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Linear Combinations of Unitaries and Taylor Simulation

1 · Prerequisites

2 · Summary

An LCU input is a finite ordered decomposition together with complete PREPARE, SELECT, inverse, controlled-access, and cost contracts. Its clean-ancilla projected block is the represented matrix divided by the coefficient sum; the zero branch has an input-dependent postselection weight. One-step oblivious amplification uses the explicit reflection I−2Π0 and a tracked global phase. The Taylor construction expands ordered products, adds cancelling identity terms to set the LCU scale, and segments time so robust clean-input error and retained-ancilla leakage accumulate within the tolerance. Query, gate, width, coefficient, table, inverse, control, and classical generation costs remain separate. Continuous-rotation preparation bounds do not provide finite-gate synthesis or coefficient bit-time by themselves.

The certified Taylor scheduler uses interval bounds without exact real-zero tests and chooses its truncation order by rational factorial comparisons. Its 3rK term-query bound assumes explicitly supplied coherent indexed term selection; assembly from separate controlled terms costs up to 3rmK individual calls, with routing and phase costs recorded.

3 · Logical flowchart

4 · Definitions, theorems and proofs

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

Pure-state trace distance is bounded by vector error

Statement

Let H be a finite-dimensional complex inner-product space and let u,v∈H be unit vectors. Write Pu=∣u⟩⟨u∣ and Pv=∣v⟩⟨v∣ using the Dirac convention of Dirac Notation Conversion, and define their trace distance by D(Pu,Pv):=12∥Pu−Pv∥1, where ∥⋅∥1 is the trace norm of Trace Norm. Then D(Pu,Pv)≤∥u−v∥.

Facts & Assumptions

Given: A finite-dimensional complex-inner-product space H and unit vectors u,v∈H.

[F1]

Dirac Notation Conversion: ∣u⟩⟨u∣ sends z to ⟨z,u⟩rootu, and its coordinate matrix in an orthonormal basis is [u][u]†.

[F2]

The norm ∥v∥=⟨v,v⟩ induced by a real or complex inner product: ∥x∥2=⟨x,x⟩ for the root inner product, linear in its first argument.

[F3]

Cauchy–Schwarz: ∣⟨u,v⟩∣≤∥u∥∥v∥, with equality exactly for linearly dependent vectors: ∣⟨x,y⟩∣≤∥x∥∥y∥, with equality exactly when the vectors are linearly dependent.

[F4]

Trace Norm: ∥A∥1 is the sum of the singular values of A, counted on its finite-dimensional domain.

[F5]

Trace Norm Spectral Formula: ∥A∥1=tr⁡A†A.

Proof

Given: As in Facts & Assumptions.

1.1givenF1F2F3algebra

Put c:=⟨u∣v⟩D and s:=1−∣c∣2. By [F3], 0≤s≤1. If s=0, equality in Cauchy–Schwarz makes v a unit scalar multiple of u, so Pv=Pu and D(Pu,Pv)=0.

1.2givenF1F4F5F6algebra

Suppose s>0 and set e:=(v−cu)/s. The Dirac convention makes ⟨u∣e⟩D=0, and direct expansion gives ∥e∥=1. In the orthonormal basis (u,e) of their span, Pu−Pv has matrix (s2−cs−c‾ s−s2); it is zero on the orthogonal complement. This Hermitian matrix has trace 0 and determinant −s2, hence eigenvalues s,−s. By [F4]–[F6], ∥Pu−Pv∥1=2s, so D(Pu,Pv)=s.

2.1givenF2step 1.1step 1.2algebra∎

For either case, ∥u−v∥2=2−2Re⁡c≥2(1−∣c∣), while s2=(1−∣c∣)(1+∣c∣)≤2(1−∣c∣). Thus D(Pu,Pv)=s≤∥u−v∥, including the s=0 case from step 1.1.

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Clean-ancilla zero-branch postselection

Definition

Let A be a finite register with distinguished unit computational-basis vector ∣0A⟩ (unit by The computational basis is orthonormal), let S be a finite-dimensional complex-inner-product space, and define the isometry J:S→A⊗S by Jx=∣0A⟩⊗x. The product inner product makes J an isometry; Finite-dimensional linear isometries satisfy the adjoint identity gives J†J=IS, and An isometric quantum encoding gives an orthogonal code projector makes Π0:=JJ† the orthogonal projection onto its image. On elementary tensors this is ∣0A⟩⟨0A∣⊗IS by the convention of Tensor products of finitely many linear maps. For a unit vector ϕ∈A⊗S, define its zero-branch vector q:=J†ϕ, zero-branch weight p0:=∥q∥2, and complementary-branch weight p⊥:=∥(I−Π0)ϕ∥2. When p0>0, the conditional system vector is q/p0; when p0=0, no conditional vector is assigned. This definition fixes only the clean-ancilla two-outcome branch rule used on this page; it makes no physical realization claim.

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

Partial trace contracts the trace norm on Hermitian operators

Statement

Let HA,HE be finite-dimensional complex inner-product spaces and let X=X† be an endomorphism of HA⊗HE. Then ∥Tr⁡EX∥1≤∥X∥1. The trace norm and partial trace are those of Trace Norm and Partial trace over a finite tensor factor. If either factor is zero-dimensional, both sides are zero.

Facts & Assumptions

Given: Finite-dimensional complex-inner-product spaces HA,HE and a Hermitian endomorphism X of HA⊗HE.

[F1]

Trace Norm: ∥Z∥1 is the sum of the singular values of Z.

[F2]

Trace Norm Spectral Formula: ∥Z∥1=tr⁡Z†Z.

[F4]

Partial trace over a finite tensor factor: in ordered orthonormal product bases, (Tr⁡EX)ii′=∑jX(i,j),(i′,j).

[F5]

The partial trace is characterized by the trace pairing and is basis-independent: tr⁡((Tr⁡EX)Y)=tr⁡(X(Y⊗IE)) for every endomorphism Y of HA.

[F6]

Tensor products of finitely many linear maps: (Y⊗IE)(x⊗e)=Yx⊗e on elementary tensors.

[F7]

Ordered tensor registers, their inner product, and wire permutations: the product inner product and ordered product basis are the finite tensor-product convention.

[F8]

The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: ∥Y∥=max⁡∥x∥=1∥Yx∥ on a nonzero domain.

