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arxiv: 2604.20706 · v1 · submitted 2026-04-22 · 💻 cs.SE · cs.AI

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QuanForge: A Mutation Testing Framework for Quantum Neural Networks

Jianjun Zhao, Minqi Shao, Shangzhou Xia

Authors on Pith no claims yet

Pith reviewed 2026-05-09 23:44 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords mutation testingquantum neural networksQNN testingquantum circuitssoftware testingstatistical killingfault simulationquantum computing
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The pith

QuanForge provides a mutation testing framework for quantum neural networks using statistical killing criteria and nine operators to distinguish test suites and locate weak circuit areas.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents QuanForge as a mutation testing framework built specifically for Quantum Neural Networks to manage their inherent randomness from quantum measurements and operations. It defines a statistical mutation killing criterion that accounts for stochastic effects when deciding whether a mutant is killed by a test. The framework supplies nine post-training mutation operators acting at gate and parameter levels to model possible faults, plus an algorithm that generates effective mutants in a systematic way. Experiments across benchmark datasets and QNN architectures demonstrate that the approach can rank different test suites by effectiveness and identify vulnerable regions inside the quantum circuits. These results yield concrete guidance for improving training data and evaluating circuit structures.

Core claim

QuanForge establishes a mutation testing method for QNNs through statistical mutation killing and nine post-training mutation operators at gate and parameter levels. This enables distinguishing test suites and localizing vulnerable circuit regions, offering insights into data enhancement and structural assessment of QNNs.

What carries the argument

Statistical mutation killing criterion that accounts for randomness in mutations and measurements, together with nine gate- and parameter-level post-training mutation operators.

If this is right

  • Test suites can be compared and ranked according to how many mutants they kill.
  • Vulnerable regions inside a quantum circuit can be pinpointed for focused improvement.
  • Training data can be augmented by targeting the weaknesses revealed through mutation analysis.
  • QNN architectures can be assessed and refined using the structural insights from the mutants.
  • The framework continues to function under simulated noise levels expected on near-term quantum devices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mutation-based evaluation approach could be extended to other quantum machine learning models beyond neural networks.
  • Integration with quantum error mitigation techniques might allow the operators to reflect hardware-specific faults more closely.
  • Automated repair suggestions for QNN circuits could be derived by analyzing which mutations are hardest to kill.
  • A combined classical-quantum mutation testing pipeline might emerge for hybrid quantum-classical systems.

Load-bearing premise

The nine chosen mutation operators adequately represent the kinds of errors that actually arise in quantum circuits and the statistical killing rule correctly handles the randomness from mutations and measurements.

What would settle it

A follow-up run on real quantum hardware where the mutants produced by QuanForge fail to produce error patterns matching observed device noise or gate faults.

Figures

Figures reproduced from arXiv: 2604.20706 by Jianjun Zhao, Minqi Shao, Shangzhou Xia.

Figure 1
Figure 1. Figure 1: The overall workflow of effective mutant generation in QuanForge. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Examples of mutants generated by different mutation operators. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Mean accuracy of effective mutants generated over different circuit regions (MNIST) [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Accuracy distribution of mutants generated by MNIST and RGA operator at different depths. causing mutants more likely to deviate from the original model. To investigate this, we consider two settings for qubit range, i.e., qubits used as QNN outputs (e.g., qubits 0 and 1 for a binary task) and an equally sized random subset selected from the remaining qubits (e.g., qubits 2 and 5). The depth range is fixed… view at source ↗
read the original abstract

