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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.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Bounded-error success of Shor factoring
Statement
For a promised composite and rational , the procedure Shor factoring with verified output and bounded outer retries returns a proper divisor with probability at least after its declared outer attempts. Its exact even and perfect-power preprocessing branches succeed with probability one.
Facts & Assumptions
Given: The algorithm Shor factoring with verified output and bounded outer retries on , with the least positive integer satisfying .
The even and perfect-power preprocessing branches return proper factors whenever they apply (Even and perfect-power preprocessing returns a proper factor).
A sampled nonunit residue immediately yields a proper gcd factor, and conditional on gcd one the residue is uniform in the unit group (Random residue gcd branch for factoring).
For an odd composite that is not a perfect power, the true-order reduction succeeds on a uniform unit with probability at least (Classical randomized order-to-factor success bound).
The rejection sampler halts almost surely and returns a uniform representative (Rejection sampling is uniform, almost surely terminating, and has expected linear bit cost).
Every returned output is a proper divisor (Order-to-factor output correctness).
The algorithm uses fresh independent sampler tapes and fresh order-finding randomness across attempts (Shor factoring with verified output and bounded outer retries). For each fixed unit input, order finding uses finitely many measurements of finite registers (Success probability and repetition for modular order finding). Finite terminal outcome laws can therefore be combined by the finite product law, whose coordinate events are independent (The finite product of finite probability spaces, Product weights normalize, and coordinate events are mutually independent).
In a finite probability space, conditional probability is when (Conditional probability for ).
Countable choice and dependent choice supply only the countable independent fair-bit tape used by [F4] (The Axiom of Countable Choice (), The axiom of dependent choice: a relation in which every element is related to something admits an -indexed chain, Assuming countable and dependent choice, countable products of arbitrary probability spaces).
The current body of Success probability and repetition for modular order finding states that for every fixed unit input and integer parameter , the exact-order output probability is at least . Setting gives at least for each fixed unit input. The actual algorithm and theorem bodies and the proof route supporting this statement were inspected; this records the current carrier, not independent acceptance.
Proof
If is even or a perfect power, preprocessing returns a proper factor by [F1], so the theorem holds with probability one. Otherwise is odd, composite, and not a perfect power, so [F3] applies.
By [F4], the sampler halts almost surely and has uniform finite output law. For each unit representative , let be the finite set of order-finding measurement transcripts and its probability mass function; for a nonunit use a singleton of mass one. One attempt has finite terminal outcome space with weights : the sampler marginal is [F4], the subsequent fresh measurement law is , and these weights sum to one. All conditioning here is on this finite space as in [F7]. Conditional on the unit event , [F2] makes uniform in the unit group. Let be the representatives whose true-order reduction succeeds, so [F3] gives . For each , [A2] gives exact-order probability at least ; summing its transcript weights gives . On that event the algorithm returns a proper factor. This uses a pointwise order-finding bound, without assuming independence between the unit and its measurement outcome.
Let be the probability that a sampled residue is a unit. The attempt succeeds with probability at least . Any output is correct by [F5]; a nonminimal or otherwise unsuccessful order candidate can only cause this attempt to continue.
Preassign independent terminal records with the finite product law . By the fresh randomness in [F6], this is the joint terminal law of the actual attempts; stopping at the first success simply leaves subsequent records unused. The event that any of these finitely many samplers does not halt has probability zero by [F4]. Let be the coordinate event that attempt returns no factor. Step 2.1 gives , and finite coordinate independence [F6] gives . Thus the probability of returning a proper factor is at least .
Remarks
The exact-order probability premise [A2] is taken from the current batch-32 carrier and checked against its stated fixed-unit hypotheses; the cited carrier remains a draft and this record is not independent acceptance. Countable choice and dependent choice are used only to supply the sampler's independent fair-bit tapes [A1].
Depends on
- Shor factoring with verified output and bounded outer retries
- Even and perfect-power preprocessing returns a proper factor
- Random residue gcd branch for factoring
- Success probability and repetition for modular order finding
- Classical randomized order-to-factor success bound
- Order-to-factor output correctness
- Rejection sampling is uniform, almost surely terminating, and has expected linear bit cost
- The unit group $(\mathbb{Z}/n)^\times$ and Euler's totient $\varphi(n)=\lvert(\mathbb{Z}/n)^\times\rvert$ for $n\ge1$
- For $n\ge1$, $[a]_n$ is a unit if and only if $\gcd(a,n)=1$
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The axiom of dependent choice: a relation in which every element is related to something admits an $\mathbb{N}$-indexed chain
- Assuming countable and dependent choice, countable products of arbitrary probability spaces
- Finite probability spaces, outcome weights, events, and event probabilities
- Conditional probability $\mathbb P(A\mid B)$ for $\mathbb P(B)>0$
- The finite product of finite probability spaces
- Product weights normalize, and coordinate events are mutually independent
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
98 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Peter W. Shor, Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer (standard reference, not scraped)
- Ronald de Wolf, Quantum Computing: Lecture Notes (standard reference, not scraped)