[F10]

Every finite-dimensional real or complex inner product space has an orthonormal basis: each finite-dimensional inner-product factor has an orthonormal basis.

[F12]

The basis-independent trace of an endomorphism of a finite-dimensional vector space: trace is the sum of diagonal entries in any ordered basis.

Proof

Given: As in Facts & Assumptions.

1.1F1F2F3F4F8F9F11F12choosecasesalgebra

If either tensor factor is zero-dimensional, then X=0 and the claimed inequality follows from the empty coordinate sum in [F4]; assume both factors are nonzero for the rest of the proof. For any Hermitian Z on a nonzero finite-dimensional complex-inner-product space, [F3] gives an orthonormal eigenbasis (ei) with eigenvalues λi. The adjoint identity [F11] and conjugate symmetry make λi=⟨Zei,ei⟩=⟨ei,Zei⟩=λi‾, so each λi is real. For every Hermitian Y with ∥Y∥≤1, [F12] gives tr⁡(ZY)=∑iλi⟨Yei,ei⟩root. Again [F11] makes each diagonal entry real, and [F8]–[F9] put it in [−1,1]. Thus ∣tr⁡(ZY)∣≤∑i∣λi∣=∥Z∥1: in the eigenbasis, Z†Z has eigenvalues λi2, so [F2] its nonnegative square root has eigenvalues ∣λi∣, and [F1], [F2], [F12] give the trace norm sum. Equality is attained by the Hermitian contraction acting by sgn⁡(λi) on each ei (and by 0 when λi=0). Hence ∥Z∥1=sup⁡Y=Y†, ∥Y∥≤1∣tr⁡(ZY)∣.

1.2givenF4F6F7F8F10F11algebra

Formula [F4] shows Tr⁡EX is Hermitian: transposing and conjugating its (i,i′) entry gives ∑jX(i′,j),(i,j)‾=∑jX(i,j),(i′,j). If Y=Y† and ∥Y∥≤1, choose an orthonormal basis (fj) of HE by [F10] and write arbitrary ξ=∑jxj⊗fj and ζ=∑jyj⊗fj. The product inner product and tensor-map action [F6]–[F7] give ⟨(Y⊗IE)ξ,ζ⟩=∑j⟨Yxj,yj⟩=∑j⟨xj,Yyj⟩=⟨ξ,(Y⊗IE)ζ⟩ by self-adjointness and [F11], so Y⊗IE is Hermitian. Also [F6]–[F8] give ∥(Y⊗IE)ξ∥2=∑j∥Yxj∥2≤∑j∥xj∥2=∥ξ∥2, so ∥Y⊗IE∥≤1.

2.1F1F4F5step 1.1step 1.2∎

For the remaining nonzero-factor case, apply the variational identity in step 1.1 to Tr⁡EX. For each allowed Y, [F5] and step 1.2 give ∣tr⁡((Tr⁡EX)Y)∣=∣tr⁡(X(Y⊗IE))∣≤∥X∥1, using step 1.1 for the Hermitian contraction Y⊗IE. Taking the supremum proves the claimed inequality.

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

Clean-ancilla postselection has normalized branch weights

Statement

With the notation of Clean-ancilla zero-branch postselection, every unit vector ϕ has p0,p⊥∈[0,1] and p0+p⊥=1. If p0>0, the assigned conditional vector q/p0 is a unit vector; if p0=0, then q=0 and no conditional vector exists.

Facts & Assumptions

Given: Finite registers A,S, the injection Jx=∣0A⟩⊗x, the projector Π0=JJ†, and a unit vector ϕ∈A⊗S.

[F1]

Clean-ancilla zero-branch postselection: q=J†ϕ, p0=∥q∥2, and p⊥=∥(I−Π0)ϕ∥2.

[F2]

Ordered tensor registers, their inner product, and wire permutations: the tensor-product inner product is the product inner product on elementary tensors.

[F3]

Dirac Notation Conversion, Linear isometries, and orthogonal or unitary operators on finite-dimensional inner product spaces, and Finite-dimensional linear isometries satisfy the adjoint identity: the bra is the adjoint functional for the root-linear-first pairing, and a norm-preserving linear map satisfies J†J=IS.

[F5]

The computational basis is orthonormal: the distinguished computational-basis vector ∣0A⟩ has norm 1.

Proof

Given: As in Facts & Assumptions.

1.1givenF2F3F5algebra

From the product inner product [F2] and unit norm [F5], ∥Jx∥2=⟨∣0A⟩⊗x,∣0A⟩⊗x⟩=∥x∥2 for every x, so J is an isometry by its definition in [F3]. Therefore J†J=IS by [F3]. It follows that Π0=JJ† is self-adjoint and idempotent, Π0ϕ=Jq, and ϕ=Π0ϕ+(I−Π0)ϕ.

2.1F1F4step 1.1algebra

The two summands are orthogonal because Π0(I−Π0)=0 and Π0†=Π0. Expanding their squared norm by [F4] gives 1=∥ϕ∥2=∥Π0ϕ∥2+∥(I−Π0)ϕ∥2=∥q∥2+p⊥=p0+p⊥. Both summands are nonnegative, so each lies in [0,1].

3.1F1F4step 2.1cases∎

If p0>0, then ∥q/p0∥2=p0/p0=1. If p0=0, definiteness of the inner-product norm gives q=0, so normalization is undefined and the definition correctly assigns no conditional vector.

DefinitionDefinition: Literature-sourcedProof: Not applicableOpen item page →

LCU decomposition and access model

Definition

Let S be a finite-dimensional complex-inner-product system space and let M=∑j=0m−1cjVj,cj∈C, where m≥1, each Vj:S→S is unitary, and α:=∑j=0m−1∣cj∣>0. The finite ordered list (cj,Vj)j<m is an LCU representation of M; it is part of the input, and a matrix description alone does not supply circuits for any Vj.

Set q=⌈log⁡2m⌉ and let Aidx be the q-qubit index register, with the one-dimensional q=0 case allowed. Its computational indices are 0,…,2q−1. For j<m put ωj=cj/∣cj∣ when cj≠0 and ωj=1 when cj=0. Define the complete SELECT unitary by Ssel(∣j⟩⊗x)={∣j⟩⊗ωjVjx,j<m,∣j⟩⊗x,m≤j<2q. This basis rule fixes every valid and padded index; it extends linearly to all superpositions and acts as identity on any separately declared work register.

