q<n, every monomial of p is zS=∏i∈Szi for a proper subset S⊊[n]. Under independent uniform signs, E[χzS]=E[∏i∉Szi]=0, since at least one independent sign appears to the first power and has mean zero. Linearity gives E[χp]=0, contradicting step 2.1. Hence 2q≥n.

AlgorithmAlgorithm: Literature-sourcedProof: Not applicableOpen item page →

Qubitized Hamiltonian simulator

Definition

Inputs. A finite Hermitian H, α>0 with the certified norm promise ∥H∥op≤α, rational time t, rational 0<ϵ≤1, and either (i) the exact (α,a,0) block encoding U with clean injection J and J∗UJ=H/α, or (ii) a separately selected bounded-block-error mode with the same declared ancilla embedding J and a unitary U whose clean block satisfies ∥J∗UJ−H/α∥op≤ϵBE for a supplied certified rational upper bound ϵBE≥0. 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 U,U∗, controlled-U, controlled-U∗, 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 Vimpl. Its success claim is on the clean-input isometry Jclean∣ψ⟩=∣0work⟩∣ψ⟩; 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 τ=αt and ρ=ϵ2/10000. For t=0, return the identity circuit and zero query count. Otherwise:

  1. Starting at d=max⁡(1,⌈∣τ∣⌉), increase d until u=∣τ∣/(2(d+2))<1 and 2(∣τ∣/2)d+1(d+1)!(1−u)≤ρ/8. 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.

  2. For each k=0,…,d, compute Jk(τ) from the series in Hamiltonian-simulation polynomial target. Stop its alternating-series evaluation only after its ratio τ2/[4(j+1)(j+k+1)] is below 1 and its geometric remainder is at most ρ/(32(d+1)). Store a rational coefficient and its error interval. Form T0=1,T1=x,Tk+1=2xTk−Tk−1. Set E=J0+2∑1≤2r≤d(−1)rJ2rT2r,O=2∑1≤2r+1≤d(−1)rJ2r+1T2r+1. Thus Q:=E−iO is the rounded Jacobi–Anger truncation and its certified scalar error is ∥Q−fτ∥[−1,1]≤ρ; the sign (−i)k is literal.

  3. Require a certified combined polynomial error at most ρ. Then ∣E(x)∣,∣O(x)∣≤1+ρ on [−1,1]. Put B=1+2ρ, pE=E/B, and pO=O/B, and set δqsp=ρ/64. Define Γ=(3ϵ128)2−3ρ−2δqsp>0, since 3ρ+2δqsp=97ϵ2/320000<9ϵ2/16384. In exact-block mode put ϵBE=0. In bounded-block-error mode require the supplied rational upper bound to satisfy (8d)2ϵBE≤Γ2; 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 in efficient-numerical mode with tolerance δqsp, their actual degree, and its parity. If either target is identically zero, declare degree 0 and even parity. Retain each complementary-polynomial identity, phase list, per-phase precision and optional compiler certificates. A successful call certifies ηpoly+∑ℓζℓ≤δqsp, where ηpoly is the exact phase-list polynomial error and ζℓ 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 0 in exact mode or at most ϵBE in bounded mode. For the bounded mode, compare each exact Φ and −Φ 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 δqsp, while the averaged block perturbation is at most 4dϵBE. Thus each extracted clean block differs from its target by at most δqsp+4dϵBE. Since pE is even and pO odd, these singular-value transforms on Hermitian H/α equal the ordinary polynomials pE(H/α) and pO(H/α): the even values agree at λ=±σ, while for the odd transform a negative eigenvalue has left singular vector u=−v and therefore supplies the sign; also pO(0)=0. Propagate every non-success status from either synthesis or extraction unchanged; do not report a simulator on a failed certificate.

  4. Pad both extraction workspaces to the same ordered register space. Prepare a clean coefficient qubit in ∣+⟩. SELECT applies the pE(H/α) block in branch 0 and −i times the pO(H/α) block in branch 1. Unprepare the coefficient qubit. The ideal target block is M/2, where M=pE(H/α)−ipO(H/α)=Q(H/α)/B. Let Mph be twice the clean block of this phase-certified W when its remaining operations are exact. By the step-3 synthesis certificates and branchwise robustness from Operator perturbation bound for QSVT, ∥Mph−M∥op≤2δqsp+8dϵBE, In exact-block mode this bound uses ϵBE=0. Since B=1+2ρ, the ideal target polynomial error satisfies ∥M−e−itH∥op≤ρB+B−1B=3ρ1+2ρ<3ρ; here fτ(H/α)=e−itH by finite spectral calculus and ∣fτ(x)∣=1. The factor −i is a relative phase under any outer control and is not discarded.

  5. Apply the explicit phase-corrected oblivious-amplitude-amplification unitary C=−WRW∗RW, where R=I−2Πzero and W is the complete even/odd block-combination circuit. Retain its work registers. By step 4, with V=e−itH, the certified OAA input error is ηOAA:=3ρ+2δqsp+8dϵBE≤(3ϵ128)2<1/100. 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 10ηOAA≤15ϵ/64, including the retained-work leakage.

    For the remaining implementation error, unroll the complete post-amplification circuit, including every occurrence within W,W∗,W, both OAA reflections, the known correction, each U/U∗ 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 N be the number of remaining primitive-unitary occurrences in this unrolled list. For N>0, require each finite-basis implementation G~j of its ideal unitary Gj to have a certified full-space bound ∥G~j−Gj∥op≤ϵ/(64N); 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 ϵ/64 (zero if N=0). The complete clean-input isometry certificate is therefore at most 15ϵ/64+ϵ/64=ϵ/4<ϵ; return CERTIFICATE_ERROR if 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.

LemmaStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-6-luna)Open item page →

Unitary hybrids on a preserved clean-work subspace

Statement

Let J:H→K be an isometry of nonzero finite-dimensional complex Hilbert spaces, V a unitary on H, and U a unitary on K. If ∥UJ−JV∥op≤η, then ∥U∗J−JV∗∥op≤η. The same inequalities hold after adjoining an arbitrary finite reference space or coherently controlling U and V against identity.

More generally, let U1,…,Us be actual unitaries on K and V1,…,Vs ideal unitaries on H, with ∥UjJ−JVj∥op≤ηj. Then ∥Us⋯U1J−JVs⋯V1∥op≤∑j=1sηj. The empty products give zero error. Each bound concerns the clean input subspace ran⁡J; 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.

[F1]

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.

[F2]

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

1.1givenF1algebra

The exact identity U∗(JV−UJ)V∗=U∗J−JV∗, together with unitary invariance, gives ∥U∗J−JV∗∥op=∥JV−UJ∥op≤η. Thus reversing the actual circuit supplies the inverse clean-input bound, without applying a forward error promise to dirty work.

2.1givenF2step 1.1algebra

If E:H→K has singular values sj, [F2] gives orthonormal bases in which ∥Eψ∥2=∑jsj2∣ψj∣2. Tensor those bases with any orthonormal reference basis; for Ψ=∑j,rcj,rej⊗fr, ∥(E⊗I)Ψ∥2=∑j,rsj2∣cj,r∣2≤∥E∥op2∥Ψ∥2. A top singular vector tensored with any unit reference vector attains equality, so ∥E⊗I∥op=∥E∥op. 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 E on the orthogonal control-one summand; its norm is at most ∥E∥op. These statements apply to step 1.1 as well as to the original forward bound.

3.1givenF1algebracases∎

For s≥1, expansion and cancellation give Us⋯U1J−JVs⋯V1=∑j=1sUs⋯Uj+1(UjJ−JVj)Vj−1⋯V1. Every factor outside the local difference is unitary, so each summand has norm at most ηj. The triangle inequality yields the claimed sum. On the right of each local difference are only ideal system unitaries followed by J, 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 s=0, both products are identity maps, so the difference is zero.

LemmaStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-6-luna)Open item page →

Clean sparse-state preparation from sorted XOR locations

Statement

Let H have the exact sorted d-sparse Hermitian XOR-location/value interface of Sparse Hamiltonian row and value oracles, with N,d≥1. An optional support-list variant is also admissible: each row lists at most d 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 j supplies the column list at j. 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 M=2⌈log⁡2(N+d)⌉ and H~=H⊕0 on the M-label register. Given a positive certified bound h≥max⁡i,j∣Hij∣, an exact costed preparation d−1/2∑l<d∣l⟩, and costed exact value-decoding and flag-rotation arithmetic, one can construct a unitary whose clean block is H~/(dh). It and its actual adjoint and coherent controlled versions use O(log⁡(d+1)) QA20 location calls and O(1) value calls each. Index bits, reversible workspace, search arithmetic, comparisons, uniform preparation, flags and gate precision remain separately charged. In particular h=1 is admissible when ∥H∥op≤1, 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.

[F1]

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.

[F2]

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.

[F3]

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 UL∗UR is ⟨UR(0,j),UL(0,i)⟩.

[F4]

QC24 State Preparation Cost Model charges the actual uniform-state preparation, inverse, arithmetic and controlled implementations separately.

Proof

1.1givenF1F2algebraconstruct

For x<M and l<d, let Fx(l) be the listed column if x<N and that slot exists, and N+l otherwise; set Fx(l)=0 on l≥d. This is a total function of finite bitstrings. On [d] it is strictly increasing: actual indices are distinct, sorted and below N, followed by distinct pads N+l. All outputs are below N+d≤M. For an invalid x, 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.

2.1F1F2step 1.1algebraconstruct

For each x,y<M, compute a total inverse lookup Gx(y) by lower_bound search of the strictly increasing list Fx(0),…,Fx(d−1). Start lo=0,hi=d; for ⌈log⁡2(d+1)⌉+1 rounds, if lo<hi, set m=⌊(lo+hi)/2⌋ and update lo=m+1 if Fx(m)<y, otherwise hi=m. 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 lo<d and Fx(lo)=y, output lo; 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 O(log⁡(d+1)) forward/reverse location calls including the final equality test. Strict increase gives Gx(Fx(l))=l for every l<d, including l=0; off the image no inversion is asserted.

3.1F1F2F4step 1.1step 2.1algebraconstruct

On two log⁡2M-bit registers apply the total permutation circuit ∣l,0⟩↦∣l,Fx(l)⟩↦∣lXORGx(Fx(l)),Fx(l)⟩↦∣Fx(l),0⟩, where the middle map is XOR evaluation of Gx into the first register and the final map swaps the registers. The displayed clean formula is asserted only on l<d 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 d−1/2∑l<d∣Fx(l)⟩ with all additional work zero. This constructs the required row-neighbor and column-neighbor isometries; Hermiticity identifies the column list at j with the row list at j.

4.1givenF1F4step 3.1algebraconstruct

Let VL∣0,i⟩=d−1/2∑l<d∣i,Fi(l)⟩ and VR∣0,j⟩=d−1/2∑l<d∣Fj(l),j⟩, including the common zero work registers from step 3.1. Put UL=Iflag⊗VL. After VR, compute aij=H~ij/h and apply the flag unitary with first column (aij,1−∣aij∣2)T and second column (−1−∣aij∣2,aij‾)T, then uncompute values and arithmetic. Its columns are orthonormal because ∣aij∣≤1, so this defines UR. Only UR inserts the value amplitude; UL has good-flag amplitude one.

