Stochastic Optimization of Quantum Programs

Liu, Peng; Hu, Shaohan; Pistoia, Marco; Chen, ChunFu Richard; Gambetta, Jay

doi:10.1109/mc.2019.2909711

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Given their importance on the execution and result of a QP, as a simple typo on the name of the gate could cause bugs that developers may not be aware of, our set of mutation operators to generate faulty versions of QPs is based on single-and multi-qubit quantum gates, in particular, syntactically equivalent gates. We argue that our quantum mutants match real world bugs as (1) Liu et al [32] described quantum mutation to be helpful to assess the correct behavior of QPs, and (2) 3 out of the 8 common bug patterns in Qiskit programs described by Zhao et al [33] are related to quantum gates as so are the majority of our mutation operators. Nevertheless, and as part of our future work, we will investigate and develop novel quantum mutation operators based on conceptual mistakes a developer might make when developing QPs.…”

Section: A Quantum Mutation Operatorsmentioning

confidence: 84%

Mutation Testing of Quantum Programs: A Case Study With Qiskit

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Campos

Abreu

2022

IEEE Trans. Quantum Eng.

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As quantum computing is still in its infancy, there is an inherent lack of knowledge and technology to test a quantum program properly. In the classical realm, mutation testing has been successfully used to evaluate how well a program's test suite detects seeded faults (i.e., mutants). In this paper, building on the definition of syntactically equivalent quantum operations, we propose a novel set of mutation operators to generate mutants based on qubit measurements and quantum gates. To ease the adoption of quantum mutation testing, we further propose QMutPy, an extension of the well-known and fully automated opensource mutation tool MutPy. To evaluate QMutPy's performance, we conducted a case study on 24 real quantum programs written in IBM's Qiskit library. Furthermore, we show how better test suite coverage and improvements to test assertions can increase the test suites' mutation score and quality. QMutPy has proven to be an effective quantum mutation tool, providing insight into the current state of quantum tests and how to improve them.

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