A PREPARE access is a fully specified unitary P on the index register and all its declared work registers, with P∣0anc⟩=eiθ(∑j<m∣cj∣/α ∣j⟩)⊗∣0work⟩ for some phase θ∈R. The same full unitary, including its action on every nonzero input, determines P†; neither an inverse nor a controlled call is implied unless explicitly supplied. The allowed global phase cancels between P and P† in the LCU block. PREPARE work wires are included among the ancillas projected to zero.

For an amplitude-table implementation with D=2q entries, the dense fixed-width input length, table reading, arithmetic, and coefficient precision are separate costs. If the continuous-rotation QC24 procedure QC24 State Preparation is used, its source theorem QC24 State Preparation Rotation Level Upper Bound gives O((q+1)D) bounded-arity gates and serial depth, with at most max⁡{q−1,0} reusable clean work bits; the table and exact arithmetic costs remain separate. This bound is for continuous rotations. Finite-alphabet synthesis and finite-precision bit-time require separate guarantees. A supplied PREPARE circuit is instead charged by its declared full description, gates, wires, and generation cost.

The LCU block circuit is the complete unitary W:=P†SselP on all coefficient/work ancillas and the system. The access record separately lists PREPARE, PREPARE†, SELECT, SELECT†, and every controlled form used, together with their query, gate, wire, coefficient-bit, and classical-description costs under Hamiltonian simulation error and resources and Circuit-family and computation cost model. Unspecified access, inverse access, controlled access, and table loading have no free cost. The zero LCU representation is outside this access model because α=0 does not define a normalized coefficient state.

AlgorithmAlgorithm: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

LCU block encoding

Definition

Input: An LCU representation and complete PREPARE and SELECT access from LCU decomposition and access model, including their stated work-register and padded-index behavior. The access record must explicitly supply PREPARE† and its cost; an input without that inverse access is inadmissible.

Procedure: On the coefficient/work ancillas and system, apply PREPARE, then SELECT, then the supplied PREPARE†. Return the complete unitary W=P†SselP. Charge the three calls at their declared costs. No measurement or deterministic state-output guarantee is part of this procedure.

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Oblivious ancilla reflection

Definition

Let A be the full coefficient and PREPARE-work ancilla register in LCU decomposition and access model, let S be the system, and let J:S→A⊗S be the clean injection of Clean-ancilla zero-branch postselection. Set Π0:=JJ†; An isometric quantum encoding gives an orthogonal code projector makes this the orthogonal projection onto the image of J. Its basis formula is Π0=∣0A⟩⟨0A∣⊗IS, where ∣0A⟩ is unit by The computational basis is orthonormal and the tensor action is that of Tensor products of finitely many linear maps. Define R0:=I−2Π0. Thus R0 acts as −I on the clean-ancilla subspace and as +I on its orthogonal complement; it acts identically on the unknown system state. In the convention of Orthogonal-Projector Reflection and Access Contract, R0=SΠ0, while that item writes RΠ0=2Π0−I=−R0. These signs are distinct inside controlled blocks: if only the controlled RΠ0 is supplied, implement controlled R0 with the exact control-Z correction of Projector Reflections Are Hermitian Unitary Involutions and charge that gate. A projector-reflection call and its controlled form are explicit access operations, not free consequences of a state vector.

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Finite Taylor LCU polynomial

Definition

Let H=∑j=0m−1ajVj on a finite-dimensional complex-inner-product space, with m≥1, real aj and Hermitian unitaries Vj, and put Λ=∑j∣aj∣. For real h and integer K≥0, define MK(h):=∑k=0K(−ihH)kk!. Its ordered product-term LCU list has one empty tuple for k=0, with coefficient 1 and unitary I, and for each 1≤k≤K and ordered tuple (j1,…,jk) has coefficient ck,j:=(−i)khkaj1⋯ajkk! and unitary Vj1⋯Vjk. Distinct tuples remain distinct entries even if their product unitaries coincide. The absolute coefficient sum of this listed representation is sK(h):=∑k=0K(Λ∣h∣)kk!.

Whenever sK(h)≤2, append two distinct identity entries with coefficients +q and −q, where q=(2−sK(h))/2. They leave the represented matrix unchanged and raise the listed absolute coefficient sum to exactly 2. If q=0, the zero-weight entries may be retained with zero preparation amplitude or omitted. The polynomial, coefficient phases, ordered tuple index, and any numerical precision used to generate them are part of the finite input/access record; no term product, coefficient table, PREPARE circuit, or SELECT circuit is free.

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

LCU projected-block and postselection correctness

Statement

Let M=∑j<mcjVj be a finite LCU representation with α=∑j∣cj∣>0, let W be the unitary from LCU block encoding, and let Jx=∣0anc⟩⊗x include every coefficient and PREPARE-work ancilla. Then J†WJ=M/α. For every unit input vector ψ, the clean-ancilla zero-branch vector is Mψ/α and its postselection weight, under Clean-ancilla zero-branch postselection, is ∥Mψ∥2/α2. If this weight is positive, the conditional system vector is Mψ/∥Mψ∥; if it is zero, the conditional vector is undefined.

Facts & Assumptions

Given: The finite LCU representation, complete PREPARE and SELECT access, explicitly supplied PREPARE† access with its declared cost, and a unit system vector ψ.

[L1]

LCU block encoding: W=P†SselP, where PREPARE sends the all-zero ancilla to the coefficient-amplitude state, up to one global phase, and SELECT applies each coefficient phase with its unitary term.

[F1]

Dirac Notation Conversion: the bra is the adjoint functional, and ∣x⟩⟨y∣ acts by z↦⟨z,y⟩rootx.

[F2]

Finite registers and their ordered computational bases: the index register has the declared ordered computational basis, including its one-dimensional zero-qubit case.

[F3]

Clean-ancilla zero-branch postselection: the zero-branch vector of ϕ is J†ϕ and its weight is the squared norm of that vector.

[F4]

Clean-ancilla postselection has normalized branch weights: the zero and complementary branch weights of a unit vector are in [0,1] and sum to one; a normalized conditional vector exists only when the corresponding weight is positive.

[F5]

The computational basis is orthonormal: the computational index basis, including the zero-qubit register's basis, is orthonormal.

Proof

Given: As in Facts & Assumptions.

