Alphabeta Math
Pipeline-generated
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

✓ 9 results · all verified · 6 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 3 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Quantum Phase Estimation

1 · Prerequisites

2 · Summary

This page fixes phase modulo one, the big-endian phase-register convention, circular distance, and the separate controlled-power and repeated-controlled-unitary access models. It defines forward quantum phase estimation using controlled positive powers followed by the inverse of the positive-sign binary QFT, with an explicit Lüders projective readout. The finite outcome kernel gives the exact-grid law, the nearest-grid probability bound, and the circular tail estimate. It also records exact query and ideal gate counts, plus the eigenspace-weighted output law and conditional instrument for superpositions. Coherent uncomputation requires separately supplied and charged controlled inverse powers.

3 · Logical flowchart

4 · Definitions, theorems and proofs

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

Finite geometric sum for phase-estimation amplitudes

Statement

For n∈N and z∈C, define Sn(z):=∑j=0n−1zj, with the empty sum S0(z)=0. Then Sn(z)={ιC(n),z=1,(1−zn)/(1−z),z≠1, where ιC is the canonical natural embedding of N into C.

For Q=2m, θ∈[0,1), and integer y, setting z=exp⁡(2πi(θ−y/Q)) gives zQ=exp⁡(2πiQθ); moreover z=1 exactly when Qθ−y∈QZ.

Facts & Assumptions

Given: n∈N, z∈C, and, for the specialization, m≥1, Q=2m, θ∈[0,1), and y∈Z.

[F1]

A finite sum is independent of its enumeration and splits over a disjoint union; for the initial segment {0,…,n}, this gives Sn+1(z)=Sn(z)+zn (A finite sum in a commutative monoid indexed by an arbitrary finite set, Finite commutative-monoid sums are invariant under bijective reindexing, split over disjoint unions, and satisfy the finite Fubini rule).

[F2]

Complex powers satisfy z0=1 and zn+1=znz (Integer powers in the complex field).

[F3]

The complex numbers form a field, so multiplication distributes over addition and every nonzero element has an inverse (C=R[x]/(x2+1) is a field, every element is uniquely a+bi, and every nonzero element has inverse (a−bi)/(a2+b2)).

[F4]

The canonical natural is defined recursively by ιC(0)=0, ιC(n+1)=ιC(n)+1 (The canonical natural ι(n)=n⋅1F of a field).

[F5]

Proof

1.1F1F2basealgebra

At n=0, the sum is empty, so (1−z)S0(z)=0=1−z0 by [F1] and [F2].

1.2givenih

Fix n∈N and assume (1−z)Sn(z)=1−zn; this is the induction hypothesis.

2.1F1F2F3F5step 1.2discharge-induction

By [F1] and [F2], (1−z)Sn+1=(1−z)Sn+(1−z)zn=(1−zn)+(zn−zn+1)=1−zn+1, using distributivity in [F3] and the induction hypothesis [step 1.2]. Thus [F5] proves (1−z)Sn(z)=1−zn for every n.

3.1F1F3F4F5step 2.1

If z=1, every summand is 1, so [F1] gives S0(1)=0 and Sn+1(1)=Sn(1)+1. At n=0, this equals ιC(0)=0 by [F4]; if Sn(1)=ιC(n), the two successor recursions give Sn+1(1)=Sn(1)+1=ιC(n)+1=ιC(n+1). Induction [F5] proves Sn(1)=ιC(n) for every n. If z≠1, then 1−z≠0, so divide the identity from step 2.1 by 1−z in the field [F3] to obtain Sn(z)=(1−zn)/(1−z).

4.1F2F6step 2.1discharge-induction∎

In the QPE specialization, repeated use of [F6] and the power recursion [F2] gives zQ=exp⁡(2πiQ(θ−y/Q))=exp⁡(2πi(Qθ−y)). Since y∈Z, [F6] gives exp⁡(−2πiy)=1, hence zQ=exp⁡(2πiQθ). The same kernel statement gives z=1 exactly when 2πi(θ−y/Q)∈2πiZ, equivalently θ−y/Q∈Z, or Qθ−y∈QZ.

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

Sine chord and linear bounds

Statement

For 0≤u≤1/2 and v≥0, sin⁡(πu)≥2u,sin⁡(v)≤v.

Facts & Assumptions

Given: u∈[0,1/2], v≥0, and π as defined by Pi as twice the smallest positive zero of cosine.

[F2]

sin⁡′=cos⁡, and cos⁡ is nonincreasing on [0,π/2] (The derivatives of sine and cosine are cosine and minus sine, Signs, monotonicity intervals, and ranges of sine and cosine).

[F6]

Sine is 1-Lipschitz on R: ∣sin⁡a−sin⁡b∣≤∣a−b∣ (Sine and cosine are 1-Lipschitz on R).

Proof

1.1givenF1F3F4construct

Define g(t)=sin⁡t−2t/π on [0,π/2]. By [F1], [F3], and [F4], g(0)=g(π/2)=0 and g′(t)=cos⁡t−2/π on the interior.

2.1F2step 1.1

By [F2], g′ is nonincreasing: if s<t, then cos⁡s≥cos⁡t, so g′(s)≥g′(t).

3.1F1F5step 2.1choosealgebra

Fix t∈(0,π/2). Apply [F5] on [0,t] and [t,π/2] to obtain a∈(0,t) and b∈(t,π/2) with g(t)=tg′(a) and −g(t)=(π/2−t)g′(b). Since a<b, step 2.1 gives g′(a)≥g′(b). Dividing by the positive numbers t and π/2−t, then multiplying by their product, gives g(t)π/2≥0; as π>0, g(t)≥0.

