Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE/ACM International Conference on Automated Software Engineering (ASE)

QuraTest: Integrating Quantum Specific Features in Quantum Program Testing

Jiaming Ye, Shangzhou Xia, Fuyuan Zhang, Paolo Arcaini, Lei Ma, Jianjun Zhao, Fuyuki Ishikawa

Published Sep 11, 2023• 25 citations
Publisher

Abstract

The recent fast development of quantum computers breaks several computation limitations that are difficult for conventional computers. Up to the present, although many approaches and tools have been proposed to test quantum programs, the fundamental features of quantum programs, i.e., magnitude, phase, and entanglement, have been largely overlooked, leading to limited fault detection capability and reduced testing effectiveness. To address this problem, we propose an automated testing framework named QURATEST, equipped with three test case generators (including two newly proposed techniques, UCNOT and IQFT in this paper, as well as one based on Random techniques) to test quantum programs. Overall, the proposed generators enable the generation of diverse test inputs by considering the quantum features of quantum programs. In the experiments, we perform an in-depth evaluation of QURATEST from three aspects: generated test case diversity, output coverage of the program under test, and fault detection capability. The results demonstrate the potential of our newly proposed techniques in that IQFT can generate the most diverse test cases regarding magnitude, phase, and entanglement, with 66% cell coverage. Comparatively, the Random approach only has 10% cell coverage. Regarding the evaluations of the output coverage, IQFT can achieve the highest output coverage in 70.2% (33 out of 47) of all quantum programs. In terms of fault detection, UCNOT outperforms the other two techniques. Specifically, the test cases generated by UCNOT have the best mutation score in 88.4% (23 out of 26) quantum programs.

DOI
10.1109/ase56229.2023.00196

Related papers

Mutation Testing of Quantum Programs: A Case Study With Qiskit

Daniel Fortunato, José Campos, Rui Abreu

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 open-source 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.

IEEE Transactions on Quantum Engineering•Jan 1, 2022•62 citations

NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation

Tiancheng Jin, Shangzhou Xia, Jianjun Zhao

Quantum programs are designed to run on quantum computers, leveraging quantum circuits to solve problems that are intractable for classical machines. As quantum computing advances, ensuring the reliability of quantum programs has become increasingly important. This paper introduces NovaQ, a diversity-guided testing framework for quantum programs. NovaQ combines a distribution-based test case generator with a novelty-driven evaluation module. The generator produces diverse quantum state inputs by mutating circuit parameters, while the evaluator quantifies behavioral novelty based on internal circuit state metrics, including magnitude, phase, and entanglement. By selecting inputs that map to infrequently covered regions in the metric space, NovaQ effectively explores under-tested program behaviors. We evaluate NovaQ on quantum programs of varying sizes and complexities. Experimental results show that NovaQ consistently achieves higher test input diversity and detects more bugs than existing baseline approaches.

arXiv:2509.04763•IEEE/ACM International Conference on Automated Software Engineering (ASE)•Nov 16, 2025•1 citation

Application of Combinatorial Testing to Quantum Programs

Xinyi Wang, Paolo Arcaini, Tao Yue +1 more

The capability of Quantum Computing (QC) in solving complex problems has been increasingly recognized. However, similar to classical computing, to fully exploit QC's potential, it is important to ensure the correctness of quantum programs. Doing so via software testing is, however, very challenging because of QC's inherent properties: superposition and entanglement. Towards the direction of ensuring the correctness of quantum programs, we propose an approach called QuCAT (QUantum CombinAtorial Testing) for systematic and automated testing of quantum programs by benefiting from combinatorial testing, which has been proven to be cost-effective in testing classical programs. QuCAT supports two combinatorial test suite generation scenarios, i.e., generating combinatorial test suites of a given strength, and incrementally generating and executing combinatorial test suites of increasing strength until a fault is found. The approach employs two types of test oracles to assess test results. We performed an empirical study with 18 faulty versions of quantum programs to evaluate QuCAT with strengths of two, three, and four in the two test generation scenarios. We compare the cost-effectiveness of combinatorial testing of various strengths and random testing (taken as baseline approach). Results show that combinatorial testing always performs better than random testing with the same cost and finds faults more quickly (in terms of required number of test cases). In addition, in most cases, combinatorial testing with a higher strength outperforms the lower strength in terms of effectiveness.

