Quantum Software Testing: Current Trends and Emerging Proposals

Barrera, Antonio García de la; Guzmán, Ignacio García Rodriguez de; Polo, Macario; Cruz-Lemus, José A.

doi:10.1007/978-3-031-05324-5_9

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…QECC is [ [7,1,3]] Steane code protected quantum information from errors [11]. The [ [7,1,3]] Steane code mathematically corrects up to one QE or arbitrary phase error using seven qubits.…”

Section: Quantum Error Correction Code (Qecc)mentioning

confidence: 99%

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Quantum Machine Learning Techniques Integrated in Quantum Software Testing

Gutam,

Malchi

2024

Preprint

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Quantum computing (QC) is the era of the mechanisms that quantum solves complex problems faster than classical computers (CCs). Today, there are more opportunities for the public and private sectors to use high-value quantum solutions. QC relies on quantum phenomena to calculate incredible speeds and set for significant expansion. Quantum computers, QC-based Internet of Things (IoT), and communication devices to create, process, and transmit quantum states and entanglement are anticipated to enhance society’s quantum software. In this context, this paper introduces the Quantum Software Testing (QST) approach with Machine Learning (ML) techniques integrated into the Quantum Software Testing Life Cycle (QSTLC). The proposed Quantum Machine Learning Testing (QMLT) exploits ML techniques at critical stages of the QST to enhance testing efficiency, accuracy, and adaptability. Finally, the proposed automated test report generation, summarizing, and knowledge extraction technique facilitates comprehensive documentation and knowledge transfer.

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“…QECC is [ [7,1,3]] Steane code protected quantum information from errors [11]. The [ [7,1,3]] Steane code mathematically corrects up to one QE or arbitrary phase error using seven qubits.…”

Section: Quantum Error Correction Code (Qecc)mentioning

confidence: 99%

“…In our work, we initially integrate Machine learning (ML) into the QSTLC within the QC environment [3]. We introduce a novel technique applied to ML algorithms to enhance and optimize the testing for QS.…”

Section: Introductionmentioning

confidence: 99%

Quantum Machine Learning Techniques Integrated in Quantum Software Testing

Gutam,

Malchi

2024

Preprint

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Verification and Validation of Quantum Software

Fortunato,

Jiménez-Navajas,

Campos

et al. 2024

Quantum Software

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Quantum software—like classic software—needs to be designed, specified, developed, and, most importantly, tested by developers. Writing tests is a complex, error-prone, and time-consuming task. Due to the particular properties of quantum physics (e.g., superposition), quantum software is inherently more complex to develop and effectively test than classical software. Nevertheless, some preliminary works have tried to bring commonly used classical testing practices for quantum computing to assess and improve the quality of quantum programs. In this chapter, we first gather 16 quantum software testing techniques that have been proposed for the IBM quantum framework, Qiskit. Then, whenever possible, we illustrate the usage of each technique (through the proposed tool that implements it, if available) on a given running example. We showcase that although several works have been proposed to ease the burn of testing quantum software, we are still in the early stages of testing in the quantum world. Researchers should focus on delivering artifacts that are usable without much hindrance to the rest of the community, and the development of quantum benchmarks should be a priority to facilitate reproducibility, replicability, and comparison between different testing techniques.

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A systematic decision-making framework for tackling quantum software engineering challenges

2023

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Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.

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Quantum Software Testing: Current Trends and Emerging Proposals

Cited by 6 publications

References 34 publications

Quantum Machine Learning Techniques Integrated in Quantum Software Testing

Quantum Machine Learning Techniques Integrated in Quantum Software Testing

Verification and Validation of Quantum Software

A systematic decision-making framework for tackling quantum software engineering challenges

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