4.1F1step 3.1algebra

If 0<u<1/2, take t=πu∈(0,π/2) in step 3.1 to get sin⁡(πu)≥2u. At u=0 and u=1/2, the same inequality is equality by [F1].

5.1F1F6step 4.1algebra∎

For v≥0, [F6] and sin⁡0=0 give sin⁡v=sin⁡v−sin⁡0≤∣sin⁡v−sin⁡0∣≤v. Thus both stated inequalities hold.

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

Circular phase distance modulo one

Definition

For a real x∈[−1,1], define its circular distance to the nearest integer by dT(x):=min⁡{∣x∣, 1−∣x∣}. Here ∣x∣ is the real absolute value and min⁡ is the minimum of the displayed finite set (Order on the reals, Maximum and minimum of a set); existence of this finite minimum follows from Every nonempty finite set of reals has a maximum and a minimum. Both entries lie in [0,1], so the value lies in [0,1/2]. If ∣x∣≤1/2, the nearest integer is 0; if x>1/2, it is 1; if x<−1/2, it is −1, so the formula is the distance to the nearest integer. For phases θ,ϕ∈[0,1), their circular distance is dT(θ−ϕ); the difference belongs to (−1,1), the stated domain.

DefinitionDefinition: Literature-sourcedProof: Not applicableOpen item page →

Eigenphase estimation access model

Definition

Let H be a finite-dimensional complex inner-product space, U:H→H a unitary, and ψ∈H a unit vector with a promised phase θ∈[0,1) such that Uψ=exp⁡(2πiθ)ψ. For an integer m≥1, let Q=2m. The phase register is (C2)⊗m with ordered computational basis indexed big-endian by x=∑k=0m−1bk2m−1−k, where bk∈{0,1} (Finite registers and their ordered computational bases, Big-endian encoding of ordered bit strings). Thus the wire of weight 2j is qm−1−j.

There are distinct forward-access options:

  1. Controlled-power access. The model explicitly supplies one controlled U2j operation for each j=0,…,m−1. Each use is counted as one controlled-power query; an implementation's ordinary gate cost is recorded separately.
  2. Single controlled-unitary access. The model supplies calls to C(U)=diag⁡(IH,U) in the control-first order of Controlled Unitary Access. A controlled U2j may be implemented by 2j sequential calls to C(U); those calls are charged individually.

Optional controlled inverse-power access is a separate capability that explicitly supplies C(U−2j) for each required j. It is not implied by either forward option. It is needed only by a consuming procedure that reverses the controlled-power portion of a coherent QPE prefix. Reversing the inverse-QFT gate list does not use U−1.

Finite ordered-register syntax is specified by Finite circuit syntax and ordered wire labels, gate action by Denotational semantics of a finite layered circuit, and projective measurement outcomes by Finite projective measurements. The definition is an access-model promise: it does not assert that the supplied controlled powers have an efficient construction or that θ has an exact m-bit expansion.

AlgorithmAlgorithm: Literature-sourcedProof: Not applicableOpen item page →

Quantum phase estimation

Definition

Input. An integer m≥1; an m-qubit phase register; a finite-dimensional system register H; a unitary U:H→H; a unit eigenvector ψ with promised phase θ∈[0,1); and one of the forward access capabilities in Eigenphase estimation access model.

Output. A classical label y∈{0,…,Q−1} and its phase estimate y/Q, where Q=2m.

Instructions.

  1. Initialize the phase register in ∣0m⟩ and the system register in ψ.
  2. Apply the Hadamard H=(X+Z)/2 of Exact one-qubit gates and rotations to each phase wire. This prepares Q−1/2∑x=0Q−1∣x⟩⊗ψ in the declared big-endian order.
  3. For j=0,…,m−1, apply the supplied controlled U2j to control wire qm−1−j and the system. Under single controlled-unitary access, implement that operation with 2j sequential calls to C(U), as specified by the access model.
  4. Apply the inverse of the positive-sign exact binary QFT circuit of Positive-sign cyclic quantum Fourier transform and Exact binary QFT circuit correctness. Construct it by reversing the chronological gate list and replacing each gate by its adjoint, including the final swaps and negating controlled-phase angles.
  5. Perform the computational-basis projective measurement on the phase register. Its specified Lüders instrument has branch map Z↦(∣y⟩⟨y∣⊗IH)Z(∣y⟩⟨y∣⊗IH) for each label y (Finite projective measurements, Finite instruments and conditional states). Output the observed label y and the ratio y/Q.

The inverse in step 4 is the inverse QFT, not U−1; a forward QPE run makes no controlled inverse-unitary query. The coherent prefix is the circuit through step 4 before measurement. A consumer that coherently uncomputes this prefix needs the separate controlled inverse-power capability from Eigenphase estimation access model.

Remarks

For the instrument clause in step 5, let Hjoint=(C2)⊗m⊗H and Py=∣y⟩⟨y∣⊗IH. Tensor products of these operators act factorwise on elementary tensors by Tensor products of finitely many linear maps. The phase-register basis projectors are self-adjoint idempotents, mutually orthogonal and complete, so (Py)y=0Q−1 is a projective measurement on Hjoint (Finite projective measurements, Finite registers and their ordered computational bases, Ordered tensor registers, their inner product, and wire permutations). The space Hjoint is nonzero and finite-dimensional because m≥1 and the input includes a unit vector in finite-dimensional H (Eigenphase estimation access model). For every v∈Hjoint, ⟨Pyv,v⟩=⟨Py2v,v⟩=⟨Pyv,Pyv⟩≥0. Thus each Py is nonnegative (Non-negative and positive operators) and the complete family is a finite POVM (Finite POVMs and their outcome law). Since Py is itself nonnegative and Py2=Py, uniqueness of the nonnegative square root gives Py1/2=Py (A non-negative operator has a unique non-negative square root). Applying The square-root Lüders maps form an instrument to this POVM therefore proves that the branch maps Z↦PyZPy specified in step 5 form a valid finite instrument.