1.1givenL1F1F2F5algebra

Write βj=∣cj∣/α and ωj=cj/∣cj∣ for cj≠0 (take ωj=1 when cj=0). PREPARE gives eiθ∑j<mβj∣j,0work⟩ from the all-zero ancilla. After SELECT, the jth component is eiθβj∣j,0work⟩⊗ωjVjψ. The all-zero matrix element of PREPARE† on ∣j,0work⟩ is e−iθβj by [F1], so the global phase cancels and J†WJψ=∑jβj2ωjVjψ=Mψ/α. Padded invalid indices have zero prepared amplitude. Since this holds for every ψ, it proves the block identity; orthonormality [F5] validates these basis matrix elements.

2.1givenF3F4step 1.1algebra

Apply [F3] to ϕ=WJψ: its zero-branch vector is J†WJψ=Mψ/α. Its branch weight is therefore ∥Mψ∥2/α2, and [F4] makes this a valid probability in [0,1].

3.1F3F4step 2.1cases∎

If ∥Mψ∥>0, division of the branch vector by its norm gives Mψ/∥Mψ∥. If Mψ=0, the weight is zero and [F4] forbids assigning a normalized conditional vector to that branch.

AlgorithmAlgorithm: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

One-step oblivious amplitude amplification

Definition

Input: A unitary W on coefficient/work ancillas and system, explicit inverse access to W, the reflection R0=I−2Π0 of Oblivious ancilla reflection, and an operator M satisfying J†WJ=M/2 for the clean injection Jx=∣0anc⟩⊗x.

Procedure: Chronologically apply W, then R0, then W†, then R0, then W. The resulting unitary is C:=−WR0W†R0W. The scalar −1 is an explicit phase correction; it is not omitted when this procedure is controlled. The circuit uses two W calls, one W† call, and two reflection calls. A controlled invocation requires the declared controlled forward, inverse, reflection, and phase-correction gates. This procedure makes no measurement and asserts no exact ancilla cleanup for a nonunitary or approximate M; the robust clean-input guarantee is proved separately.

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

Taylor simulation tail bound

Statement

Let H=∑j<majVj be a finite-dimensional self-adjoint Hamiltonian on a nonzero complex-inner-product space, with m≥1, written with real aj and Hermitian unitaries Vj, and set Λ=∑j∣aj∣. For real h, integer K≥0, and x=Λ∣h∣, ∥MK(h)−e−ihH∥≤exxK+1(K+1)!. The Taylor coefficient sum sK=∑k=0Kxk/k! satisfies sK≤ex≤2 when x≤ln⁡2. If 0<η<1 and x≤ln⁡2, there is a K=O ⁣(1+log⁡(1/η)log⁡log⁡(e+1/η)) for which the right-hand side is at most η.

Facts & Assumptions

Given: A nonzero finite-dimensional complex-inner-product space, the stated self-adjoint decomposition, real h, and K≥0.

[L1]

Finite Taylor LCU polynomial: MK(h)=∑k=0K(−ihH)k/k!, and Λ=∑j∣aj∣.

[L2]

Finite Hermitian matrix dynamics: e−ihH is defined by the finite spectral functional calculus on the eigenvalues of H.

[F3]

The inner-product norm is definite, homogeneous, and satisfies the triangle inequality: the induced inner-product norm is homogeneous and satisfies the triangle inequality.

[F4]
[F7]

The natural logarithm as the inverse of the exponential function and The exponential function is strictly increasing: exp⁡(ln⁡2)=2, and x≤ln⁡2 implies ex≤2.

Proof

Given: As in Facts & Assumptions.

1.1givenL1L2F1F2F3F4algebra

By [F1], choose an orthonormal eigenbasis of H with eigenvalues λ. For a unit eigenvector uλ, ∣λ∣=∥Huλ∥=∥∑jajVjuλ∥≤∑j∣aj∣∥Vjuλ∥=Λ by [F2]–[F4]. On this basis, [L1] and [L2] make the difference diagonal with scalar entries ∑k=0K(−ihλ)k/k!−exp⁡(−ihλ).

1.2givenL1F5F7algebra

Since x≥0, the finite Taylor coefficient sum obeys 0≤sK=∑k=0Kxk/k!≤∑k=0∞xk/k!=ex by [L1] and [F5]. If x≤ln⁡2, [F7] gives sK≤ex≤2.

2.1step 1.1F4F5F6algebra

Put n=K+1. The absolute scalar-series tail at eigenvalue λ is at most ∑k=n∞(∣h∣∣λ∣)k/k!. Writing k=n+j and using (n+j)!≥n!j! bounds this by (∣h∣∣λ∣)n/n!∑j=0∞(∣h∣∣λ∣)j/j!=e∣h∣∣λ∣(∣h∣∣λ∣)n/n! by [F5]–[F6]. If the diagonal error has entries di in the eigenbasis and z=∑iziui, then its squared output norm is ∑i∣di∣2∣zi∣2≤(max⁡i∣di∣)2∥z∥2; an eigenbasis vector attaining the finite maximum gives equality in the operator norm [F4]. Hence the operator norm is max⁡i∣di∣. Since 0≤∣h∣∣λi∣≤x, the monotonicity of eu and un on nonnegative reals gives e∣h∣∣λi∣∣h∣n∣λi∣n/n!≤exxn/n!. Taking the maximum proves the stated tail bound.

3.1givenstep 2.1F5F7algebra∎

Assume x≤ln⁡2, so ex≤2 by [F7]. Let L=ln⁡(1/η). The exponential series gives 2<e<3: its first two terms sum to 2, while 1/k!≤2−(k−1) for k≥2, with strict inequality for k≥3, so the remaining sum is less than 1. Thus exp⁡(0)=1<2<exp⁡(1)=e by [F5], and the strict increase and inverse relation in [F7] give 0<ln⁡2<1. If L<1, n=10 gives 2x10/10!<2/10!<1/3<e−1<η. If L≥1, take n=⌈16eL/ln⁡(e+L)⌉. For z≥1, g(z)=z−ln⁡z has derivative g′(z)=(z−2)/(2z), so it decreases up to z=4 and increases thereafter. Since e>2, exp⁡(2)=e2>4, hence ln⁡4<2 by [F7]; thus g(z)≥g(4)=2−ln⁡4>0. Also e+L<4L. Therefore ne≥16Lln⁡(e+L)≥16Le+L>4e+L, so ln⁡(n/e)>12ln⁡(e+L). Hence nln⁡(n/e)≥8eL≥L+ln⁡4, using e>2, L≥1, and ln⁡4<2. For the factorial estimate, monotonicity of ln⁡s gives ln⁡(n!)=∑k=1nln⁡k≥∫1nln⁡s ds=nln⁡n−n+1, so n!≥(n/e)n≥eL+ln⁡4=4/η. Since exxn/n!≤2/n!, this choice gives a tail at most η/2≤η. In both cases n=O(1+L/ln⁡(e+L)). Since ln⁡(e+eL)≤L+ln⁡2≤e+L, this is also O(1+L/ln⁡ln⁡(e+eL)), the displayed order after changing logarithm bases. Hence K=n−1 has the stated order.