5.1F1F3givenstep 3.1step 4.1algebra

Each row and, by Hermiticity, each column has at most d nonzero entries, each of magnitude at most h. For any vector v, ∣(Hv)i∣2≤(∑j∣Hij∣)(∑j∣Hij∣∣vj∣2) by Cauchy–Schwarz. Summing over i and using the row and column bounds gives ∥Hv∥2≤(dh)2∥v∥2, so ∥H∥op≤dh. The zero extension has the same operator norm, so the normalization dh is valid for the block encoding in [F3]. By [F3], the clean (i,j) matrix entry of UL∗UR is ⟨UR(0,j),UL(0,i)⟩. For a valid nonzero Hij, the ordered pair (i,j) occurs once on each side, with coefficients Hij/(hd) and 1/d, respectively. Their root pairing is Hij/(dh). 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, H=0, d>N, N=1, d=1, padded labels and repeated sentinel replies.

6.1F1F2F4step 1.1step 2.1step 3.1step 5.1algebra∎

Each clean location circuit uses O(log⁡(d+1)) 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 ∣Hij∣=∣⟨Hej,ei⟩∣≤∥H∥op, so h=1 is a valid supplied bound in the normalized HHL mode.

AlgorithmAlgorithm: AI-adaptedProof: Not applicableOpen item page →

Sparse Hermitian block encoding

Definition

Input. A finite dH-row-sparse Hermitian matrix H, with dH≥1. Put hmax⁡=max⁡i,j∣Hij∣ and supply a certified nonnegative entry bound h≥hmax⁡. Supply exact row and column enumerations. Write their neighbor labels as ρ(i,k) and κ(j,k), including dH 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 Or∣i,k⟩=∣i,ρ(i,k)⟩,Oc∣j,k⟩=∣κ(j,k),j⟩, 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 DdH preparing dH−1/2∑k=1dH∣k⟩, its inverse, and their costs. A classical table is not a free state-preparation circuit. If h=0, then H=0; use one flag qubit and return Xflag⊗I with normalization 1, whose all-zero flag compression is 0=H. Otherwise set α=dHh and supply a total reversible value-decoding query that writes a finite exact encoding of hij=Hij/h on valid pairs and 0 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 h>0. Put H^=H/h, so every queried entry hij=H^ij obeys ∣hij∣≤1. On the paired row/column index registers define VL=Or(I⊗DdH) SWAP,VR=Oc SWAP(DdH⊗I). Let UL=Iflag⊗VL. Define UR by first applying VR, then querying hij, applying to the flag (hij−1−∣hij∣21−∣hij∣2hij‾), and unquerying the value and arithmetic workspace. The square root is the nonnegative real root. Invalid sentinel pairs use hij=0. Return UL∗UR 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 O(1)-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 h≥max⁡i,j∣Hij∣, 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 M=2⌈log⁡2(N+dH)⌉ and returns a clean block encoding of (H⊕0)/(dHh), with normalization α=dHh. The location workspace is exactly clean on every prepared slot state. Each encoding, actual adjoint or coherent controlled encoding costs O(log⁡(dH+1)) original location calls and O(1) value calls; its sorting assumption is part of this mode's input. If ∥H∥≤1, choose h=1 without computing a maximum. Keep the one-sided value amplitude on UR exactly as above. The original supplied-permutation mode and its sharper call count remain available.

TheoremStatement: Literature-sourcedProof: AI-adaptedjudge pass (gpt-6-luna)Open item page →

Sparse Hamiltonian no-fast-forwarding

Statement

For n≥1, let Hx be the 2(n+1)-dimensional, at-most-two-sparse Hermitian path matrix of Sparse parity-path transfer and norm for x∈{0,1}n, with weights wi=i(n+1−i)/n. Put η=1/(6πn), choose b=⌈log⁡2(3πn)⌉, and round each nonzero weight to the nearest dyadic qi=2−bround⁡(2bwi), with ties rounded upward. Let H~x be the same path matrix with weights qi, and encode its entries exactly in the row-location/value XOR-oracle model of Sparse Hamiltonian row and value oracles, using O(log⁡(n+1)) value bits for the finite set {0,q1,…,qn}. Then ∥Hx−H~x∥op≤2η and the sparse normalization αsp=2max⁡iqi lies between positive absolute constants.

Suppose a uniform sparse-oracle query algorithm uses q total row-location and value-oracle calls and returns a clean-input isometry VxJclean satisfying ∥VxJclean−Joute−itH~x∥op≤13,t=πn/2, where Jout appends its work registers in zero. Then q≥n/4. Thus this sparse row/value model requires Ω(n)=Ω(t)=Ω(αspt) queries for this family. The result does not assert a lower bound for arbitrary block-encoding access.

Facts & Assumptions

Given: An integer n≥1, an input string x∈{0,1}n, the path family, and the coherent query model in the Statement.

[F1]

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.

[F2]

Sparse parity-path transfer and norm: ∥Hx∥op≤1 and e−iHxπn/2∣0,0⟩=(−i)n∣n,parity⁡(x)⟩.

[F3]

Unitary and differential identities for finite matrix dynamics: finite Hermitian matrix evolutions are unitary and differentiable with derivative −iHe−itH.

[F4]

The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: operator norm is submultiplicative and satisfies the finite-dimensional adjoint norm identity.

[F5]

Coherent XOR function oracle: the parity-bit oracle maps ∣j,y⟩↦∣j,y⊕xj⟩.

[F6]

A total Boolean XOR oracle is unitary and involutive: every total XOR-function oracle is a unitary involution.

[F7]

Bounded-error parity query lower bound: computing parity on all n-bit inputs with error at most 1/3 needs at least n/2 coherent bit queries.

[F8]

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.

[F9]

Circuit-family and computation cost model: named oracle calls are counted separately from ordinary gates and classical computation.

Proof

Given: As in Facts & Assumptions.