LemmaStatement: Literature-sourcedProof: AI-adaptedprecheck passOpen item page →

Phase-estimation finite outcome law

Statement

Let m≥1, Q=2m, and let the input be an eigenvector with Uψ=exp⁡(2πiθ)ψ, where θ∈[0,1). For each y∈{0,…,Q−1}, the probability of measuring y in the algorithm of Quantum phase estimation is Pθ(y)=Q−2∣∑j=0Q−1exp⁡ ⁣(2πij(θ−y/Q))∣2. Thus the finite outcome kernel is defined at every phase, including Qθ∈Z; no quotient with a zero denominator is needed in this formula.

Facts & Assumptions

Given: The eigenphase input and access model of Eigenphase estimation access model, the algorithm of Quantum phase estimation, and m≥1.

[F1]

The phase convention is Uψ=exp⁡(2πiθ)ψ, and the phase-register basis is big-endian with the wire of weight 2j at position m−1−j (Eigenphase estimation access model, Big-endian encoding of ordered bit strings).

[F2]

The algorithm prepares Q−1/2∑x∣x⟩⊗ψ, applies controlled U2j on the wire of weight 2j, then applies the inverse of the positive-sign binary QFT and measures the phase register (Quantum phase estimation).

[F9]

The denotation of chronological layers L1,…,Ld is ULd⋯UL1, with each gate embedded on its declared wires (Denotational semantics of a finite layered circuit).

[F3]

The positive-sign cyclic QFT has matrix action ∣x⟩↦Q−1/2∑ye2πixy/Q∣y⟩; the exact binary circuit implements it in the declared order (Positive-sign cyclic quantum Fourier transform, Exact binary QFT circuit correctness).

[F4]

On the phase register, Py=∣y⟩⟨y∣⊗IH is one member of the finite orthogonal resolution of the identity; the outcome weight of a density operator ρ is tr⁡(ρPy) (Finite registers and their ordered computational bases, Ordered tensor registers, their inner product, and wire permutations, Tensor products of finitely many linear maps, Finite projective measurements, Projective measurement outcome weights are probabilities, Finite POVMs and their outcome law).

[F5]

The Dirac convention is linear in the first argument of the root inner product, so ∣v⟩⟨w∣ acts by z↦⟨z,w⟩rootv, has coordinate matrix [v][w]†, and has trace ⟨v,w⟩root (Dirac Notation Conversion, Dirac Notation Coordinate Identity, The basis-independent trace of an endomorphism of a finite-dimensional vector space, The trace tr⁡(A) as the sum of the diagonal entries). A normalized nonzero vector defines the density operator ∣v⟩⟨v∣ by Ray-to-projector is well-defined and bijective.

[F6]

The finite cyclic character sum is Q at frequency 0 mod Q and 0 otherwise (Orthogonality of the characters x↦e2πikx/N on Z/NZ).

[F8]

Trace is cyclic for compatible finite-dimensional maps: tr⁡(AB)=tr⁡(BA) (For A∈Mm×n(F) and B∈Mn×m(F), tr⁡(AB)=tr⁡(BA)).

Proof

1.1F1F2F9algebra

Before the controlled powers, the state is Q−1/2∑x=0Q−1∣x⟩⊗ψ by [F2]. Write x=∑jcj2j, where cj is the bit at wire qm−1−j by [F1]. The controlled operation on that wire applies U2j exactly when cj=1; multiplying the eigenvalues gives ∏jexp⁡(2πiθcj2j)=exp⁡(2πiθx). Hence the state after the controlled-power stage is Q−1/2∑xexp⁡(2πiθx)∣x⟩⊗ψ. Reading this gate sequence as an operator on the ordered register uses [F9].

2.1F2F3F9step 1.1algebra

The inverse QFT matrix coefficient from input x to output y is Q−1/2exp⁡(−2πixy/Q), by [F3] and the adjoint convention. Applying it to step 1.1 gives χ=∑y=0Q−1ay∣y⟩⊗ψ, where ay=Q−1∑x=0Q−1exp⁡(2πix(θ−y/Q)). The sign is positive in the eigenphase and negative in the inverse-QFT coefficient, yielding the displayed difference θ−y/Q. The operator ordering is the one in [F9].

3.1F6F7F10step 2.1algebra

The vector χ is normalized. Expand the finite squared moduli using [F7]. For every real t, [F10] gives exp⁡(it)‾=exp⁡(−it), and the exponential addition law combines each conjugate pair of phases; interchanging the finite sums then gives ∑y∣ay∣2=Q−2∑x,x′exp⁡(2πi(x−x′)θ)∑yexp⁡(−2πi(x−x′)y/Q). By [F6], the inner sum is Q when x=x′ and 0 otherwise, because 0≤x,x′<Q; therefore ∑y∣ay∣2=Q−2Q⋅Q=1. Since ∥ψ∥=1 and the ∣y⟩ are orthonormal, ∥χ∥=1.