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

Oblivious-amplification block identity

Statement

Let J:S→A⊗S be the clean-ancilla isometry Jx=∣0anc⟩⊗x, let Π=JJ† be the orthogonal projection onto its image, and set R0=I−2Π. Let W be unitary and assume J†WJ=M/2. For the chronological circuit WR0W†R0W, J†WR0W†R0WJ=−32M+12MM†M. If M is unitary, this projected block is −M. The identity alone gives no bound on leakage outside the clean-ancilla subspace for a nonunitary M.

Facts & Assumptions

Given: A finite-dimensional system, a unitary W, the clean-ancilla isometry J, and J†WJ=M/2.

[L1]

One-step oblivious amplitude amplification: the chronological operation is W, R0, W†, R0, W, and R0=I−2Π.

[F1]

Oblivious ancilla reflection: Π projects only the full clean coefficient/work ancilla to zero and is identity on the system.

[F2]

Projector Reflections Are Hermitian Unitary Involutions: for an orthogonal projection Π, I−2Π is a unitary involution; the opposite sign is distinct under control.

[F3]

Linear isometries, and orthogonal or unitary operators on finite-dimensional inner product spaces and Finite-dimensional linear isometries satisfy the adjoint identity: a unitary is a surjective linear isometry, so W†W=WW†=I; an isometry J satisfies J†J=I.

[F4]

An isometric quantum encoding gives an orthogonal code projector: JJ† is the orthogonal projection onto the image of an isometry J.

[F5]

Finite registers and their ordered computational bases, The computational basis is orthonormal, and Ordered tensor registers, their inner product, and wire permutations: the clean basis vector is unit and the product inner product makes Jx=∣0anc⟩⊗x a linear isometry.

Proof

Given: As in Facts & Assumptions.

1.1givenL1F1F2F3F4F5algebra

Let B=J†WJ=M/2. By [F3]–[F5], J†J=I and Π=JJ† is the stated orthogonal projection; hence J†Π=J† and ΠJ=J. By [L1]–[F2], the three-use circuit is the stated unitary circuit with two reflections.

2.1givenF1step 1.1F3F4algebra

Expand (I−2Π) twice inside J†W(I−2Π)W†(I−2Π)WJ. Since W is unitary, [F3] gives WW†=W†W=I, and [F4] gives Π2=Π. The four terms compress to B−2B−2B+4BB†B=−3B+4BB†B: respectively, use W†W=I, ΠJ=J, J†Π=J†, and Π=JJ†. Substituting B=M/2 gives −32M+12MM†M.

3.1givenstep 2.1cases∎

If M is unitary, then MM†M=M, so the block is −M. For arbitrary M, step 2.1 proves only the compression identity; it contains no estimate for (I−Π)WR0W†R0WJ.

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

Robust one-step OAA including ancilla leakage

Statement

Let S be a nonzero finite-dimensional system space, let A be the complete clean-ancilla register, let Jx=∣0A⟩⊗x, and put Π=JJ†, R0=I−2Π. Suppose W is unitary, J†WJ=M/2, V:S→S is unitary, and ∥M−V∥≤η≤1/100. For C:=−WR0W†R0W one has the clean-input isometry bound ∥CJ−JV∥≤10η. The estimate includes residual ancilla leakage; it does not assert exact cleanup for nonzero η.

Facts & Assumptions

Given: The finite system and ancilla spaces, the stated block condition, unitaries W,V, and 0≤η≤1/100.

[L1]

One-step oblivious amplitude amplification fixes the chronological circuit −WR0W†R0W.

[F1]

Oblivious-amplification block identity: J†WR0W†R0WJ=−3M/2+MM†M/2 when J†WJ=M/2.

[F2]

Oblivious ancilla reflection: R0=I−2Π for the projector Π onto the entire clean coefficient/work ancilla, tensored with identity on the system.

[F3]
[F5]

The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: the operator norm is the maximum norm on unit vectors.

[F7]

The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: ∥AB∥≤∥A∥∥B∥ and ∥T†T∥=∥T∥2.

[F8]

Ordered tensor registers, their inner product, and wire permutations and Tensor products of finitely many linear maps: product registers have the product inner product and T⊗I acts factorwise.

[F9]

Every finite-dimensional real or complex inner product space has an orthonormal basis: every finite-dimensional inner-product space has an orthonormal basis.

[F10]

Finite registers and their ordered computational bases and The computational basis is orthonormal: the distinguished clean register vector is a unit vector. Together with [F8], the clean injection Jx=∣0A⟩⊗x is an isometry; Finite-dimensional linear isometries satisfy the adjoint identity gives J†J=I, and An isometric quantum encoding gives an orthogonal code projector makes Π=JJ† the orthogonal projection onto its image.

Proof

Given: As in Facts & Assumptions.

1.1givenL1F1F2F3F4F5F6F10algebra

By [F10], J†J=I and Π is an orthogonal projection. By the circuit order in [L1], the factors in C are unitary by [F2]–[F4], so CJ is an isometry. Put D:=J†CJ. By [F1] and the minus sign in C, D=3M/2−MM†M/2. Write E=M−V. For every unit vector x, ∥Mx∥≤∥Vx∥+∥Ex∥≤1+η, so ∥M∥≤1+η by [F5]. For each endomorphism T of the nonzero space S, Cauchy–Schwarz gives ∥Tx∥=max⁡∥y∥=1∣⟨Tx,y⟩∣ (with Tx=0 immediate), hence ∥T∥=max⁡∥x∥=∥y∥=1∣⟨Tx,y⟩∣. The adjoint identity interchanges the unit vectors, so ∥T†∥=∥T∥; in particular ∥M†∥≤1+η and ∥E†∥=∥E∥≤η.

