MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform
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Abstract (arXiv)
As quantum computing is becoming increasingly popular, the underlying quantum computing platforms are growing both in ability and complexity. Unfortunately, testing these platforms is challenging due to the relatively small number of existing quantum programs and because of the oracle problem, i.e., a lack of specifications of the expected behavior of programs. This paper presents MorphQ, the first metamorphic testing approach for quantum computing platforms. Our two key contributions are (i) a program generator that creates a large and diverse set of valid (i.e., non-crashing) quantum programs, and (ii) a set of program transformations that exploit quantum-specific metamorphic relationships to alleviate the oracle problem. Evaluating the approach by testing the popular Qiskit platform shows that the approach creates over 8k program pairs within two days, many of which expose crashes. Inspecting the crashes, we find 13 bugs, nine of which have already been confirmed. MorphQ widens the slim portfolio of testing techniques of quantum computing platforms, helping to create a reliable software stack for this increasingly important field.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2022.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2023Fuzz4All: Universal Fuzzing with Large Language Modelscited by 88
- 2023Retromorphic Testing: A New Approach to the Test Oracle Problemcited by 2
- 2026KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Modelscited by 1
- 2026MetaSpace: Metamorphic Testing for Spatial Cognition in Embodied Agentscited by 0
- 2026KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models28 Jul 2026
- 2026MetaSpace: Metamorphic Testing for Spatial Cognition in Embodied Agents26 Jul 2026
- 2023Retromorphic Testing: A New Approach to the Test Oracle Problem10 Oct 2023
- 2023Fuzz4All: Universal Fuzzing with Large Language Models9 Aug 2023