3.2F4F5F8step 2.1

The ray-projector theorem [F5] makes ρ=∣χ⟩⟨χ∣ a density operator. The phase-register projective outcome has projector Py from [F4], so it extracts ay∣y⟩⊗ψ. By [F5] and [F8], tr⁡(ρPy)=tr⁡(Pyρ)=tr⁡(∣Pyχ⟩⟨χ∣)=⟨Pyχ,χ⟩root=∥Pyχ∥2=∣ay∣2, where the penultimate equality uses Py=Py∗=Py2. The outcome law in [F4] therefore gives this probability to the classical label y. Substituting the value of ay from step 2.1 gives exactly the displayed Pθ(y).

4.1F1F9step 2.1algebra∎

The finite sum in the displayed law is defined for every y,θ, including when Qθ is an integer. For its geometric-sum specialization, the denominator is zero exactly when z=exp⁡(2πi(θ−y/Q))=1, equivalently Qθ−y∈QZ; in every other case Finite geometric sum for phase-estimation amplitudes gives the nonzero-denominator quotient. Thus the stated squared-sum law handles all phases without an undefined quotient.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passOpen item page →

Phase-estimation query and gate costs

Statement

Let m≥1 and Q=2m. Count costs in the ideal continuous-rotation submodel, where each exact controlled phase used by the binary QFT is one gate. If the controlled powers C(U2j), 0≤j<m, are supplied as one query each, one run uses exactly m controlled-power queries. If the only oracle is C(U), the repeated implementation uses exactly Q−1 controlled-U queries. If the supplied controlled-power circuits have ordinary gate counts Cj, the unitary gate count after the eigenstate input is provided is ∑j=0m−1Cj+2m+m(m−1)2+⌊m2⌋. The final register measurement reads m bits and is not included in this unitary gate count. No cost-free power construction, fixed finite gate alphabet, uniform family, or state-preparation bound is asserted.

Facts & Assumptions

Given: The m-bit QPE procedure, Q=2m, and the ideal gate-cost convention stated above.

[F1]

The QPE procedure applies one controlled U2j for each j=0,…,m−1, with m Hadamards to prepare the phase register (Quantum phase estimation).

[F2]

Under single-controlled-unitary access, one call is the control-first block C(U)=diag⁡(IH,U); the eigenphase-estimation model permits the jth controlled power to be implemented by 2j sequential calls (Controlled Unitary Access, Eigenphase estimation access model).

[F3]

The exact m-wire QFT circuit uses m Hadamards, m(m−1)/2 controlled phases, and ⌊m/2⌋ named swaps in the ideal model; its inverse has the same count (Exact binary QFT ideal gate count).

[F5]

The operation model may assign gate, classical-bit, and query costs separately, and each coordinate counts the calls actually made (Finite circuit resource accounting).

[F4]

The natural-valued sum Sn:=∑j<n2j has S0=0 and Sn+1=Sn+2n (Finite sums and finite products of natural numbers, ∑k<nak and ∏k<nak in N).

[F6]

Natural powers satisfy 20=1 and 2n+1=2n⋅2; natural multiplication satisfies a⋅2=a+a, using a⋅1=a (Exponentiation of natural numbers, mn, and its agreement with the integer power in R, Multiplication of natural numbers, Zero and one under multiplication).

[F7]

Natural induction is valid, and natural addition is associative and commutative (The principle of mathematical induction, Addition is associative, Addition is commutative).

Proof

1.1F1givenalgebra

Under controlled-power access, the loop in [F1] invokes exactly one supplied operation for each of the m indices j=0,…,m−1, hence exactly m controlled-power queries.

1.2F2F4F6F7givenbaseihdischarge-induction

Under single controlled-unitary access, the repeated implementation specified by the procedure uses 2j sequential calls to C(U) for the controlled power at index j [F2], so the total query count is the natural sum Sm. To identify this natural-valued sum, prove Sn+1=2n by induction. At n=0, S0+1=0+1=1=20. If Sn+1=2n, then [F4, F6, F7] give Sn+1+1=(Sn+2n)+1=(Sn+1)+2n=2n+2n=2n⋅2=2n+1. Hence Sm+1=2m=Q; by the defining truncated difference in [F4]'s source and commutativity of addition, Sm=Q−1.

1.3F1F3givenalgebra

The supplied power circuits contribute ∑jCj ordinary gates. Phase-register preparation contributes m Hadamards, and the inverse QFT contributes the count in [F3]. Adding gives the displayed unitary gate count; reversing the QFT circuit changes neither its count nor the query count.

2.1F5step 1.1step 1.2step 1.3∎

Measurement outputs one m-bit label. The operation model [F5] keeps query, ordinary-gate, and classical-bit costs separate; the input eigenstate's preparation is outside the theorem's stated count. Therefore the displayed formula asserts only the stated gate profile and does not infer an implementation of powers from a black-box U.

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

Nearest-grid probability in phase estimation

Statement

For every m≥1, Q=2m, and θ∈[0,1), a nearest grid label y∈{0,…,Q−1}, minimizing dT(θ−y/Q), exists. Every nearest label satisfies Pθ(y)≥4π2. In particular, this holds at ties between adjacent labels and at the wrap-around between Q−1 and 0.

Facts & Assumptions

Given: m≥1, Q=2m, and θ∈[0,1).

[F1]

For every real t, there is an integer n=⌊t⌋ with n≤t<n+1 (Integer part: for every real x there is exactly one integer m with m≤x<m+1).

[F2]

For x∈(−1,1), dT(x)=min⁡(∣x∣,1−∣x∣)∈[0,1/2] (Circular phase distance modulo one).