With the growing synergy between deep learning and quantum computing, Quantum Neural Networks (QNNs) have emerged as a promising paradigm by leveraging quantum parallelism and entanglement. However, testing QNNs remains underexplored due to their complex quantum dynamics and limited interpretability. Developing a mutation testing technique for QNNs is promising while requires addressing stochastic factors, including the inherent randomness of mutation operators and quantum measurements. To tackle these challenges, we propose QuanForge, a mutation testing framework specifically designed for QNNs. We first introduce statistical mutation killing to provide a more reliable criterion. QuanForge incorporates nine post-training mutation operators at both gate and parameter levels, capable of simulating various potential errors in quantum circuits. Finally, a mutant generation algorithm is formalized that systematically produces effective mutants, thereby enabling a robust and reliable mutation analysis. Through extensive experiments on benchmark datasets and QNN architectures, we show that QuanForge can effectively distinguish different test suites and localize vulnerable circuit regions, providing insights for data enhancement and structural assessment of QNNs. We also analyze the generation capabilities of different operators and evaluate performance under simulated noisy conditions to assess the practical feasibility of QuanForge for future quantum devices.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes QuanForge, a mutation testing framework for Quantum Neural Networks (QNNs). It introduces a statistical mutation killing criterion to handle stochasticity arising from mutation operators and quantum measurements, defines nine post-training mutation operators operating at gate and parameter levels to simulate potential circuit errors, and formalizes a mutant generation algorithm. Experiments on benchmark datasets and various QNN architectures are presented to demonstrate that the framework can distinguish different test suites, localize vulnerable circuit regions, analyze the generation capabilities of individual operators, and evaluate performance under simulated noisy conditions, thereby offering insights for data enhancement and structural assessment of QNNs.

Significance. If the experimental results hold with appropriate quantitative support and controls, this work represents a meaningful contribution to quantum software engineering by providing the first dedicated mutation testing approach tailored to QNNs. The explicit treatment of stochastic factors via the statistical killing criterion and the concrete set of post-training operators are practical strengths that could support more reliable development and validation of quantum machine learning models as hardware matures.

minor comments (2)
  1. [Abstract] The abstract contains the grammatically awkward phrase 'while requires addressing'; this should be rephrased for clarity (e.g., 'but requires addressing').
  2. A summary table listing the nine mutation operators, their levels (gate/parameter), and intended error types would improve readability and allow readers to quickly compare their effects.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of our work and for recommending minor revision. The referee's description accurately reflects the key elements of QuanForge, including the statistical mutation killing criterion, the nine post-training operators, and the experimental evaluation on benchmark QNNs. No major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity; claims rest on novel framework and experiments

full rationale

The paper introduces QuanForge as a new mutation testing framework for QNNs, defining statistical mutation killing, nine post-training operators at gate/parameter levels, and a mutant generation algorithm. Effectiveness claims are supported by experiments on benchmark datasets and architectures rather than any derivation that reduces to its own inputs by construction. No self-definitional steps, fitted inputs renamed as predictions, or load-bearing self-citations appear in the provided text. The central results (distinguishing test suites, localizing vulnerable regions) are presented as empirical outcomes of the proposed method, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The framework rests on domain assumptions about quantum stochasticity and introduces new operators and a statistical criterion without external independent validation mentioned.

axioms (1)
  • domain assumption Quantum measurements and mutations introduce inherent stochasticity that requires statistical handling for reliable testing
    Abstract explicitly identifies stochastic factors as the core challenge the framework must address.
invented entities (2)
  • Statistical mutation killing criterion no independent evidence
    purpose: Provide reliable decision on whether a mutant is killed despite randomness
    New criterion introduced to handle quantum stochasticity; no external benchmarks cited.
  • Nine post-training mutation operators no independent evidence
    purpose: Simulate various errors at gate and parameter levels in QNN circuits
    Specific set of operators proposed as part of the framework; no prior reference or independent evidence given.

pith-pipeline@v0.9.0 · 5504 in / 1368 out tokens · 59587 ms · 2026-05-09T23:44:38.317865+00:00 · methodology

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Reference graph

Works this paper leans on

62 extracted references · 35 canonical work pages · 2 internal anchors

  1. [1]

    https://quantum.cloud.ibm.com/learning/en/courses/utility-scale- quantum-computing/error-mitigation

    2024.IBM: Quantum noise and error mitigation. https://quantum.cloud.ibm.com/learning/en/courses/utility-scale- quantum-computing/error-mitigation

    2024

  2. [2]

    https://github.com/KangHaiYue/partial-QEC-QVC

    2025.Implementations of quantum noise. https://github.com/KangHaiYue/partial-QEC-QVC

    2025

  3. [3]

    https://quantum.cloud.ibm.com/docs/en/guides/build-noise-models

    2026.IBM: Build noise models. https://quantum.cloud.ibm.com/docs/en/guides/build-noise-models

    2026

  4. [4]

    https://docs.pennylane.ai/en/stable/code/api/pennylane.NoiseModel.html

    2026.PennyLane: NoiseModel. https://docs.pennylane.ai/en/stable/code/api/pennylane.NoiseModel.html