1.1F1F2givenalgebra

The maximum weight satisfies 1/2≤max⁡iwi≤1: i(n+1−i)≤(n+1)2/4≤n2 gives the upper bound, and a central integer i has i(n+1−i)≥n2/4 for the lower bound. Every edge satisfies i(n+1−i)−n=(i−1)(n−i)≥0, so wi≥w1=1/n>η. The grid spacing 2−b≤1/(3πn)=2η, hence nearest rounding gives ∣qi−wi∣≤η and qi>0; the support is unchanged. It follows that each row and column of Ex=H~x−Hx has at most two nonzero entries, each of magnitude at most η, and 2/9<max⁡iqi<2; in particular 2max⁡iqi is bounded above and below by positive constants independent of n,x.

1.2F1F5F6F8F9algebra

Order path vertices lexicographically by (i,b0). A location query's predecessor slot, when present, uses only xi, and its successor slot, when present, uses only xi+1; 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 qi 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 qi table is generated by integer arithmetic: binary search finds the nearest grid point by comparing squared integers with 22bi(n+1−i)/n2, using O(log⁡(n+1))-bit numbers and polynomially many steps in n. The location and value encodings are total, including sentinels, and this x-independent arithmetic is part of the fixed inter-query circuit.

2.1F1step 1.1algebra

For any vector v, Cauchy–Schwarz in each row gives ∣(Exv)r∣2≤(∑c∣(Ex)rc∣)(∑c∣(Ex)rc∣∣vc∣2). Summing over r, each row sum and each column sum of ∣Ex∣ is at most 2η. Hence ∥Exv∥2≤(2η)(2η)∥v∥2, proving ∥H~x−Hx∥op≤2η.

3.1F3F4step 2.1algebra

For 0≤s≤t, differentiate G(s)=e−i(t−s)H~xe−isHx. By [F3], G′(s)=ie−i(t−s)H~x(H~x−Hx)e−isHx. The two outside factors are unitary, so [F4] gives ∥G′(s)∥op≤∥H~x−Hx∥op. Integrating from 0 to t and using step 2.1 yields ∥e−itH~x−e−itHx∥op≤t(2η)=πnη=16.

4.1F2F8step 3.1algebra

Apply the assumed simulator to the initial state ∣0,0⟩ with clean work registers. The triangle inequality, step 3.1, and [F2] show that its output vector is within 1/3+1/6=1/2 of Jout(−i)n∣n,parity⁡(x)⟩. The orthogonal projection onto outcomes whose second path bit is not parity⁡(x) annihilates this target vector. Its norm on the simulator output is therefore at most 1/2, so measuring the path bit computes parity with error at most 1/4. This is a valid bounded-error parity algorithm on every x.

5.1F7step 1.1step 4.1step 1.2algebra∎

Replacing each of the algorithm's q sparse-oracle calls by this reversible simulation yields a parity algorithm using at most 2q bit queries and error at most 1/4≤1/3. By [F7], 2q≥n/2, hence q≥n/4. From step 1.1 the sparse normalization is Θ(1), and t=πn/2, so the lower bound is equivalently Ω(n)=Ω(t)=Ω(αspt) on this family.

AlgorithmAlgorithm: AI-adaptedProof: Not applicableOpen item page →

Small-time-aware qubitized simulator

Definition

Input. An exact Hermitian block encoding of H/α, α>0, a rational time t, rational 0<ϵ<1, and a terminating certified comparison of τ=α∣t∣ with ϵ, charged under the access model. For the τ>ϵ branch, supply all exact-block-mode inputs of Qubitized Hamiltonian simulator: the certified norm promise ∥H∥op≤α, 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 τ with ϵ.

Instructions.

  1. If τ≤ϵ, 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.
  2. If τ>ϵ, invoke Qubitized Hamiltonian simulator with t,ϵ, 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.

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Correctness and access cost of sparse block encoding

Statement

For a dH-row-sparse Hermitian H, put hmax⁡=max⁡i,j∣Hij∣ and supply a certified bound h≥hmax⁡. In the supplied-permutation mode, if h=0 then H=0 and the returned Xflag⊗I has zero all-zero-flag compression and normalization 1; if h>0, the designated all-zero auxiliary compression of UL∗UR from Sparse Hermitian block encoding is H/(dHh). 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 DdH, inverse, flag-rotation, and arithmetic implementations. In the optional sorted-XOR mode with h>0, the encoded matrix is H⊕0 with normalization dHh, and each encoding, actual adjoint or coherent controlled call costs O(log⁡(dH+1)) original location calls and O(1) 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 h≥hmax⁡, exact reversible location permutations and costed uniform preparation; when h>0, 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 h=0 branch is common to both modes.

[F1]

Sparse Hermitian block encoding: VL prepares the uniform row-neighbor state, VR the uniform column-neighbor state, and the supplied-permutation mode's flag-zero value amplitude is hij=Hij/h for its certified bound h.

[F2]

Qubitization access model: an all-zero compression is J∗UJ; its normalization and access costs are separate.

[F3]

Finite block encoding: a projected block of a supplied unitary is the encoded matrix divided by its declared normalization.

[F4]

Sparse Hamiltonian row and value oracles: valid row/value access describes a Hermitian matrix and counts each named oracle call separately.

[F5]

Dirac Notation Conversion: ∣u⟩⟨v∣ contributes the matrix entry ⟨u,ei⟩root⟨ej,v⟩root in the stated basis convention.

[F6]

Qubitization access model: an exact block encoding is a unitary dilation whose all-zero compression is the normalized target matrix.

[F7]

Clean sparse-state preparation from sorted XOR locations constructs the optional sorted-XOR mode with clean workspace, normalization dHh, and the stated ordinary, inverse and controlled query counts.

Proof

Given: As in Facts & Assumptions.

