Covering array EXtender

Torres-Jiménez, José; Acevedo-Juárez, Brenda; Ávila-George, Himer

doi:10.1016/j.amc.2021.126122

Applied Mathematics and Computation

2021

DOI: 10.1016/j.amc.2021.126122

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Covering array EXtender

José Torres-Jiménez

Brenda Acevedo-Juárez

Himer Ávila-George

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2024

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Cited by 2 publications

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“…Introduction. The study of covering arrays has been motivated by their applications in many areas: biology, medicine, agriculture, industrial processes, material designs, designs of experiments, and software testing, among others, whose references are cited in [27]. Besides their applications, covering arrays have been developed using many tools: algebraic methods, combinatorial constructions, and several computational searches.…”

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confidence: 99%

Recursive constructions for covering schemes of strength 3

Castoldi,

Martinhão,

Carmelo

et al. 2025

AMC

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Covering schemes over an abelian group were posed recently in connection with combinatorial arrays such as covering arrays, orthogonal arrays, difference matrices, and difference schemes. This work investigates recursive constructions for covering schemes throughout basic bounds, truncation, reduction, column augmentation, Roux type constructions, and block diagonal construction. Most of the results improve upper bounds and give exact classes for covering schemes of strength 3. Numerical comparison with known upper bounds is discussed in tables for small instances.

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mentioning

confidence: 99%

Recursive constructions for covering schemes of strength 3

Castoldi,

Martinhão,

Carmelo

et al. 2025

AMC

View full text Add to dashboard Cite

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Techniques for learning sparse Pauli-Lindblad noise models

van den Berg,

Wocjan

2024

Quantum

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Error-mitigation techniques such as probabilistic error cancellation and zero-noise extrapolation benefit from accurate noise models. The sparse Pauli-Lindblad noise model is one of the most successful models for those applications. In existing implementations, the model decomposes into a series of simple Pauli channels with one- and two-local terms that follow the qubit topology. While the model has been shown to accurately capture the noise in contemporary superconducting quantum processors for error mitigation, it is important to consider higher-weight terms and effects beyond nearest-neighbor interactions. For such extended models to remain practical, however, we need to ensure that they can be learned efficiently. In this work we present new techniques that accomplish exactly this. We introduce twirling based on Pauli rotations, which enables us to automatically generate single-qubit learning correction sequences and reduce the number of unique fidelities that need to be learned. In addition, we propose a basis-selection strategy that leverages graph coloring and uniform covering arrays to minimize the number of learning bases. Taken together, these techniques ensure that the learning of the extended noise models remains efficient, despite their increased complexity.

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Covering array EXtender

Cited by 2 publications

References 34 publications

Recursive constructions for covering schemes of strength 3

Recursive constructions for covering schemes of strength 3

Techniques for learning sparse Pauli-Lindblad noise models

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