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.
Classical and Quantum Error-Correcting Codes — Examples
1 · Prerequisites
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Classical and Quantum Error-Correcting Codes
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Free Modules, Exact Sequences, Projective and Injective Modules
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Group Homomorphisms and the Isomorphism Theorems
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Modules, Submodules, Quotient Modules and the Isomorphism Theorems
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Registers, Tensor Products, and Partial Trace
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Simple Field Extensions and the Construction of the Complex Numbers
- Suprema and Infima
- Tensor Products of Modules
- The ZFC Axioms and the Basic Set Constructions
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
This companion works through the three-bit repetition code, the binary Hamming code, and a short Reed–Solomon evaluation code. It also computes a quantum repetition-code projector and exhibits a weight-one undetectable operator at distance one. The finite syndrome and projector calculations are explicit; the page remains a logical leaf and supplies no prerequisites to the theory page.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Repetition-code syndrome, known erasures, and unlocated errors
Example
Use the binary repetition code and . A single middle-bit error in gives and a syndrome identifying that error. Two known erasures at coordinates still leave a unique completion when coordinate is received. By contrast, two unlocated errors in can produce , which lies closer to the wrong codeword .
Facts & Assumptions
Given: The binary repetition code , the parity-check matrix above, a received word , known erasures in zero-based coordinates, and the alternative unlocated-error word .
Rectangular matrix multiplication and the identity matrix , including zero-sized shapes: Matrix multiplication uses finite coordinate sums over the field.
Generator and parity-check matrices over a finite field: A parity-check matrix has independent rows and nullspace equal to .
Hamming weight and distance of a finite-field linear code: Weight counts nonzero coordinates, and is the minimum nonzero codeword weight.
Syndrome of a received word over a finite field: The syndrome of a received word is .
Classical distance gives error detection, unique decoding, and erasure recovery: Distance uniquely decodes one unknown error and gives unique erasure completion on every known support of size less than .
Verification
The codewords are and , and [L2] gives . For , multiplication gives , so exactly when and . The first two columns of form , an invertible triangular matrix over , so the rows of are independent. Therefore is a parity-check matrix by [L1]; the field operations are valid by [F1].
Direct multiplication gives the weight-at-most-one syndromes , , , and , all distinct. For , [L3] gives , so the unique matching error is and . The distinct syndromes give the one-error correction in [L4].
If coordinates are erased in , the received value at coordinate is , so is compatible and is not. More generally, two compatible codewords would differ only in , so their distance would be at most , contradicting [L4]. Hence is the unique completion.
If instead is sent, the two-bit error produces . Then but , so is the unique radius-one decoding candidate. The unique-radius-one rule therefore miscorrects this word; [L4] gives no correction guarantee for the two-bit error.
Remarks
Coordinates are zero-based. The example distinguishes a single unknown error from two known erasures and from two unknown errors; the code is guaranteed to correct one unknown error, while known locations carry stronger information.
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.
A short Reed–Solomon code over and its symbol-error syndromes
Example
In , evaluate the degree-less-than-two polynomials at . This gives the Reed–Solomon evaluation code
with encoding and generator/parity-check matrices
It has parameters . Its four parity-check columns give distinct projective directions, so every one-symbol error value at every position has a distinct syndrome.
Facts & Assumptions
Given: The field , evaluation points , and matrices displayed above.
For every prime , the two operations on make it a field: For every prime , is a field.
Rectangular matrix multiplication and the identity matrix , including zero-sized shapes: Matrix products are the finite coordinate sums over the field.
Rank-nullity: : The dimension of a finite-dimensional domain is the sum of kernel and image dimensions.
If and is a linear subspace of , then is finite-dimensional, , and if and only if : A subspace of a finite-dimensional space with equal dimension is the whole space.
Row rank equals column rank, and both equal the number of pivots: The row rank and column rank of a finite matrix over a field are equal.
A full-column-rank matrix has image dimension equal to its number of columns: The image of a full-column-rank matrix has dimension .
Syndrome of a received word over a finite field: A received vector's syndrome is .
Classical distance gives error detection, unique decoding, and erasure recovery: Distance uniquely decodes one unknown symbol error and distinct allowed errors have distinct syndromes.
Verification
The map is injective because its first two coordinates recover and . Hence the displayed has independent columns and [L1] gives .
Multiplication gives : the four row-column dot products are , all zero in . The first two columns of , and , are independent, so its column rank is ; [F5] makes its row rank as well.
If and , then has no roots. If , the equation has the unique solution , so at most one of the four distinct evaluation points is a root. Every nonzero codeword therefore has weight at least , and gives of weight . Thus .