1.1F1F3F4F6givenalgebra

First consider the supplied-permutation mode. If h=0, the promised bound implies every entry of H is zero. The returned Xflag⊗I is unitary and its compression against the clean flag ∣0⟩ is ⟨0∣X∣0⟩I=0, so normalization 1 encodes H=0. Now suppose h>0. Each row and, by Hermiticity, each column has at most dH nonzero entries, each of magnitude at most h. For any vector v, Cauchy–Schwarz gives ∣(Hv)i∣2≤(∑j∣Hij∣)(∑j∣Hij∣∣vj∣2). Summing over i and using the row and column bounds gives ∥Hv∥2≤(dHh)2∥v∥2, so ∥H∥op≤dHh and the stated normalization is valid in [F3]. For each system label i, VL∣0,i⟩=dH−1/2∑k=1dH∣i,ρ(i,k)⟩, where ρ(i,k) is its listed column or a distinct sentinel. For each system label j, (DdH⊗I)∣0,j⟩=dH−1/2∑ℓ=1dH∣ℓ,j⟩; the register SWAP sends this to dH−1/2∑ℓ=1dH∣j,ℓ⟩, so the supplied action of Oc gives VR∣0,j⟩=dH−1/2∑ℓ=1dH∣κ(j,ℓ),j⟩. Each is normalized because its dH terms are orthonormal and have coefficient dH−1/2.

1.2F1F4algebra

For ∣h∣≤1, the two columns of the stated flag matrix have unit norm and inner product h‾(−1−∣h∣2)+1−∣h∣2 h‾=0; hence it is unitary and its flag-zero output amplitude from ∣0⟩ is h. The value oracle is uncomputed after this rotation, so the good-flag overlap retains no value workspace.

2.1F1F2F3F4F5F6step 1.1step 1.2algebra

With the root inner product linear in its first argument, the (i,j) matrix entry of UL∗UR is ⟨UR(∣0⟩⊗∣j⟩), UL(∣0⟩⊗∣i⟩)⟩root, by the adjoint identity and the ordered-basis coordinate convention in [F5]. The row and column lists contain the pair (i,j) once each exactly when Hij≠0, by Hermiticity and the nonzero-entry enumeration. In that case the unique matching basis pair contributes dH−1hij=Hij/(dHh): the UR amplitude is first in the root pairing and equals hij/dH, while the row-state amplitude is 1/dH. If Hij=0, there is no valid matching pair, and the sentinel branch has flag-zero amplitude 0. Thus every clean matrix entry is the stated one, proving the compressed block identity.

3.1F1F2step 2.1

The displayed circuit for UL∗UR applies the inverse row-location permutation once through VL∗, the column-location permutation once through VR, 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.

4.1F7step 3.1algebra∎

In the optional sorted-XOR mode, apply [F7] to the supplied sorted QA20 interface and positive entry bound h. Its clean isometries insert Hij/h only on the column side, give the padded block (H⊕0)/(dHh), 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.

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Jacobi–Anger Chebyshev truncation

Statement

With Jk,Tk defined in Hamiltonian-simulation polynomial target, for every real τ and integer d≥0, sup⁡∣x∣≤1∣exp⁡(−iτx)−(J0(τ)+2∑k=1d(−i)kJk(τ)Tk(x))∣≤2∑k>d∣Jk(τ)∣≤2(∣τ∣/2)d+1(d+1)!(1−∣τ∣/(2(d+2))) whenever ∣τ∣/(2(d+2))<1. In the OP4 simulator, ρ=ϵ2/10000 and the factorial-tail stopping test therefore certifies truncation error at most ρ/8; rounding each coefficient to error at most ρ/(32(d+1)) keeps the combined polynomial error below ρ. For the nontrivial simulation regime ∣τ∣>ϵ, an integer d passing OP4's test exists with d=O(∣τ∣+ln⁡(1/ϵ)ln⁡(e+ln⁡(1/ϵ)/∣τ∣)). For ∣τ∣≤ϵ, the constant polynomial 1 approximates the target within ϵ; the wrapper algorithm uses the exact identity circuit in that branch.

Facts & Assumptions

Given: Real τ, the coefficient series and Chebyshev recurrence in Hamiltonian-simulation polynomial target, and rational 0<ϵ<1.

[F1]

Hamiltonian-simulation polynomial target: Jk(τ)=∑j≥0(−1)j(τ/2)2j+k/(j!(j+k)!), T0=1,T1=x,Tk+1=2xTk−Tk−1, and fτ(x)=e−iτx.

[F2]

The complex exponential by its power series: ez=∑n≥0zn/n!.

[F3]

The complex exponential series converges absolutely for every complex argument: the exponential series converges absolutely at every complex argument.

[F5]

The complex exponential is entire and its complex derivative is itself: the complex exponential is differentiable with derivative equal to itself.

[F6]

exp⁡(z+w)=exp⁡z exp⁡w, and the complex exponential extends the real exponential: the complex exponential satisfies exp⁡(z+w)=exp⁡(z)exp⁡(w) for all complex z,w.

[F7]

Qubitized Hamiltonian simulator: OP4 sets ρ=ϵ2/10000, starts the degree loop at d=max⁡(1,⌈∣τ∣⌉), accepts a factorial-tail bound at most ρ/8, and rounds d+1 coefficients within ρ/(32(d+1)) each.

[F8]

Small-time-aware qubitized simulator: if α∣t∣≤ϵ, the wrapper returns the exact identity circuit.

Proof

Given: As in Facts & Assumptions.

1.1F1algebrainduction

The Chebyshev recurrence and cos⁡((k+1)θ)+cos⁡((k−1)θ)=2cos⁡θcos⁡(kθ) show by induction that Tk(cos⁡θ)=cos⁡(kθ) for all k≥0. Every x∈[−1,1] is cos⁡θ for some real θ, so ∣Tk(x)∣≤1.