2.1givenF4F5F6F7step 1.1algebra

Since VV†V=V, MM†M−V=(M−V)M†M+V(M†−V†)M+VV†(M−V). For every unit vector, apply the vector triangle inequality [F6] and [F7] to these three terms; using ∥E†∥=∥E∥≤η and ∥V∥=∥V†∥=1 from [F4] gives ∥MM†M−V∥≤η((1+η)2+(1+η)+1). Maximizing over unit vectors by [F5], ∥D−V∥≤32η+12η((1+η)2+(1+η)+1)≤4η for η≤1/100.

2.2givenF2F3F4F7F10step 1.1algebra

Let L=(I−Π)CJ. Since Π=JJ† and J†J=I by [F10], CJ=JD+L and J†L=J†(I−JJ†)CJ=J†CJ−(J†J)J†CJ=0. Hence both cross terms vanish in (CJ)†CJ=(JD+L)†(JD+L), and D†D+L†L=(CJ)†CJ=I because C and J are isometries. Thus L†L=I−D†D and ∥L∥2=∥I−D†D∥ by [F7].

3.1givenF4F5F6F7step 2.1step 2.2algebra

Set δ=∥D−V∥≤4η by step 2.1. For every unit vector x, ∥Dx∥≤∥Vx∥+∥(D−V)x∥≤1+δ, so ∥D∥≤1+δ. Since V†V=I, I−D†D=(V−D)†D+V†(V−D); the vector triangle inequality [F6] and [F7] therefore give ∥(I−D†D)x∥≤δ∥D∥+δ≤δ(2+δ) for unit x. Maximizing by [F5], ∥I−D†D∥≤δ(2+δ)≤4η(2+4η)≤9η. Thus ∥L∥≤3η.

4.1F4F5F6F10step 2.1step 3.1algebra

From CJ−JV=J(D−V)+L, for every unit x the vector triangle inequality [F6] and the isometry of J in [F10] give ∥(CJ−JV)x∥≤∥D−V∥+∥L∥≤4η+3η. Maximizing by [F5] and using η≤1/100 yields ∥CJ−JV∥≤10η.

5.1F5F8F9F10step 4.1algebra∎

For any finite reference space R, choose an orthonormal basis (ej) by [F9] and write ξ=∑jxj⊗ej. By [F8], ∥(CJ−JV)⊗IR ξ∥2=∑j∥(CJ−JV)xj∥2≤(10η)2∑j∥xj∥2=(10η)2∥ξ∥2. Thus the same clean-input isometry bound holds with any finite reference, including entangled inputs.

AlgorithmAlgorithm: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Segmented Taylor LCU simulator

Definition

Inputs: A finite list H=∑j=0m−1ajVj on a nonzero finite-dimensional complex system space, where m≥1, aj∈R and each Vj is a supplied Hermitian unitary; real time t and a tolerance 0<ϵ≤1, each with a finite exact description. The description of ϵ supplies arbitrarily tight rational interval enclosures. Also supply the complete PREPARE/SELECT, inverse, and cost interfaces from LCU decomposition and access model for the Taylor list selected below. For the certified procedure the real descriptions supply arbitrarily tight rational interval enclosures for S=Λ∣t∣ and S/ln⁡2, where Λ=∑j∣aj∣. Exact coefficient phases and the required unitary/circuit access for the selected list are separate supplied data; interval names alone do not provide them. Arithmetic, refinement, description and generation costs are charged separately. No efficient classical compiler is asserted.

For the indexed term-query implementation, additionally supply the complete unitary T(∣j⟩⊗x)=∣j⟩⊗Vjx for j<m, acting as identity at padded term indices, together with every controlled or inverse form actually used and its separate costs. A call to this coherent multiplexer is one indexed term query. Individual accesses to the separate Vj do not grant this operation: when it is assembled from individual controlled Vj, each multiplexed call uses m equality-controlled individual calls, and all label tests, routing, phase gates and work are charged. With an independently supplied Taylor SELECT, only its declared internal resource record applies.

Procedure:

  1. Refine an enclosure [ϵ−,ϵ+] of ϵ until 0<ϵ− and ϵ+≤2ϵ−. Refine an enclosure [S−,S+] of S=Λ∣t∣, intersecting with the known nonnegative half-line, until its width is less than ϵ−/8. If S+≤ϵ−/4, return the exact identity circuit with no oracle calls and the certificate S≤ϵ/4. Otherwise proceed to the segmented branch; no positive lower bound on S is needed. These refinements terminate because ϵ>0 and the supplied descriptions give arbitrarily tight rational enclosures. No exact equality test on interval-described reals is used; every t=0 or Λ=0 input takes the identity branch.
  2. In the remaining branch put q=S/ln⁡2. In the ideal exact-arithmetic variant set r=max⁡(1,⌈q⌉). In the certified variant refine an enclosure [q−,q+] until its width is less than 1, then set r=max⁡(1,⌈q+⌉). Either choice has r≥1, Λ∣t∣/r≤ln⁡2, and r<q+2. Set h=t/r and η=ϵ2/(1600r2). All refinement and segment-count costs are recorded.
  3. Set η−:=ϵ−2/(1600r2), a positive rational lower bound for η. Set n=1 and successively double n until the exact rational comparison 4/n!≤η− holds; compute the factorial by finite integer multiplication and set K=n−1. This finite scheduler has no real-equality test or unbounded refinement at a fixed failed order. Since x=Λ∣h∣≤ln⁡2<1, the tail in Taylor simulation tail bound is at most 2/n!≤η−/2≤η/2. Its factorial estimate gives the stated logarithmic-over-double-logarithmic order, including the factor of at most two from doubling. Charge the factorial and coefficient arithmetic.
  4. Expand MK(h) into the ordered product-term LCU list of Finite Taylor LCU polynomial. The certificate Λ∣h∣≤ln⁡2 and Taylor simulation tail bound prove sK≤2 analytically. Add two distinct identity terms +qI,−qI with q=(2−sK)/2, retaining zero-weight padding labels if needed; the matrix is unchanged and the listed norm is exactly 2. Under the indexed term-query implementation, implement each Taylor SELECT using at most K controlled indexed term queries: process tuple positions from K down to 1, condition each call on a valid tuple having that position, and use its term label. Compute/uncompute those labels in charged work registers. This reverse execution order yields the displayed product Vj1⋯Vjk. Apply the coefficient phase separately and act as identity on padding and unused labels. The adjoint SELECT reverses this sequence and uses the declared inverse calls.
  5. For each of the r segments, allocate a fresh clean register containing the expanded-term index and every PREPARE work wire. Build W=P†SselP using the exact coefficient phases and the padded invalid-index behavior. Apply the phase-corrected chronological circuit W,R0,W†,R0,W, with R0=I−2Π0 on the complete clean coefficient/work register. Thus the segment unitary is −WR0W†R0W, with two reflection calls, two forward W calls, and one inverse call.
  6. Compose the r segment circuits in time order. Retain every segment's coefficient/work register in the output dilation; do not measure or claim exact cleanup. The output is the finite unitary/access-circuit description together with the chosen r,K, coefficient table or generator, and resource record.