The columns of are , , , and . Their six pairwise determinants in are , all nonzero, so no two are scalar multiples. A nonzero error has syndrome ; within one position distinct nonzero give distinct syndromes, and across positions equality would force proportional columns. Hence all nonzero one-symbol errors have distinct syndromes.
Therefore . By [F3], ; [F4] gives .
For example, the error has syndrome in . Since , [L3] gives unique one-symbol correction. The example uses the evaluation-code definition; its exact distance and syndrome claims above are proved here rather than inferred from a source formula.
Remarks
The MIT lecture notes define Reed–Solomon codes by evaluating degree-less-than- polynomials at distinct field points. Their displayed root-count bound is one looser than the sharp degree bound needed to derive the stated exact distance formula; this example does not rely on it and proves its parameters directly.
Quantum repetition subspace projector and a distance-one boundary error
Example
Use the ordered three-qubit tensor basis supplied by the QC02 register interface. Set the logical space to with the standard pairing , and let be its standard basis. Define , . The code projector is diagonal in this basis, with diagonal entry at and elsewhere. Define and . Then is detectable but is not; the code has distance , and the boundary operator has weight exactly .
Facts & Assumptions
Given: The ordered three-qubit tensor basis and its orthonormality as specified by the QC02 interface, together with the maps defined above.
Finite registers and their ordered computational bases: A qubit is with ordered standard basis ; for qubits, bit positions are ordered lexicographically with the first wire most significant.
Ordered tensor registers, their inner product, and wire permutations: The tensor inner product is the product of the factor pairings, linear in the first argument, and product basis vectors are ordered lexicographically.
The product basis is orthonormal and tensor dimensions multiply: The elementary product vectors form an orthonormal basis of the finite tensor register.
Tensor products of finitely many linear maps: A tensor map is the unique linear extension of its factorwise action on elementary tensors.
The adjoint is characterised by : The adjoint satisfies .
Quantum code as an isometric encoding and its code projector: The code projector is and the code subspace is .
An isometric quantum encoding gives an orthogonal code projector: is the orthogonal projection onto the code subspace.
Support and weight of an operator on a finite register: Support on a set of wires defines operator weight, and scalar identity operators have weight zero.
Detectable operators and distance of a finite-register quantum code: An operator is detectable iff ; distance is the minimum weight of an undetectable operator when .
Detectable operators form a subspace and quantum distance is well defined: For logical dimension at least two, the undetectable set is nonempty and its minimum weight exists; weight-zero operators are detectable.
Every operator below the quantum code distance is detectable: Every operator of weight strictly below the quantum distance is detectable.
Verification
From the standard pairing in [F1], , so are orthonormal. Each physical factor is also with this pairing; [F2] gives , and [F3] gives the ordered product basis. Thus are orthonormal, and is isometric. For each basis vector , [F5] gives , which is for or and otherwise. Since , this computes , , and for the other six basis strings. Thus has exactly the stated diagonal entries and is the code projector in [L1] and [L2].
The map sends to and to , both orthogonal to the code subspace by [F3]. Thus , so is detectable with scalar . By [F1] and [F4], its action on elementary tensors is ; hence it is supported on the first wire and has weight at most .
The map fixes and negates . Thus acts by eigenvalues and on the two-dimensional code subspace and is not a scalar multiple of . By [F1] and [F4], it is , so it has weight at most . It is non-detectable; every weight-zero operator is detectable by [L5]. Therefore the minimum non-detectable weight is , and .
By [L6], every operator of weight is detectable; step 1.3 exhibits a non-detectable operator of weight exactly . Thus the detection boundary is sharp, and no correction-map claim follows.
Remarks
The calculation follows the current QC02 basis and tensor-map definitions; the relevant supplier bodies were inspected before this example was recorded.
Sources
- Richard Cleve, Quantum Information Processing course notes, §§36.1 and 37.1–37.1.3
- John Preskill, Quantum Computation, Chapter 7, §§7.6 and 7.4.1
- Richard Cleve, Quantum Information Processing course notes, §§37.1.2–37.1.3
- John Preskill, Quantum Computation, Chapter 7, §§7.6 and 7.8
- Madhu Sudan, Essential Coding Theory, MIT 6.895, Lecture 5, “Reed-Solomon Codes” (Fall 2004)
- John Preskill, Quantum Computation, Chapter 7, §7.6
- Richard Cleve, Quantum Information Processing course notes, §§36.3 and 37.3
- John Preskill, Quantum Computation, Chapter 7, §§7.1 and 7.4.1