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arXiv:2602.20366 · IEEE International Symposium on Information Theory (ISIT)

On the Height Profile of Analog Error-Correcting Codes

Ron M. Roth, Ziyuan Zhu, Changcheng Yuan, Paul H. Siegel, Anxiao Andrew Jiang

Published Jun 1, 2026• 0 citations
arXivHTMLPDF

Abstract

In recent work, it has been shown that maintaining reliability in analog vector--matrix multipliers can be modeled as the following coding problem. Vectors in $\mathbb{R}^k$ are encoded into codewords of a linear $[n,k,d]$ code $C$ over $\mathbb{R}$. For prescribed positive reals $δ< Δ$, additive errors of magnitude at most $δ$ are tolerable and need no handling, yet outlying errors of magnitude greater than $Δ$ are to be located or detected. The trade-off between the ratio $Δ/δ$ and the number of outlying errors that can be handled is determined by the height profile of $C$; as such, the height profile provides a finer description of the error handling capability of $C$, compared to the minimum distance $d$, which only determines the number of correctable errors. This work contains a further study of the notion of the height profile. Several characterizations of the height profile are presented, thereby yielding methods for computing it. The starting point is formulating this computation as an optimization problem that is solved by a set of linear programs. This, in turn, leads to a combinatorial characterization of the height profile as a maximum (or max--min) over a certain finite set of codewords of $C$. Moreover, this characterization is shown to have a simple geometric interpretation when the columns of the generator matrix of $C$ all have the same $L_2$ norm. Through examples of several code families, it is demonstrated how the results herein can be used to compute the height profile explicitly.