The default procedure is uncontrolled. A controlled variant is defined only when controlled access to every component is supplied; the known minus sign is applied as a phase on the outer control, not discarded. If a compiled implementation is used, the per-call operator-norm error bounds δi must satisfy the certified sum ∑iδi≤δimpl≤ϵ/2. The exact-access variant has δimpl=0. Every PREPARE, SELECT, inverse, reflection, control, coefficient, table, precision, wire, and gate cost remains explicit.

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

Segmented Taylor error and resources

Statement

Let H=∑j<majVj be the finite Hermitian-unitary decomposition on a nonzero finite-dimensional complex-inner-product space in Segmented Taylor LCU simulator, let Λ=∑j∣aj∣, let t∈R, and let 0<ϵ≤1. For the segmented branch, let r be the segment count selected by that algorithm. It satisfies Λ∣t∣/r≤ln⁡2 and r<Λ∣t∣/ln⁡2+2; put η=ϵ2/(1600r2). Choose K by the factorial-doubling rule in that algorithm. Let Vimpl be its complete circuit or access-circuit unitary, including all work registers, and suppose the sum of its per-call operator-norm error bounds is at most ϵ/2. Let Jin append all initial work registers in zero, and let Jout append the same work registers in zero to an ideal output. Then ∥VimplJin−Joute−itH∥≤ϵ. For every finite reference space and every input density operator on system plus reference, tracing out the work registers gives output states ρimpl,ρideal satisfying D(ρimpl,ρideal):=12∥ρimpl−ρideal∥1≤ϵ. The certified small-time branch returns identity with error at most ϵ/3; it is exact when t=0 or Λ=0. No operator-norm bound on arbitrary nonzero initial work registers or exact cleanup of the output registers is claimed.

On the segmented branch the segment count is r, and the truncation order obeys K=O(1+log⁡(1/η)/log⁡log⁡(e+1/η)). With the explicitly supplied controlled indexed term multiplexer of the algorithm, the construction uses at most 3rK indexed term queries. If each such multiplexed call is instead assembled from the m individually supplied controlled Vj, the corresponding bound is 3rmK individual term calls; equality tests, routing, coefficient phases and their inverse/control operations remain separately charged. Independently supplied Taylor SELECT access has only its declared internal query/gate cost, with no inferred K-call implementation. It uses exactly 3r PREPARE calls, 3r PREPARE† calls, 2r SELECT calls, r SELECT† calls, and 2r reflection calls. If the expanded Taylor list has NK=∑k=0Kmk ordered terms and each segment keeps a fresh index register, the index width is ⌈log⁡2(NK+2)⌉ qubits and the r retained registers' total index width is r⌈log⁡2(NK+2)⌉. All PREPARE table, internal work, term gates, coefficient bits, circuit description, control, inverse, and classical generation costs are charged separately under Hamiltonian simulation error and resources.

Facts & Assumptions

Given: The exact finite term list and access model, the specified r,K,η, and a compiled unitary whose summed operator-norm error is at most ϵ/2.

[L1]

Segmented Taylor LCU simulator: its interval procedure obtains a rational lower bound ϵ− with ϵ/2≤ϵ−≤ϵ; the identity branch certifies Λ∣t∣≤ϵ/4, and the other branch chooses r and factorial doubling chooses K using η−:=ϵ−2/(1600r2), where η/4≤η−≤η. In the segmented branch, each segment uses fresh clean coefficient/work registers, pads the Taylor LCU to norm 2, applies −WR0W†R0W, retains all work registers, and has certified sum of per-call errors ∑iδi≤δimpl≤ϵ/2.

[F1]

Finite Taylor LCU polynomial: the Taylor expansion has listed coefficient norm sK=∑k=0K(Λ∣h∣)k/k! and the two padding coefficients cancel in the represented operator.

[F2]

Taylor simulation tail bound: ∥MK(h)−e−ihH∥≤eΛ∣h∣(Λ∣h∣)K+1/(K+1)!, ∥H∥≤Λ, and ln⁡2<1. Its proof supplies the scalar exponential series, the factorial estimate n!≥(n/e)n, and the factorial-order choice argument.

[F3]

LCU projected-block and postselection correctness: PREPARE/SELECT/PREPARE† has clean-ancilla block M/α for its listed LCU.

[F4]

Oblivious-amplification block identity: the projected block of WR0W†R0W is −3M/2+MM†M/2 when the input block is M/2.

[F5]

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

[F6]

Finite Hermitian matrix dynamics and Unitary and differential identities for finite matrix dynamics: UH(t)=e−itH is unitary and UH(t+s)=UH(t)UH(s).

[F7]

Hamiltonian simulation error and resources: operator-norm simulation error and query, gate, controlled/inverse, width, coefficient-bit, description, and classical-generation costs are distinct coordinates.

[F8]

Density operator: a density operator is a nonnegative trace-one endomorphism of a nonzero finite-dimensional complex-inner-product space.

[F10]

Partial trace over a finite tensor factor: the partial trace is the finite coordinate contraction over the discarded factor.

[F11]

Ordered tensor registers, their inner product, and wire permutations: tensor factors have the declared product inner product and ordered product basis.

[F12]

Pure-state trace distance is bounded by vector error: for unit vectors u,v, 12∥∣u⟩⟨u∣−∣v⟩⟨v∣∥1≤∥u−v∥.

[F13]

Partial trace contracts the trace norm on Hermitian operators: partial trace does not increase the trace norm of a Hermitian difference.

[F14]

The basis-independent trace of an endomorphism of a finite-dimensional vector space: trace is the sum of diagonal entries in any ordered basis.

[F15]

Every finite-dimensional real or complex inner product space has an orthonormal basis: a finite-dimensional inner-product reference space has an orthonormal basis.