[F3]

The phase-estimation probability is Pθ(y)=Q−2∣∑j<Qexp⁡(2πij(θ−y/Q))∣2 (Phase-estimation finite outcome law).

[F4]

If z≠1, then ∑j<Qzj=(1−zQ)/(1−z) (Finite geometric sum for phase-estimation amplitudes).

[F5]

For 0≤u≤1/2, sin⁡(πu)≥2u, and for v≥0, sin⁡v≤v (Sine chord and linear bounds).

[F7]

Complex conjugation preserves modulus and ∣z+w∣≤∣z∣+∣w∣ (Conjugation is an involutive real-field automorphism, zz‾=∣z∣2, and modulus is definite, multiplicative, and subadditive).

[F8]

Proof

1.1F1F2givenchoose

Put n=⌊Qθ⌋ and r=Qθ−n, so n∈{0,…,Q−1} and 0≤r<1 by [F1]. If r≤1/2, choose y=n; if r>1/2 and n+1<Q, choose y=n+1; if r>1/2 and n+1=Q, choose y=0. The resulting circular distance is respectively r/Q or (1−r)/Q, at most 1/(2Q).

2.1step 1.1F1F2algebra

Every integer k has a lift representation k=y+sQ with s∈Z and y∈{0,…,Q−1}: apply [F1] to k/Q and set s=⌊k/Q⌋, y=k−sQ, so 0≤y<Q. Thus grid labels and their lifts range over all integers, and minimizing circular phase distance is the same as choosing an integer nearest to Qθ=n+r. If r≤1/2, every integer k≤n is at distance at least r and every k≥n+1 is at distance at least 1−r≥r, so n is nearest; when r=1/2, n+1 is also nearest. If r>1/2, every integer k≤n is at distance at least r>1−r and every k≥n+1 is at distance at least 1−r, so n+1 is nearest. If a nearest integer is Q, its label is 0: this is the wrap-around choice in step 1.1 when r>1/2, and the tied wrap-around label when r=1/2. Thus every nearest label has distance d≤1/(2Q).

3.1F5F6F7F8step 2.1algebra

Fix a nearest label and set x=θ−y/Q, d=dT(x). Since x∈(−1,1), the definition of d gives an integer ℓ∈{−1,0,1} and sign ε∈{−1,1} with x=ℓ+εd. By [F6], exp⁡(2πix)=exp⁡(2πiεd). If ε=−1, Euler's formula and the evenness of cosine and oddness of sine in [F8] give exp⁡(−2πid)=exp⁡(2πid)‾, so [F7] shows its distance from 1 has the same modulus as for d. Euler's formula gives 1−exp⁡(2πid)=exp⁡(πid)(exp⁡(−πid)−exp⁡(πid))=−2iexp⁡(πid)sin⁡(πd). Since ∣exp⁡(πid)∣=1 and sin⁡(πd)≥2d≥0 by [F5], ∣1−exp⁡(2πix)∣=2sin⁡(πd). The same calculation with Qx=Qℓ+εQd gives ∣1−exp⁡(2πiQx)∣=2sin⁡(πQd) when d>0, because Qd≤1/2.

4.1F3F4step 3.1

When d>0, [F4] and step 3.1 yield Pθ(y)=Q−2(sin⁡(πQd)/sin⁡(πd))2. If d=0, then x=0 and [F3] gives Pθ(y)=1.

5.1F5step 4.1step 2.1algebra∎

If d>0, step 2.1 gives 0<Qd≤1/2. By [F5], sin⁡(πQd)≥2Qd and 0<sin⁡(πd)≤πd; hence step 4.1 gives Pθ(y)≥Q−2(2Qd/(πd))2=4/π2. If d=0, step 4.1 gives Pθ(y)=1≥4/π2. Thus every nearest label, including either label at a tie, has the claimed probability.

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

Circular-distance tail bound in phase estimation

Statement

Let m≥1, Q=2m, θ∈[0,1), η∈R, and K=⌊Qη⌋≥2. With dT(θ−y/Q) as in Circular phase distance modulo one, Pr⁡y∼Pθ ⁣[dT(θ−y/Q)≥η]≤12(K−1).

Facts & Assumptions

Given: m≥1, Q=2m, θ∈[0,1), and η∈R with K=⌊Qη⌋≥2.

[F1]

The outcome law is Pθ(y)=Q−2∣∑j<Qexp⁡(2πij(θ−y/Q))∣2 (Phase-estimation finite outcome law).

[F2]

Circular distance satisfies 0≤dT(x)≤1/2 for x∈(−1,1) (Circular phase distance modulo one).

[F3]

The integer part K=⌊Qη⌋ satisfies K≤Qη<K+1, and the same floor property applies to QdT(θ−y/Q) (Integer part: for every real x there is exactly one integer m with m≤x<m+1).

[F4]

The geometric-sum identity gives ∑j<Qzj=(1−zQ)/(1−z) for z≠1 (Finite geometric sum for phase-estimation amplitudes).

[F5]

For 0≤u≤1/2, sin⁡(πu)≥2u (Sine chord and linear bounds).