1.2F1F2F3F4algebra

If τ≠0, the ratio of consecutive absolute terms in the defining series of Jk(τ) is ∣τ∣2/[4(j+1)(j+k+1)], which tends to zero, so the series converges absolutely; for τ=0 the series is immediate. For the integral Ik,j=∫01uj−1/2(1−u)k−1/2 du, splitting 1=u+(1−u) and integrating the derivative of uj+1/2(1−u)k+1/2 gives Ik,j+1/Ik,j=(j+1/2)/(j+k+1). Iterating yields Ik,j/Ik,0=(2j)!k!/[4jj!(j+k)!]. Expanding eiτt uniformly on [−1,1], substituting u=t2, and using this moment ratio gives Jk(τ)=(τ/2)kk!Ik,0∫−11eiτt(1−t2)k−1/2 dt. The weight is nonnegative and its normalized integral is one; [F4] therefore gives ∣Jk(τ)∣≤(∣τ∣/2)k/k!.

1.3F1F7givenalgebra

Put L=ln⁡(1/ϵ)>0, τ0=∣τ∣>ϵ, and S=τ0+L/ln⁡(e+L/τ0). If τ0≥1/4, then S≥1/4. If τ0<1/4, put u=1/τ0>4, so L>ln⁡u. The function g(y)=y/ln⁡(e+uy) is increasing for y>0, since its derivative has numerator ln⁡(e+uy)−uy/(e+uy)>0; the first term exceeds 1 and the second is below 1. Also ln⁡u≤u/2 for u≥4 (the difference has positive derivative and is positive at 4), and e≤u2/2, so e+uln⁡u≤u2. Therefore S≥g(L)>g(ln⁡u)≥1/2. In all cases S≥1/4. Set m=⌈64S⌉; then m≥16, m≥64τ0, and d=m−1≥max⁡(1,⌈τ0⌉), so this is an eligible candidate in the OP4 loop starting rule [F7]. The real parameter τ is defined in [F1].

2.1F1F2F3F4F6step 1.1algebra

For x=cos⁡θ, [F6] writes e−iτx as the product of the absolutely convergent exponential series for e−iτeiθ/2 and e−iτe−iθ/2. Absolute convergence permits grouping terms by the exponent difference k; for each k≥0 the coefficient of eikθ is (−i)kJk(τ), and the coefficient of e−ikθ is the same. Pairing these terms and using [F1] gives e−iτx=J0(τ)+2∑k≥1(−i)kJk(τ)Tk(x). By [F1], [F3], [F4], and [F6], the defining product is absolutely and uniformly convergent in θ on bounded intervals, so the regrouping is valid.

3.1F1step 1.1step 1.2step 2.1algebra

Subtracting the degree-d partial sum in step 2.1, applying the triangle inequality, and using ∣Tk(x)∣≤1 from step 1.1 gives error at most 2∑k>d∣Jk(τ)∣. With m=d+1, the ratio of consecutive factorial majorants (∣τ∣/2)k/k! for k≥m is at most u=∣τ∣/(2(m+1))<1. The geometric-series bound from step 1.2 now yields the stated factorial tail.

4.1F1F7step 3.1algebra

By [F7], each of the d+1 OP4 coefficient errors is at most ρ/(32(d+1)). Since a nonconstant Chebyshev term has coefficient multiplier 2, their total uniform contribution is at most (1+2d)ρ/(32(d+1))<ρ/16; adding the tail ρ/8 from step 3.1 gives a combined error <ρ. The coefficients and multiplier 2 are those of the expansion in [F1].

4.2F1F7givenstep 3.1algebra

Let r=L/τ0. If r≥1, then m≥64τ0(1+r/ln⁡(e+r)). Since ln⁡y≤y for y≥1 and r/(e+r)≥1/(e+1), we have r/ln⁡(e+r)≥e+r/(e+1). Thus 2m/(eτ0)≥[128/(e(e+1))]e+r>e+r, because e<3 gives 128/(e(e+1))>128/12>1; hence ln⁡(2m/(eτ0))≥12ln⁡(e+r). If r<1, m≥64τ0 gives 2m/(eτ0)≥128/e, hence ln⁡(2m/(eτ0))>1, since e<3 implies 128/e>42>e. For r≥1, these bounds give mln⁡(2m/(eτ0))≥32τ0(ln⁡(e+r)+r)≥32(L+τ0), because ln⁡(e+r)>1. For r<1, they give mln⁡(2m/(eτ0))>64τ0>32(L+τ0), because L<τ0. Also um=τ0/[2(m+1)]≤1/128, so 2/(1−um)≤256/127<4, and the factorial tail expression is bounded by the estimate of step 3.1. The real τ is defined in [F1]. ln⁡(m!)=∑j=1mln⁡j≥∫1mln⁡x dx≥mln⁡m−m, so m!≥(m/e)m. The factorial-tail test at d=m−1 is consequently at most 2(τ0/2)mm!(1−um)≤4exp⁡ ⁣(−mln⁡2meτ0)≤4e−32(L+τ0)≤e−2L/80000=ρ/8. The last inequality holds because L+τ0>1: for 0<ϵ<1, the function ln⁡(1/ϵ)−(1−ϵ) is positive (its derivative is (ϵ−1)/ϵ<0 as ϵ increases to 1, and its value at 1 is zero), and τ0>ϵ. Hence 30(L+τ0)>30>ln⁡(320000); the numerical comparison follows from e>2, which gives e30>230>320000. Since m≤64S+1≤68S using S≥1/4, the passing degree d=m−1 is O(S). The factorial tail is the estimate of step 3.1 and the OP4 threshold is [F7].

5.1F4F5F8givenalgebra∎

If τ0≤ϵ, the derivative fact [F5] and ∣e−isx∣=1 from [F4] give e−iτx−1=∫0τ−ixe−isx ds,∣e−iτx−1∣≤∣τx∣≤ϵ for every x∈[−1,1]. Thus the wrapper [F8] has the stated small-time error, including τ=0.

