arXiv subjects

Miras Seilkhan

Publications and source records attributed to Miras Seilkhan.

2 recordsLinked to original sources

arXiv2026

Intrinsic Redundancy and Local Robustness in Finite $\beta$-Expansion Systems

Redundancy in non-standard numeration systems is often associated with robustness, but its practical value in finite digital arithmetic depends on how representation, storage, and repair are defined. We study intrinsic redundancy in finite beta-expansion systems using a bounded-window model that separates semantic non-uniqueness from canonical codebook admissibility. The model distinguishes arithmetic canonicalization from corruption repair and evaluates structural detectability, value-preserving repair of the observed state, and survival of the original value. For the golden-ratio system and related multinacci bases, we prove that a genuine single-digit corruption in a canonically injective finite codebook cannot be semantically recovered by exact repair without external information. Semantic survival under exact structural repair is possible only for localized multi-digit perturbations corresponding to algebraic rewrite identities, such as the equivalence of 100 and 011. Experiments comparing standard binary, signed-digit non-adjacent form, and multinacci systems quantify trade-offs among codebook sparsity, fault visibility, canonicalization cost, residual error, and boundary loss. The results show that intrinsic beta-redundancy is a constrained-language resource for structural digital integrity rather than a substitute for classical error-control redundancy.

cs.IT↗

arXiv2026

Comparing Classical and Quantum Variational Classifiers on the XOR Problem

Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models operate on qubits in high-dimensional Hilbert spaces and provide an alternative approach to model expressivity. We compare classical models and a variational quantum classifier on the XOR problem. Logistic regression, a one-hidden-layer multilayer perceptron, and a two-qubit variational quantum classifier with circuit depths 1 and 2 are evaluated on synthetic XOR datasets with varying Gaussian noise and sample sizes using accuracy and binary cross-entropy. Performance is determined primarily by model expressivity. Logistic regression and the depth-1 quantum circuit fail to represent XOR reliably, whereas the multilayer perceptron and the depth-2 quantum circuit achieve perfect test accuracy under representative conditions. Robustness analyses across noise levels, dataset sizes, and random seeds confirm that circuit depth is decisive for quantum performance on this task. Despite matching accuracy, the multilayer perceptron achieves lower binary cross-entropy and substantially shorter training time. Hardware execution preserves the global XOR structure but introduces structured deviations in the decision function. Overall, deeper variational quantum classifiers can match classical neural networks in accuracy on low-dimensional XOR benchmarks, but no clear empirical advantage in robustness or efficiency is observed in the examined settings.

cs.LG↗