[F8]

Proof

1.1F1F4F5F6F7F8givenalgebra

For an outcome y, put x=θ−y/Q and d=dT(x). If d>0, then x∉Z; write x=ℓ+εd with ℓ∈{−1,0,1} and ε∈{−1,1}. By [F4] and [F1], Pθ(y)=Q−2∣1−exp⁡(2πiQx)∣2/∣1−exp⁡(2πix)∣2. By [F6], integer shifts do not change these exponentials. For d∈[0,1/2], Euler's formula gives 1−exp⁡(2πid)=exp⁡(πid)(exp⁡(−πid)−exp⁡(πid))=−2iexp⁡(πid)sin⁡(πd). Since ∣exp⁡(πid)∣=1 and sin⁡(πd)≥2d by [F5], the denominator modulus is at least 4d; if ε=−1, Euler's formula and the evenness of cosine and oddness of sine in [F8] give exp⁡(−2πid)=exp⁡(2πid)‾, so [F7] gives the same modulus. The numerator is at most 2 by [F6]–[F7]. Hence Pθ(y)≤1/(4Q2d2). If d=0, this outcome is not in the tail event because Qη≥K≥2.

1.2F2F3givenalgebra

For an outcome in the tail, let k=⌊Qd⌋. If k<K, then Qd<k+1≤K≤Qη≤Qd, a contradiction; hence k≥K. Also k≤Q/2 by [F2], since Qd≤Q/2. If K>Q/2, there are no tail outcomes.

1.3F2F3givenconstruct

Suppose K≤Q/2. For each label y, choose ℓy=0 if ∣x∣≤1/2, ℓy=1 if x>1/2, and ℓy=−1 if x<−1/2, where x=θ−y/Q. Then δy=Q(x−ℓy)∈Qθ+Z and ∣δy∣=Qd. Distinct labels give distinct lifts: equality would make their difference a multiple of Q, while their label difference has magnitude less than Q. For an annulus k≤Qd<k+1, the lift lies in [k,k+1) or (−(k+1),−k]. Each half-open interval of length 1 contains at most one element of the integer translate Qθ+Z, because distinct elements differ by a nonzero integer. Thus at most two labels lie in this annulus.

2.1F3step 1.1step 1.2step 1.3algebra∎

Every such label has d≥k/Q, so step 1.1 bounds its probability by 1/(4k2). The total tail probability is therefore at most 12∑k=KQ/2k−2. For k≥K≥2, k−2≤[k(k−1)]−1=(k−1)−1−k−1. The finite sum telescopes to at most 1/(K−1), so the tail is at most 1/(2(K−1)). Together with the empty-tail case in step 1.2 this proves the claim.

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

Exact phase recovery on the QPE grid

Statement

Let m≥1, Q=2m, and θ∈[0,1). If Qθ=t∈Z, then t∈{0,…,Q−1}, phase estimation returns y=t with probability 1, and every other output has probability 0.

Facts & Assumptions

Given: The eigenphase input, m≥1, Q=2m, and Qθ=t∈Z.

[F1]

The outcome probability is Pθ(y)=Q−2∣∑j<Qexp⁡(2πij(θ−y/Q))∣2 (Phase-estimation finite outcome law).

[F2]

For Q≥1 and integers k,ℓ, ∑j<Qexp⁡(2πi(k−ℓ)j/Q) is Q when k≡ℓ(modQ) and 0 otherwise (Orthogonality of the characters x↦e2πikx/N on Z/NZ).

Proof

1.1givenF1algebra

Since 0≤θ<1, the integer t=Qθ satisfies 0≤t<Q. At y=t, every summand in [F1] is 1, so Pθ(t)=Q−2Q2=1.

2.1givenF1F2step 1.1algebra∎

If y∈{0,…,Q−1} and y≠t, then t−y is not divisible by Q, since ∣t−y∣<Q and t−y≠0. Substituting θ=t/Q in [F1], the sum is the nontrivial cyclic-character sum in [F2], hence zero; therefore Pθ(y)=0.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passOpen item page →

Phase estimation on a superposition of eigenspaces

Statement

Let U be a unitary on a nonzero finite-dimensional complex inner-product space H. Let Θ⊂[0,1) be its finite set of distinct eigenphases, and let Πθ denote the orthogonal projection onto the full eigenspace for eigenvalue exp⁡(2πiθ); degeneracies are grouped in one projection. For Q=2m, m≥1, define ay(θ):=Q−1∑j=0Q−1exp⁡(2πij(θ−y/Q)),Ky:=∑θ∈Θay(θ)Πθ. For a unit input ψ=∑θ∈ΘΠθψ, the unnormalized branch vector is Kyψ and py=∥Kyψ∥2=∑θ∈Θ∣ay(θ)∣2∥Πθψ∥2. If py>0, the conditional vector is Kyψ/py; if py=0, no conditional vector is assigned. The outcome law is an eigenspace-weighted mixture, while the conditional branch can retain coherent cross terms between distinct eigenspaces.

After discarding the phase register, the induced system instrument has branch maps Iy(X)=KyXKy† for operators X on H. More generally, for a density operator ρ on H⊗R, where R is a nonzero finite-dimensional reference space, the unnormalized reference-preserving branch after measuring and discarding the phase register is Iy(R)(ρ)=(Ky⊗IR)ρ(Ky†⊗IR). Its probability is tr⁡Iy(R)(ρ), and the conditional state is Iy(R)(ρ)/tr⁡Iy(R)(ρ) exactly when that probability is positive. With wθ=tr⁡((Πθ⊗IR)ρ), the reference outcome law is py=∑θ∈Θ∣ay(θ)∣2wθ, where wθ≥0 and ∑θwθ=1. If every eigenphase is on the Q-grid, then Ky=Πy/Q when y/Q∈Θ and Ky=0 otherwise, so the instrument branch is X↦KyXKy†. If any eigenphase is off-grid, its nonzero coefficient has modulus less than one in every Ky, so Ky†Ky is not a projection and the induced instrument is not the spectral projection instrument.