IEEE International Conference on Software Quality, Reliability and Security (QRS)•Dec 1, 2021•44 citations

Randomized and Diverse Input State Generation for Quantum Program Testing

Maryse Ernzer, Seung Yeob Shin, Fabrizio Pastore +1 more

With the accelerating development of quantum technologies and their growing computational potential, quantum systems are being adapted for simulations and other critical tasks across diverse domains, making the reliability of the corresponding quantum software an essential concern. Although recent efforts have started to incorporate quantum-specific properties such as magnitude, phase, and entanglement under the form of input-coverage criteria into software testing, the unique structure of the quantum state space demands for more comprehensive testing. In particular, the notion of complete state-space exploration has so far received little attention. To address this gap, we propose a framework for evaluating test circuit generators with respect to their coverage of the quantum state space. Our contribution is threefold: we develop a set of diversity scores that capture both local and global indicators of the extent to which the state space is explored; we propose a test circuit generator that produces test input states via a Brick-Circuit (BC) construction designed to approximate ideal random states using hardware-compatible gates; we compare the proposed construction with existing generators based on their ability to generate uniformly distributed random test input states. Our extended diversity scores quantify the local correlations and global spread of magnitude, phase and entanglement. Using these scores, we evaluate the expressibility, defined as the capability to span the quantum state space uniformly, and entangling capabilities of existing generators relative to the BC generator. Our results show that the hardware-compatible BC generator achieves higher expressibility and entanglement performance at shallower depths than existing circuit generators.

arXiv:2605.03957•May 5, 2026•0 citations

Assessing the Effectiveness of Input and Output Coverage Criteria for Testing Quantum Programs

Shaukat Ali, Paolo Arcaini, Xinyi Wang +1 more

Quantum programs implement quantum algorithms solving complex computational problems. Testing such programs is challenging due to the inherent characteristics of Quantum Computing (QC), such as the probabilistic nature and computations in superposition. However, automated and systematic testing is needed to ensure the correct behavior of quantum programs. To this end, we present an approach called Quito (QUantum InpuT Output coverage) consisting of three coverage criteria defined on the inputs and outputs of a quantum program, together with their test generation strategies. Moreover, we define two types of test oracles, together with a procedure to determine the passing and failing of test suites with statistical analyses. To evaluate the cost-effectiveness of the three coverage criteria, we conducted experiments with five quantum programs. We used mutation analysis to determine the coverage criteria' effectiveness and cost in terms of the number of test cases. Based on the results of mutation analysis, we also identified equivalent mutants for quantum programs.

IEEE International Conference on Software Testing, Verification and Validation (ICST)•Apr 1, 2021•82 citations

Testing Quantum Programs with Multiple Subroutines

Peixun Long, Jianjun Zhao

Errors in quantum programs are challenging to track down due to the uncertainty of quantum programs. Testing is, therefore, an indispensable method for assuring the quality of quantum software. Existing testing methods focus only on testing quantum programs with quantum circuits or single subroutines and, therefore, cannot effectively test quantum programs with multi-subroutines. In this paper, we first discuss several critical issues that must be considered when testing multi-subroutine quantum programs and point out the limitations and problems with existing testing methods. We then present a novel framework for testing multi-subroutine quantum programs that allow for both unit and integration testing. Our framework includes two novel test coverage criteria for the equivalent class partition of quantum variables to guide our testing tasks and techniques to test quantum programs with several common patterns. We also discuss how to generate test cases based on our framework. To evaluate the effectiveness of our testing framework, we implemented a tool called QSharpTester for testing Q\# programs with multiple subroutines. We used it to conduct experiments on hundreds of mutation programs deriving from seven original Q\# programs. The experimental results show that our testing methods can deal with broader types of quantum programs than existing ones and perform well on almost all faulty mutation programs.