Proof

Given: As in Facts & Assumptions.

1.1givenL1F1F2F6algebracases

On the identity branch let S=Λ∣t∣≤ϵ/4≤1/4<1. In an orthonormal eigenbasis as in [F2], each Hamiltonian eigenvalue satisfies ∣λ∣≤Λ. Its scalar-series error obeys ∣e−itλ−1∣≤∑k≥1Sk/k!≤∑k≥1Sk=S/(1−S)≤ϵ/(4−ϵ)≤ϵ/3. The diagonal norm calculation in [F2] proves ∥I−UH(t)∥≤ϵ/3, and the error is zero if t=0 or Λ=0. Otherwise step 2 chooses a finite r with x:=Λ∣t∣/r≤ln⁡2; no lower bound on S is needed. Hence sK≤ex≤2, and cancelling padding makes the norm exactly 2 without changing MK.

1.2givenL1F2F3F4F5

By [F3], the padded circuit has block MK(h)/2. The uncorrected amplified block is −3MK(h)/2+MK(h)MK(h)†MK(h)/2 by [F4], so the explicit phase correction in [L1] gives the corresponding opposite-sign block; [F5] supplies the needed full isometry estimate including leakage. The Taylor-tail rule gives ∥MK(h)−UH(h)∥≤η, and η≤1/1600<1/100. Apply [F5] with V=UH(h) to obtain a clean-input isometry error at most 10η=ϵ/(4r) for each segment, including any finite reference and all earlier retained registers.

1.3givenL1F2algebra

By [L1], the scheduler uses the positive rational η−:=ϵ−2/(1600r2), where η/4≤η−≤η. The loop terminates because n! tends to infinity. Since ϵ−≤ϵ≤1 and r≥1, η−≤1/1600; hence L−:=ln⁡(4/η−)>1. For N=⌈16eL−/ln⁡(e+L−)⌉, the inequalities ln⁡(e+L−)≤e+L− and e+L−<4L− give N/e>4e+L−, hence ln⁡(N/e)>12ln⁡(e+L−). Therefore Nln⁡(N/e)≥8eL−≥L−+ln⁡4. Monotonicity of ln⁡s gives ln⁡(N!)=∑k=1Nln⁡k≥∫1Nln⁡s ds=Nln⁡N−N+1, so N!≥(N/e)N≥4/η−. The least power of two at least N is less than 2N and already meets the factorial comparison, so the scheduler stops at some n<2N. Thus K=n−1=O(1+L−/ln⁡(e+L−)). Since η/4≤η−≤η and η≤1/1600, this is O(1+log⁡(1/η)/log⁡log⁡(e+1/η)). The rule uses only a finite integer loop, and its cost is a separate classical coordinate. For the error estimates, exxn/n!≤2/n!≤η−/2≤η/2 because x≤ln⁡2<1.

2.1step 1.2F6algebra

The ideal segment maps each input to the same system evolution UH(h) and a fresh zero register. Telescoping the r compositions, every intervening exact segment is an isometry and has norm 1, so the total access-circuit isometry error is at most rϵ/(4r)=ϵ/4. The ideal system factor is UH(h)r=UH(t) by [F6].

3.1givenL1step 2.1algebra

Replacing the exact access calls by their compiled unitaries changes the full circuit in operator norm by at most the sum of their per-call errors: expand the product difference as a telescoping sum and use norm 1 for each unitary factor. The declared sum is at most ϵ/2 by [L1]. The triangle inequality therefore gives ∥VimplJin−JoutUH(t)∥≤ϵ/4+ϵ/2=3ϵ/4≤ϵ.

4.1step 1.1step 3.1F10F11F12F13F15

First take a pure unit input, including any finite reference. The segmented isometry bound in step 3.1, or the identity-branch bound in step 1.1 tensored with that reference, gives vector error at most ϵ. For the identity branch, write a system-reference vector as ∑jxj⊗fj in an orthonormal reference basis [F15]; [F11] makes its squared error the sum ∑j∥(I−UH(t))xj∥2≤(ϵ/3)2∑j∥xj∥2, justifying that extension explicitly. Then [F12] bounds the joint pure-state trace distance by ϵ. The difference of the two joint density operators is Hermitian, so applying partial-trace contractivity [F13] over all work registers preserves this bound.

5.1givenF8F9F10F11F13F14F15step 4.1algebra

For a general input density operator, [F8]–[F9] give an orthonormal eigenbasis (ej) with nonnegative eigenvalues λj; [F14] and trace one imply ∑jλj=1. Take an auxiliary reference space with orthonormal basis (fj), which exists by [F15], and set Ψ=∑jλj ej⊗fj. By the product inner product [F11], ∥Ψ∥2=∑jλj=1. In the product basis, its projector has entries X(i,j),(i′,j′)=λiλi′ 1i=j1i′=j′. The coordinate rule [F10] gives (Tr⁡refX)ii′=∑jX(i,j),(i′,j), which is λi if i=i′ and 0 otherwise, exactly the matrix of ρ. Thus Ψ purifies ρ. Apply step 4.1 with the original reference and this purifying reference, then trace out the work registers and the purifying register; [F13] again preserves the bound. Therefore the reduced outputs for ρ differ in trace distance by at most ϵ.

6.1L1F1F7givenalgebra∎

In each segment the chronological OAA circuit has two forward W factors, one W† factor, and two reflections [L1]. Expanding each W=P†SselP gives three PREPARE and three PREPARE† calls, two SELECT and one SELECT† call per segment. In the indexed term-query implementation [L1], a length-k tuple acts by processing its labels in reverse order, so its K conditionally active positions implement exactly Vj1⋯Vjk. The phase and invalid-label rules are separately specified. Thus each SELECT or its adjoint uses at most K controlled indexed term calls and the total is at most 3rK. If only individual controlled Vj are used to assemble a multiplexer, apply the jth unitary conditioned on label equality, for every j<m: exactly one block is active on each valid label, so this implements the same direct sum with at most m individual calls per indexed call. This gives 3rmK individual calls with the declared label/routing/phase costs; no such decomposition bound is inferred for an arbitrary supplied SELECT. The expanded coefficient list has NK=∑k=0Kmk ordered terms and two retained padding labels, giving the stated widths. Internal work and all other resource coordinates are charged as in [F7]. The identity branch has no queries or retained segment registers.

5 · Examples, counterexamples and false statements

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