Facts & Assumptions

Given: The finite-dimensional unitary U, its spectral subspaces, the QPE circuit and access promise, a unit vector ψ, and optionally a density operator ρ on H⊗R.

[F1]

A unitary on a finite-dimensional inner-product space is normal: the isometry characterization gives U∗U=UU∗=I (For an endomorphism in finite dimension, preserving lengths, preserving inner products, carrying orthonormal bases to orthonormal bases, and T∗T=I are equivalent). The complex spectral theorem therefore gives an orthogonal decomposition into eigenspaces, with mutually orthogonal spectral projections summing to the identity (Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely, Self-adjoint and normal endomorphisms of a finite-dimensional real or complex inner product space).

[F13]

The algorithm's final readout branch is the Lüders map Z↦PyZPy for Py=∣y⟩⟨y∣⊗IH (Quantum phase estimation).

[F14]

Chronological layers L1,…,Ld denote ULd⋯UL1, and each local gate is embedded on its declared wires (Denotational semantics of a finite layered circuit). For linear maps A,B, the operator A⊗B is the linear map with (A⊗B)(u⊗v)=Au⊗Bv (Tensor products of finitely many linear maps).

[F15]

Tensor-map adjoints satisfy (A⊗B)†=A†⊗B†, so (Ky⊗IS)†=Ky†⊗IS for every finite reference factor S (Tensor maps preserve composition, identities, and adjoints).

[F16]

If X=[Xij] in the ancilla basis, the identity extension of Φ(X)=KyXKy† has blocks KyXijKy† by the block-amplification lemma. The sandwich (Ky⊗IS)X(Ky†⊗IS) has those same blocks because tensor maps act factorwise (Finite ancilla amplification is a well-defined block map, Tensor products of finitely many linear maps, Tensor maps preserve composition, identities, and adjoints).

[F4]

The inverse positive-sign QFT contributes matrix coefficient Q−1/2exp⁡(−2πixy/Q), and the eigenphase convention contributes exp⁡(2πijθ) on the j-th controlled-power branch (Positive-sign cyclic quantum Fourier transform, Quantum phase estimation, Exact binary QFT circuit correctness).

[F5]

The finite outcome kernel satisfies ∑y∣ay(θ)∣2=1 for every phase θ (Phase-estimation finite outcome law).

[F6]

Orthogonal projections satisfy Πθ∗=Πθ, ΠθΠϕ=0 for θ≠ϕ, and ∑θΠθ=IH (Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely, The orthogonal projection PWv is the W-component in V=W⊕W⊥).

[F7]

A completely positive map remains positive under every finite identity extension; a finite instrument has completely positive branches and its sum preserves trace (Completely positive maps, Finite instruments and conditional states).

[F9]

A density operator is nonnegative with trace one, and partial trace over a finite register discards that factor while retaining the reference system (Density operator, Partial trace over a finite tensor factor).

[F10]

For z≠1, the finite geometric amplitude is (1−zQ)/(1−z); exp⁡(w)=1 exactly for w∈2πiZ (Finite geometric sum for phase-estimation amplitudes, ker⁡(exp⁡)=2πiZ, and exp⁡z=exp⁡w exactly when z−w∈2πiZ).

[F11]

The phase-register computational-basis readout is a projective measurement with effects Py=∣y⟩⟨y∣⊗IH: they are self-adjoint idempotents, mutually orthogonal and complete (Finite registers and their ordered computational bases, Ordered tensor registers, their inner product, and wire permutations, Tensor products of finitely many linear maps, Finite projective measurements). For every vector v in the joint register, ⟨Pyv,v⟩=⟨Py2v,v⟩=⟨Pyv,Pyv⟩≥0, so each Py is nonnegative (Non-negative and positive operators). Together with completeness, this makes (Py)y a finite POVM (Finite POVMs and their outcome law).

[F12]

The spectral projections tensored with IR form a finite projective measurement, and their traces against a density operator are nonnegative weights summing to one (Tensor products of finitely many linear maps, Projective measurement outcome weights are probabilities, Finite projective measurements, Density operator).

Proof

1.1givenF1F2F3

By [F1], write H=⨁θ∈ΘEθ orthogonally with projections Πθ. By [F2], every eigenvalue has modulus one, and [F3] supplies its unique phase θ∈[0,1). Therefore ∑θΠθ=IH and ψ=∑θΠθψ.

2.1F4F14step 1.1algebra

On a phase-register basis branch ∣j⟩, the controlled powers act as Uj. On each eigenspace, UjΠθ=exp⁡(2πijθ)Πθ. Applying the inverse-QFT matrix in [F4], the coefficient of ∣y⟩ on Eθ is ay(θ); hence the post-readout Kraus operator on H is Ky=∑θay(θ)Πθ. Interpreting the chronological gate list as this operator uses [F14].

2.2F5F6step 1.1algebra

Orthogonality in [F6] gives Ky†Ky=∑θ∣ay(θ)∣2Πθ. Summing over y and using [F5] yields ∑yKy†Ky=∑θΠθ=IH. Consequently, for a unit vector ψ, ∥Kyψ∥2=∑θ∣ay(θ)∣2∥Πθψ∥2; all cross terms vanish because the eigenspaces are orthogonal. The weights ∥Πθψ∥2 are nonnegative and sum to one, so this is the eigenspace-weighted mixture law.

