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.
The binary [7,4,3] Hamming code and its single-error syndromes
Example
Over , take the column-vector matrices
The code has parameters . The syndrome of a single-bit error is the corresponding column of ; the seven syndromes are the seven nonzero vectors of .
Facts & Assumptions
Given: The matrices above, column-vector multiplication, and .
For every prime , the two operations on make it a field: For each prime , the residue classes form a field.
Rectangular matrix multiplication and the identity matrix , including zero-sized shapes: Matrix products are formed by finite coordinate sums.
Rank-nullity: : A linear map from a finite-dimensional domain has .
If and is a linear subspace of , then is finite-dimensional, , and if and only if : A subspace with the same dimension as its finite-dimensional ambient space equals it.
Row space, column space, nullspace, row rank, column rank and matrix rank: The row rank is the dimension of the row space, the nullspace is , and matrix rank is row rank.
A full-column-rank matrix has image dimension equal to its number of columns: Independent columns of span a subspace of dimension equal to their number.
Syndrome of a received word over a finite field: The syndrome map is .
Classical distance gives error detection, unique decoding, and erasure recovery: A code of distance uniquely decodes one unknown symbol error.
Verification
The last four rows of form , so its columns are independent and [L1] gives . The first three columns of form , so for every the input maps to . Also, a linear combination of the rows of has its first three entries equal to its three coefficients, so the rows are independent and [F5] gives rank . For , ; applying the three rows of gives . Thus and .
The map is onto by its first three columns, so [F3] gives . The subspace also has dimension by step 1.1; [F4] now gives .
The columns of are . They are all nonzero and pairwise distinct. A weight-one vector has syndrome one of these columns; a weight-two vector has syndrome the sum of two distinct columns, which is nonzero in characteristic two. Thus has no nonzero word of weight below . The word has syndrome and weight , so it lies in and .
Multiplying by the zero word and each unit vector gives , , , , , , , and ; each nonzero value is the corresponding column of . These eight syndromes are distinct, so [L3] guarantees unique correction of any one bit flip.
Remarks
The minimum weight and every syndrome are verified from the displayed matrices; no source convention is used as a proof step.
Depends on
- Rectangular matrix multiplication and the identity matrix $I_n$, including zero-sized shapes
- The vector space $M_{m \times n}(F) := F^{\,m \times n}$ of $m$ by $n$ matrices over a field, with entrywise operations
- Syndrome of a received word over a finite field
- Classical linear code over a finite field
- Generator and parity-check matrices over a finite field
- Hamming weight and distance of a finite-field linear code
- Row space, column space, nullspace, row rank, column rank and matrix rank
- The standard list $e : n \to F^{n}$ with $e_i(i) = 1_F$ and $e_i(j) = 0_F$ for $j \ne i$ is an ordered basis of $F^{n}$; hence $\dim_F F^{n} = n$, and $F^{0}$ is the zero space with basis $\varnothing$ and dimension $0$
- If $\dim_F V = n$ and $U$ is a linear subspace of $V$, then $U$ is finite-dimensional, $\dim_F U \le n$, and $\dim_F U = n$ if and only if $U = V$
- Classical distance gives error detection, unique decoding, and erasure recovery
- A full-column-rank matrix has image dimension equal to its number of columns
- Rank-nullity: $\dim_F V=\operatorname{nullity}T+\operatorname{rank}T$
- For every prime $p$, the two operations on $\mathbb{Z}/p$ make it a field
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
61 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
- Richard Cleve, Quantum Information Processing course notes, §§37.1.2–37.1.3 (standard reference, not scraped)
- John Preskill, Quantum Computation, Chapter 7, §§7.6 and 7.8 (standard reference, not scraped)