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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 t, and 0<ϵ≤1, a successful execution of Qubitized Hamiltonian simulator returns a unitary dilation Vimpl satisfying ∥VimplJclean−Joute−itH∥op≤ϵ on the clean-input isometry Jclean, where Jout 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 t=0, the declared identity branch is exact. For every reference system, tracing out work gives the corresponding system-output trace-distance bound at most ϵ.

Facts & Assumptions

Given: A finite Hermitian H with ∥H∥op≤α, either the exact or budgeted bounded-error normalized block encoding, and a successful certified OP4 execution.

[F1]

Qubitized Hamiltonian simulator: OP4 gives combined scalar error at most ρ=ϵ2/10000, sets each real phase-synthesis tolerance to δqsp=ρ/64, requires the bounded-block mode to satisfy ηOAA=3ρ+2δqsp+8dϵBE≤(3ϵ/128)2, and allocates the remaining post-amplification primitive-implementation error at most ϵ/64.

[F2]

Jacobi–Anger Chebyshev truncation: the factorial-tail polynomial and its certified coefficient rounding give the stated scalar error on [−1,1].

[F3]

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 δqsp.

[F4]

QSVT action on singular subspaces: the alternating QSVT sequence has the stated polynomial compressed block on each singular space, including endpoints and null spaces.

[F6]

The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: ∥ST∥op≤∥S∥op∥T∥op.

[F7]

Clean-input isometry error bounds output trace distance: clean-input isometry error bounds the reference-system trace distance after work is traced out.

[F9]

Qubitization access model and Qubitized Hamiltonian simulator: the exact and bounded-error modes both satisfy ∥H∥op≤α.

[F10]

Robust one-step OAA including ancilla leakage: if W has clean block M/2 and ∥M−V∥op≤η≤1/100 for a unitary V, the phase-corrected OAA circuit has clean-input isometry error at most 10η, including workspace leakage.

[F11]

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.

[F12]

Operator perturbation bound for QSVT: for any fixed exact degree-d QSVT phase sequence, changing the encoded contraction by ϵBE changes its singular-value-transform block by at most 4dϵBE.

Proof

Given: A successful output of the simulator in Facts & Assumptions; when t≠0, all OP4 certificates succeed. Put V=e−itH and ρ=ϵ2/10000.

1.1F1F8algebra

If t=0, [F1] returns the identity circuit with no signal query, and e−itH=I; hence its clean-input isometry error is zero. If t≠0, continue with the certified OP4 output.

1.2F1F2F3F4F8F9F12algebra

On an eigenvector of H with eigenvalue λ, H/α has eigenvalue x=λ/α∈[−1,1], by [F8] and [F9]. Put Q=E−iO and B=1+2ρ. The OP4 certificate gives ∥Q−fτ∥[−1,1]≤ρ by [F1]–[F2], and ∣fτ(x)∣=1. Since M=Q/B, ∣M(x)−fτ(x)∣≤∣Q(x)−fτ(x)∣/B+(B−1)∣fτ(x)∣/B≤3ρ/(1+2ρ)<3ρ. In the orthonormal eigenbasis, this error is diagonal; for any vector with coordinates cj, its squared output norm is the sum of ∣cj∣2 times the squared scalar errors. Thus the uniform scalar bound gives ∥M(H/α)−e−itH∥op<3ρ. For each real QSVT extraction, [F3] bounds the exact phase-list polynomial mismatch plus the implemented phase-slot errors by δqsp=ρ/64 uniformly on [−1,1]. For the bounded mode, apply [F12] to the exact Φ and −Φ phase sequences on the supplied block and on the target block; each branch changes by at most 4dϵBE, so their average has the same block-perturbation bound. The coefficient-qubit average therefore gives a complete block Mph/2 with ∥Mph−M(H/α)∥op≤2δqsp+8dϵBE. For even pE, pE(σ)=pE(−σ); for odd pO, the singular vector of a negative eigenvalue has left vector u=−v, so pO(σ)∣u⟩⟨v∣=−pO(σ)∣v⟩⟨v∣=pO(−σ)∣v⟩⟨v∣, and pO(0)=0. Thus these transforms are the ordinary polynomial blocks on H/α. The certified error is consequently ∥Mph−e−itH∥op<3ρ+2δqsp+8dϵBE≤ηOAA≤(3ϵ128)2.

2.1F1F10step 1.2algebra

Since 0<ϵ≤1, ηOAA≤9/16384<1/100. Apply [F10] to the unitary W whose clean block is Mph/2, with target unitary V=e−itH. The phase-corrected OAA circuit therefore has clean-input isometry error at most 10ηOAA≤30ϵ/128=15ϵ/64, including its retained-work leakage.

3.1F1F6F11step 2.1algebra

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 N>0 such occurrences, each contributes at most ϵ/(64N) 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 ∑j=1Nϵ/(64N)=ϵ/64; for N=0 the difference is zero. Thus the actual clean-input isometry error is at most 15ϵ/64+ϵ/64=ϵ/4<ϵ.

4.1F1F7step 1.1step 3.1algebra∎

If t=0, [F1] returns the exact identity and the conclusion holds with zero error. If t≠0, 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 ϵ.

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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 ∥VimplJclean−Joute−itH∥op≤ϵ. 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 t, and 0<ϵ<1.

[F1]

Small-time-aware qubitized simulator: the small branch returns identity when α∣t∣≤ϵ; otherwise it invokes OP4.

[F2]

Qubitized simulation correctness: on a successful OP4 output, the clean-input isometry error is at most ϵ, and the traced output has the same trace-distance bound.

[F3]

Finite Hermitian matrix dynamics: UH(t)=e−itH is defined by the finite spectral functional calculus.

[F4]

Unitary and differential identities for finite matrix dynamics: UH′(s)=−iHUH(s) and UH(s) is unitary.