2.3F4F10step 1.1

If every θ∈Θ has Qθ=tθ∈{0,…,Q−1}, the finite geometric sum in [F10] and cyclic-character orthogonality give ay(θ)=1 for y=tθ and 0 otherwise. Distinct phases have distinct labels, so Ky is exactly the spectral projection for the eigenspace with label y, or zero if there is none.

3.1F7F9F11F13F14F15F16step 2.1constructalgebra

By linearity of the circuit, its pre-readout map from H to the phase register tensored with H is V:v↦∑z∣z⟩⊗Kzv, with the Kz from step 2.1. For any operator X on H⊗R, the joint output is (V⊗IR)X(V†⊗IR), using [F15] for the tensor adjoint. Applying the readout branch in [F13] gives ∣y⟩⟨y∣⊗(Ky⊗IR)X(Ky†⊗IR). Reorder the phase factor to the last position using [F11], then take its partial trace by the coordinate formula [F9]; the sole nonzero diagonal phase coordinate is y, so the result is exactly (Ky⊗IR)X(Ky†⊗IR). In particular this holds for the density operator ρ in the statement. To prove complete positivity, take any finite reference factor S and any nonnegative operator X on H⊗S. Its identity extension is X↦(Ky⊗IS)X(Ky†⊗IS) by [F16]. Writing A=Ky⊗IS, its quadratic form is ⟨AXA†v,v⟩=⟨XA†v,A†v⟩≥0, so every finite extension is positive and each branch is completely positive by [F7].

3.2F5F10givenstep 2.1algebra

If some phase θ is off-grid, then for every y, z=exp⁡(2πi(θ−y/Q))≠1 and zQ=exp⁡(2πiQθ)≠1 by [F10]. The geometric formula gives ay(θ)≠0 for every y. Since Q≥2 and [F5] says the Q squared moduli sum to one, each is strictly less than one. Thus Ky acts on this eigenspace by a scalar of modulus strictly between zero and one, so Ky†Ky is not a projection; the induced system instrument is not the spectral projection instrument.

4.1F7F8F9F12F14step 2.2step 3.1

For any finite-dimensional reference R (including R=C) and any operator X on H⊗R, [F8] and step 2.2 give ∑ytr⁡Iy(R)(X)=tr⁡(((∑yKy†Ky)⊗IR)X)=tr⁡X. Taking R=C proves that the system maps form an instrument on H. For a density ρ with arbitrary reference R, cyclicity and linearity also give py=tr⁡Iy(R)(ρ)=∑θ∣ay(θ)∣2tr⁡((Πθ⊗IR)ρ). By [F12], the weights tr⁡((Πθ⊗IR)ρ) are nonnegative and sum to one. Therefore the reference outcome law is the claimed mixture. When py>0, Iy(R)(ρ)/py is nonnegative of trace one, and when py=0 no conditional density is defined. For a pure input the conditional vector is Kyψ/py.

5.1F3F10step 2.1constructalgebra∎

To witness coherence for a permitted m≥1, let Q=2m and choose the witness space Hwit=C2 with orthonormal basis u0,u1. Define U on this entire space by Uu0=u0 and Uu1=exp⁡(πi/Q)u1, extending linearly. Each basis coefficient is multiplied by a unit scalar by [F3], so U preserves the norm and is unitary by For an endomorphism in finite dimension, preserving lengths, preserving inner products, carrying orthonormal bases to orthonormal bases, and T∗T=I are equivalent. Set ψ=(u0+u1)/2, which is unit. At output y=0, a0(0)=1, while [F10] and [F3] give a0(1/(2Q))=Q−1∑j<Qexp⁡(πij/Q)=2/[Q(1−exp⁡(πi/Q))]≠0: [F10] makes the denominator nonzero since Q(1/(2Q))=1/2∉QZ, and [F3] gives exp⁡(πi)=−1 for the numerator. Thus K0ψ=(u0+a0(1/(2Q))u1)/2, and its rank-one operator has the nonzero cross terms a0(1/(2Q))∣u1⟩⟨u0∣/2 and a0(1/(2Q))‾∣u0⟩⟨u1∣/2. The branch probability is positive because its u0 coefficient is nonzero, so conditioning preserves these cross terms.

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

Phase-estimation nearest-grid and tail bounds

Statement

Let m≥1, Q=2m, and θ∈[0,1). Every nearest grid label y has Pθ(y)≥4/π2. For any η∈R with K=⌊Qη⌋≥2, Pr⁡y∼Pθ ⁣[dT(θ−y/Q)≥η]≤12(K−1). If Qθ is an integer t, then Pθ(t)=1 and all other labels have probability 0.

Facts & Assumptions

Given: m≥1, Q=2m, θ∈[0,1), and, for the tail conclusion, η∈R with K=⌊Qη⌋≥2.

[F1]

A nearest label exists and every nearest label has probability at least 4/π2 (Nearest-grid probability in phase estimation).

[F2]

The circular-distance tail for K≥2 is at most 1/(2(K−1)) (Circular-distance tail bound in phase estimation, Circular phase distance modulo one).

[F3]

Exact-grid phases are returned with probability one (Exact phase recovery on the QPE grid).

Proof

1.1givenF1

Apply [F1] to the given θ; it supplies a nearest label y and the lower bound Pθ(y)≥4/π2.

1.2givenF2

If K=⌊Qη⌋≥2, all hypotheses of [F2] hold, so the stated circular-distance tail bound follows.

2.1givenF3step 1.1step 1.2∎

If Qθ=t∈Z, [F3] gives Pθ(t)=1 and zero probability at every other label. These conclusions prove the theorem.

5 · Examples, counterexamples and false statements

None yet.

Sources