arXiv:2208.09206•Aug 19, 2022•4 citations
The recent fast development of quantum computers breaks several computation limitations that are difficult for conventional computers. Up to the present, although many approaches and tools have been proposed to test quantum programs, the fundamental features of quantum programs, i.e., magnitude, phase, and entanglement, have been largely overlooked, leading to limited fault detection capability and reduced testing effectiveness. To address this problem, we propose an automated testing framework named QURATEST, equipped with three test case generators (including two newly proposed techniques, UCNOT and IQFT in this paper, as well as one based on Random techniques) to test quantum programs. Overall, the proposed generators enable the generation of diverse test inputs by considering the quantum features of quantum programs. In the experiments, we perform an in-depth evaluation of QURATEST from three aspects: generated test case diversity, output coverage of the program under test, and fault detection capability. The results demonstrate the potential of our newly proposed techniques in that IQFT can generate the most diverse test cases regarding magnitude, phase, and entanglement, with 66% cell coverage. Comparatively, the Random approach only has 10% cell coverage. Regarding the evaluations of the output coverage, IQFT can achieve the highest output coverage in 70.2% (33 out of 47) of all quantum programs. In terms of fault detection, UCNOT outperforms the other two techniques. Specifically, the test cases generated by UCNOT have the best mutation score in 88.4% (23 out of 26) quantum programs.
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 open-source 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.
Quantum programs are designed to run on quantum computers, leveraging quantum circuits to solve problems that are intractable for classical machines. As quantum computing advances, ensuring the reliability of quantum programs has become increasingly important. This paper introduces NovaQ, a diversity-guided testing framework for quantum programs. NovaQ combines a distribution-based test case generator with a novelty-driven evaluation module. The generator produces diverse quantum state inputs by mutating circuit parameters, while the evaluator quantifies behavioral novelty based on internal circuit state metrics, including magnitude, phase, and entanglement. By selecting inputs that map to infrequently covered regions in the metric space, NovaQ effectively explores under-tested program behaviors. We evaluate NovaQ on quantum programs of varying sizes and complexities. Experimental results show that NovaQ consistently achieves higher test input diversity and detects more bugs than existing baseline approaches.
The capability of Quantum Computing (QC) in solving complex problems has been increasingly recognized. However, similar to classical computing, to fully exploit QC's potential, it is important to ensure the correctness of quantum programs. Doing so via software testing is, however, very challenging because of QC's inherent properties: superposition and entanglement. Towards the direction of ensuring the correctness of quantum programs, we propose an approach called QuCAT (QUantum CombinAtorial Testing) for systematic and automated testing of quantum programs by benefiting from combinatorial testing, which has been proven to be cost-effective in testing classical programs. QuCAT supports two combinatorial test suite generation scenarios, i.e., generating combinatorial test suites of a given strength, and incrementally generating and executing combinatorial test suites of increasing strength until a fault is found. The approach employs two types of test oracles to assess test results. We performed an empirical study with 18 faulty versions of quantum programs to evaluate QuCAT with strengths of two, three, and four in the two test generation scenarios. We compare the cost-effectiveness of combinatorial testing of various strengths and random testing (taken as baseline approach). Results show that combinatorial testing always performs better than random testing with the same cost and finds faults more quickly (in terms of required number of test cases). In addition, in most cases, combinatorial testing with a higher strength outperforms the lower strength in terms of effectiveness.
With the accelerating development of quantum technologies and their growing computational potential, quantum systems are being adapted for simulations and other critical tasks across diverse domains, making the reliability of the corresponding quantum software an essential concern. Although recent efforts have started to incorporate quantum-specific properties such as magnitude, phase, and entanglement under the form of input-coverage criteria into software testing, the unique structure of the quantum state space demands for more comprehensive testing. In particular, the notion of complete state-space exploration has so far received little attention. To address this gap, we propose a framework for evaluating test circuit generators with respect to their coverage of the quantum state space. Our contribution is threefold: we develop a set of diversity scores that capture both local and global indicators of the extent to which the state space is explored; we propose a test circuit generator that produces test input states via a Brick-Circuit (BC) construction designed to approximate ideal random states using hardware-compatible gates; we compare the proposed construction with existing generators based on their ability to generate uniformly distributed random test input states. Our extended diversity scores quantify the local correlations and global spread of magnitude, phase and entanglement. Using these scores, we evaluate the expressibility, defined as the capability to span the quantum state space uniformly, and entangling capabilities of existing generators relative to the BC generator. Our results show that the hardware-compatible BC generator achieves higher expressibility and entanglement performance at shallower depths than existing circuit generators.
Quantum programs implement quantum algorithms solving complex computational problems. Testing such programs is challenging due to the inherent characteristics of Quantum Computing (QC), such as the probabilistic nature and computations in superposition. However, automated and systematic testing is needed to ensure the correct behavior of quantum programs. To this end, we present an approach called Quito (QUantum InpuT Output coverage) consisting of three coverage criteria defined on the inputs and outputs of a quantum program, together with their test generation strategies. Moreover, we define two types of test oracles, together with a procedure to determine the passing and failing of test suites with statistical analyses. To evaluate the cost-effectiveness of the three coverage criteria, we conducted experiments with five quantum programs. We used mutation analysis to determine the coverage criteria' effectiveness and cost in terms of the number of test cases. Based on the results of mutation analysis, we also identified equivalent mutants for quantum programs.
Errors in quantum programs are challenging to track down due to the uncertainty of quantum programs. Testing is, therefore, an indispensable method for assuring the quality of quantum software. Existing testing methods focus only on testing quantum programs with quantum circuits or single subroutines and, therefore, cannot effectively test quantum programs with multi-subroutines. In this paper, we first discuss several critical issues that must be considered when testing multi-subroutine quantum programs and point out the limitations and problems with existing testing methods. We then present a novel framework for testing multi-subroutine quantum programs that allow for both unit and integration testing. Our framework includes two novel test coverage criteria for the equivalent class partition of quantum variables to guide our testing tasks and techniques to test quantum programs with several common patterns. We also discuss how to generate test cases based on our framework. To evaluate the effectiveness of our testing framework, we implemented a tool called QSharpTester for testing Q\# programs with multiple subroutines. We used it to conduct experiments on hundreds of mutation programs deriving from seven original Q\# programs. The experimental results show that our testing methods can deal with broader types of quantum programs than existing ones and perform well on almost all faulty mutation programs.