DOI
10.1109/isit62367.2026.11653716

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Analog error-correcting codes (Analog ECCs) for approximate vector-matrix multiplication have been extensively studied as means to achieve fault-tolerant in-memory computation. The theoretical foundations for such coding schemes, particularly the characterization of their correction capabilities via the height profile, have been well established in recent literature. In this paper, we focus on the case of single-error detection Analog ECCs. Among several open problems related to this case proposed by Ron M. Roth in [1], Problem 1 asks: "Identify the values of kkk and nnn for which every linear [n,k][n, k][n,k] code C\mathcal{C}C over R\mathbb{R}R satisfies: h1(C):=max⁡c∈C∖{0}h1(c)≥⌈kn−k⌉."\mathsf{h}_1(\mathcal{C}):=\max_{\boldsymbol{c}\in \mathcal{C}\setminus{\{\boldsymbol{0}\}}}\mathsf{h}_1(\boldsymbol{c})\geq \Big\lceil \frac{k}{n-k} \Big\rceil.\text{"}h1​(C):=maxc∈C∖{0}​h1​(c)≥⌈n−kk​⌉." Here, for any x∈Rn\boldsymbol{x}\in\mathbb{R}^nx∈Rn, h1(x)\mathsf{h}_1(\boldsymbol{x})h1​(x) represents the ratio between the largest and second largest absolute values of x\boldsymbol{x}x's entries. As the simplest special case of Problem 1 (with n−k=2n-k=2n−k=2), the following problem was posed as Problem 2 in [1]: "Must every (n−2)(n-2)(n−2)-dimensional subspace of Rn\mathbb{R}^nRn, nnn even, contain a nonzero vector in which the ratio between the largest and second largest absolute values of its entries is at least (n/2)−1(n/2)-1(n/2)−1?" These problems directly pertain to the lower bounds on the single-error detection threshold for Analog ECCs: Problem 1 corresponds to arbitrary n−kn-kn−k and Problem 2 corresponds to n−k=2n-k=2n−k=2. In this paper, we provide an affirmative answer to Problem 2 and a rigorous proof using theories related to convex optimization. Furthermore, we extend our analytical method to show that the lower bound in Problem 1 is tight for the case where n−kn-kn−k divides kkk. Our results fill the gap in the lower bound theory of thresholds for single-error detection in Analog ECCs.
Analog error-correcting codes (Analog ECCs), introduced by Roth [1], address errors in vector-matrix multiplication arising from analog noise and sparse outliers in in-memory computing. A fundamental open problem concerns the lower bound on the single-error correction threshold Γ2(C)Γ_2(\mathcal C)Γ2​(C) for real [n,k][n,k][n,k] linear codes with fixed redundancy r=n−k≥2r=n-k\geq 2r=n−k≥2. Li et al. [2] recently established that for redundancy r=2r=2r=2, every real [n,n−2][n,n-2][n,n−2] linear code C\mathcal{C}C satisfies Γ2(C)≥csc⁡2(π2n)Γ_2(\mathcal C)\geq \csc^2(\fracπ{2n})Γ2​(C)≥csc2(2nπ​), resolving an open problem in [1], and showed that, for every fixed r≥2r \geq 2r≥2, there exists a class of [n,k][n,k][n,k] linear code C\mathcal{C}C over R\mathbb{R}R such that Γ2(C)≤O(n1+1r−1)Γ_2(\mathcal{C}) \leq O(n^{1+\frac{1}{r-1}})Γ2​(C)≤O(n1+r−11​). This paper proves the matching converse in [2]. For every [n,k][n,k][n,k] linear code C⊆Rn\mathcal{C}\subseteq \mathbb R^nC⊆Rn with fixed redundancy $2\leq r
In any engineering discipline, one looks for methods of solving a given class of problems and then for methods of quantifying the performance and complexity of those solutions. Accordingly, in this chapter, we shall study performance in terms of the composition structure of block codes, and the probabilities of decoding error and of decoding failure. Later, after satisfactory methods are in hand, one turns to questions of optimality. Are these methods the best methods, and if not, in what ways and by how much are they inferior? To answer such questions, one needs to know how good are the known codes and how good are the best possible codes. Generally, we cannot answer either of these questions satisfactorily. Although for any given rate and blocklength, not too small, the best possible code is not known, a number of bounds are known – bounds beyond which no codes can exist, and bounds within which codes are sure to exist. We shall give some such bounds on the best possible codes later in this chapter. Knowledge of these bounds can deepen our understanding of the subject. Weight distributions of block codes If a linear block code has a minimum distance d min , then we know that at least one codeword of weight d min exists. Sometimes, we are not content with this single piece of information; we wish to know how many codewords have weight d min , and what are the weights of the other codewords.
Quantum synchronizable error-correcting codes are special quantum error-correcting codes that are designed to correct both the effect of quantum noise on qubits and misalignment in block synchronization. It is known that, in principle, such a code can be constructed through a combination of a classical linear code and its subcode if the two are both cyclic and dual-containing. However, finding such classical codes that lead to promising quantum synchronizable error-correcting codes is not a trivial task. In fact, although there are two families of classical codes that are proved to produce quantum synchronizable codes with good minimum distances and highest possible tolerance against misalignment, their code lengths have been restricted to primes and Mersenne numbers. In this paper, examining the incidence vectors of projective spaces over the finite fields of characteristic 2, we give quantum synchronizable codes from cyclic codes whose lengths are not primes or Mersenne numbers. These projective geometric codes achieve good performance in quantum error correction and possess the best possible ability to recover synchronization, thereby enriching the variety of good quantum synchronizable codes. We also extend the current knowledge of cyclic codes in classical coding theory by explicitly giving generator polynomials of the finite geometric codes and completely characterizing the minimum weight nonzero codewords. In addition to the codes based on projective spaces, we carry out a similar analysis on the well-known cyclic codes from Euclidean spaces that are known to be majority logic decodable and determine their exact minimum distances.
This research manuscript develops exact measurement-resource formulas for quantum erasure correction with stabilizer codes and an optimal sparse algorithm for selecting native surface-code checks. It studies known erasure locations, where the stabilizer observables may be chosen after the erased sites are identified. The central question is how many independent syndrome outcomes are necessary to distinguish Pauli errors on those sites, modulo stabilizers. For every prime-dimensional [[n,k,d]]_p stabilizer code encoding at least one logical qudit, the manuscript proves that the worst-case syndrome-resolving budget for δ erased sites is exactly 2δ whenever 0 ≤ δ < d. This includes degenerate codes. The result concerns the number of independent p-valued outcomes in the stated noiseless measurement model. The work is directly connected to the measurement-reduction setting studied by Ryutaroh Matsumoto in “Reducing measurements in quantum erasure correction by quantum local recovery,” Computational and Applied Mathematics, DOI: 10.1007/s40314-026-03881-4. It addresses the relationship between erasure-dependent measurement costs and code invariants, together with efficient selection of sufficient surface-code checks. The manuscript presents four connected results: Exact paired-column rank profile. For any matrix G over any field whose columns are partitioned into n labeled pairs, let r be its rank and ν the largest number of complete pairs whose columns are jointly independent. The maximum projected rank over all sets of δ pairs is exactly R(δ) = min{2δ, δ + ν, r}. A single ordering of the pairs realizes every maximum through its successive prefixes. The full profile therefore follows from two invariants, rather than requiring a separate optimization at every erasure size. Distance bounds and the exact worst-case syndrome budget. For an isotropic stabilizer space with k > 0, every inclusionwise maximal complete-pair independent set A satisfies d ≤ |A| + 1. Consequently, ν ≥ d − 1, yielding the exact 2δ worst-case budget below distance. The argument also gives a constructive extension property: every independent-pair set smaller than d − 1 can be enlarged by adding another pair. Particular correctable erasure patterns can require fewer outcomes because of stabilizers supported inside the erased region. Checkable CSS certificates. For Calderbank–Shor–Steane codes, complete-pair independence becomes simultaneous column independence in the X-check and Z-check matrices. Classical matroid intersection supplies an exact rank-partition certificate for ν. A matching independent set and partition provide lower and upper certificates that can be verified using finite-field elimination. Optimal native surface-code check selection. For binary check matrices with at most two nonzero entries per column, the restricted matrix is represented by a sparse graph with boundary pins. Connected components determine a minimum-cardinality native check basis in O(v_I + e_I) time. A pinned component retains every check; an unpinned component omits one check. With nonnegative additive costs, omitting a most expensive check in each unpinned component also minimizes total cost. The two CSS check systems are processed independently. The accompanying verification uses exact arithmetic over GF(2), GF(3), and GF(5). The algebra audit covers 120 general paired matrices and 12,276 subset-rank evaluations, including exact distance checks for 137 positive-logical-dimension code instances and 401 maximal-set distance checks. The graph audit covers 1,785 exhaustive sparse column multisets, 5,355 matrix/cost combinations, 500 additional seeded instances, and named boundary regressions. Both audit programs passed again during preparation of the release. These finite checks support implementation correctness; the general mathematical statements are supported by the supplied proofs. The release includes the complete manuscript, editable LaTeX source, Python native-check selector, exact audit programs, recorded results, generator data, figures, reproduction instructions, and an explicit claim-status record. The selector and audits require only Python 3.10 or later and its standard library; matplotlib is needed only to regenerate the figure. The results apply to independent syndrome information and classical check selection. They do not establish bounds for arbitrary coherent recovery, noisy fault-tolerant extraction, physical circuit depth, or one fixed measurement schedule that must serve every erasure pattern. At or above distance, the general formula remains an algebraic rank profile rather than a universal recovery guarantee. 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