[F5]

The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: ∥ST∥op≤∥S∥op∥T∥op.

[F6]

Clean-input isometry error bounds output trace distance: clean-input isometry error δ bounds the reference-system trace distance after tracing work by δ.

[F7]

Qubitization access model: the block-encoding normalization satisfies ∥H∥op≤α.

Proof

Given: As in Facts & Assumptions.

1.1F1F2cases

If α∣t∣>ϵ, the algorithm invokes OP4, and [F2] gives the clean-input isometry bound.

1.2F3F4F5F7givenalgebra

Suppose α∣t∣≤ϵ. For a unit vector ψ, the differential identity [F4] and the fundamental theorem of calculus give UH(t)ψ−ψ=∫0t−iHUH(s)ψ ds. Each integrand has norm at most ∥H∥op, because UH(s) preserves norm. The triangle inequality for Riemann sums and continuity yield ∥(UH(t)−I)ψ∥≤∣t∣∥H∥op≤α∣t∣≤ϵ, using [F7]. Taking the maximum over unit ψ proves the clean-input operator bound for the identity circuit. The work registers are exactly zero, so the same isometry bound holds.

2.1F1F2F6F7step 1.1step 1.2algebra∎

In either branch, the implementation on clean inputs and the target Joute−itH are isometries whose operator-norm difference is at most ϵ, by steps 1.1 and 1.2. Apply [F6] with δ=ϵ to obtain the reference-system trace-distance conclusion after tracing work.

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Qubitization query and gate resources

Statement

Let τ=α∣t∣ and L=ln⁡(1/ϵ), with 0<ϵ<1. In the nontrivial branch τ>ϵ, put S=τ+L/ln⁡(e+L/τ). The small-time-aware simulator of Small-time-aware qubitized simulator uses zero signal queries when τ≤ϵ. When τ>ϵ, the OP4 degree satisfies d=O(S). The complete OAA circuit uses at most 3(dE+dO)≤6d ordinary U/U∗ signal calls, where dE,dO≤d are the even and odd QSP degrees. It uses 3(dE+dO+2) projector-phase slots and therefore 6(dE+dO+2) 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 W,W∗,W, the total is 3(1dE odd+1dO odd)≤6 controlled signal calls. The Φ/−Φ 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 dH-row-sparse input with supplied-permutation bound h≥hmax⁡, the h=0 branch encodes H=0 with normalization 1 and uses no location or value calls. If h>0, Correctness and access cost of sparse block encoding gives normalization α=dHh; choosing the tight bound h=hmax⁡ gives α=dHhmax⁡. 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 dHh for a supplied positive entry bound h and multiplies the signal count by O(log⁡(dH+1)) location calls and O(1) value calls per signal, including inverses and coherent controls. No cost-free conversion from XOR row-location access is claimed. If τ is bounded above and below by positive constants as ϵ→0, the query bound specializes to O(τ+L/ln⁡L)=O(τ+ln⁡(1/ϵ)/ln⁡ln⁡(1/ϵ)). 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 t,ϵ, and all separately charged access operations.

[F1]

Small-time-aware qubitized simulator: use the identity branch for τ≤ϵ, otherwise execute OP4.

[F2]

Jacobi–Anger Chebyshev truncation: for τ>ϵ, the OP4 factorial-tail loop terminates at degree d=O(τ+L/ln⁡(e+L/τ)).

[F3]

QSVT signal and reflection query counts: a degree-r signal sequence has r signal calls, r+1 projector phases, and at most one controlled signal call when controlled against identity if r is odd.

[F4]

QSP signal-query and phase-rotation count: a scalar QSP sequence of degree r has r signal factors and r+1 phase factors; signal-query and phase-gate costs are separate.

[F5]

QSVT robustness, approximation error, and resources: every sequence access, phase, controlled operation, synthesis, and precision budget is a separate declared cost.

[F6]

Correctness and access cost of sparse block encoding: the supplied-permutation sparse signal block uses normalization 1 and no sparse calls when h=0; for h>0 its normalization is dHh for certified h≥hmax⁡, including the tight choice h=hmax⁡. The sorted-XOR branch with positive h has normalization dHh and costs O(log⁡(dH+1)) location and O(1) value calls per signal, including inverses and controls.

[F7]

Qubitized simulation correctness: on successful execution, the declared circuit satisfies the operator-isometry error guarantee.

Proof

Given: As in Facts & Assumptions.

1.1F1F2cases

If τ≤ϵ, [F1] returns the exact identity and makes no signal call. If τ>ϵ, the degree test of [F2] has a finite passing candidate and its least accepted degree obeys the stated O(S) bound.

1.2F1F3algebra

In one OAA application, W contains one even and one odd real QSVT extraction. By [F3], they use dE+dO signal calls and dE+dO+2 projector-phase slots. OAA uses W,W∗,W, so the total is exactly 3(dE+dO) signal calls and 3(dE+dO+2) phase slots, bounded above by 6d and 6d+6, respectively.

2.1F3F4F5step 1.2algebra

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 W,W∗,W, so the even and odd extractions together use exactly 3(1dE odd+1dO odd) such calls. By [F4], the control selecting Φ or −Φ acts only on phase rotations and adds no signal query. Any further external control invokes the separately supplied controlled-U or controlled-U∗ operations by [F5].

3.1F5F6step 1.2step 2.1

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 O(log⁡(dH+1)) location and O(1) 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.

4.1F2F7step 1.1algebra∎

When 0<c≤τ≤C<∞ and ϵ→0, L=ln⁡(1/ϵ)→∞ and ln⁡(e+L/τ)=Θ(ln⁡L), since L/C≤L/τ≤L/c. Substituting this comparison into S gives O(τ+L/ln⁡L), the stated standard-regime form. Correctness remains the separate theorem [F7].

5 · Examples, counterexamples and false statements

None yet.

Sources