    2026

  5. [5]

    Shaukat Ali, Paolo Arcaini, Xinyi Wang, and Tao Yue. 2021. Assessing the effectiveness of input and output coverage criteria for testing quantum programs. In2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 13–23

    2021

  6. [6]

    Utkarsh Azad and Animesh Sinha. 2023. qLEET: visualizing loss landscapes, expressibility, entangling power and training trajectories for parameterized quantum circuits.Quantum Inf. Process.22, 6 (2023), 256. https://doi.org/10. 1007/S11128-023-03998-Z

    2023

  7. [7]

    PennyLane: Automatic differentiation of hybrid quantum-classical computations

    Ville Bergholm, Josh A. Izaac, Maria Schuld, Christian Gogolin, and Nathan Killoran. 2018. PennyLane: Automatic differentiation of hybrid quantum-classical computations.CoRRabs/1811.04968 (2018). arXiv:1811.04968 http: //arxiv.org/abs/1811.04968

    work page internal anchor Pith review arXiv 2018

  8. [8]

    Nature549(7671), 195–202 (2017) https://doi.org/10.1038/nature23474

    Jacob D. Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. 2017. Quantum machine learning.Nat.549, 7671 (2017), 195–202. https://doi.org/10.1038/NATURE23474

    work page doi:10.1038/nature23474 2017

  9. [9]

    Gilles Blanchard, Gyemin Lee, and Clayton Scott. 2011. Generalizing from Several Related Classification Tasks to a New Unlabeled Sample. InAdvances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain, John Shawe-Taylor, Richard ...

    2011

  10. [10]

    Marco Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J. Coles. 2022. Challenges and opportunities in quantum machine learning.Nat. Comput. Sci.2, 9 (2022), 567–576. https://doi.org/10.1038/S43588- 022-00311-3

    work page doi:10.1038/s43588- 2022

  11. [11]

    Zico Kolter

    Jeremy Cohen, Elan Rosenfeld, and J. Zico Kolter. 2019. Certified Adversarial Robustness via Randomized Smoothing. InProceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 1310–1320. ht...

    2019

  12. [12]

    Iris Cong, Soonwon Choi, and Mikhail D. Lukin. 2018. Quantum convolutional neural networks.Nature Physics15 (2018), 1273 – 1278

    2018

  13. [13]

    Evandro Chagas Ribeiro da Rosa, Eduardo Inacio Duzzioni, and Rafael de Santiago. 2025. Optimizing Gate Decomposi- tion for High-Level Quantum Programming.Quantum9 (2025), 1659. https://doi.org/10.22331/Q-2025-03-12-1659

    work page doi:10.22331/q-2025-03-12-1659 2025

  14. [14]

    Pierre-Luc Dallaire-Demers and Nathan Killoran. 2018. Quantum generative adversarial networks.CoRRabs/1804.08641 (2018). arXiv:1804.08641 http://arxiv.org/abs/1804.08641

    work page arXiv 2018

  15. [15]

    Yuxuan Du, Zhuozhuo Tu, Xiao Yuan, and Dacheng Tao. 2022. Efficient measure for the expressivity of variational quantum algorithms.Physical Review Letters128, 8 (2022), 080506

    2022

  16. [16]

    Daniel Fortunato, José Campos, and Rui Abreu. 2022. Mutation testing of quantum programs: A case study with Qiskit. IEEE Transactions on Quantum Engineering3 (2022), 1–17

    2022

  17. [17]

    Daniel Fortunato, José Campos, and Rui Abreu. 2022. QMutPy: a mutation testing tool for Quantum algorithms and applications in Qiskit. InISSTA ’22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, South Korea, July 18 - 22, 2022, Sukyoung Ryu and Yannis Smaragdakis (Eds.). ACM, 797–800. https: //doi.org/10.1145/353...

    work page doi:10.1145/3533767.3543296 2022

  18. [18]

    Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad, and Hridesh Rajan. 2023. Mutation-based Fault Localization of Deep Neural Networks. In38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023, Luxembourg, September 11-15, 2023. IEEE, 1301–1313. https://doi.org/10.1109/ASE56229.2023.00171

    work page doi:10.1109/ase56229.2023.00171 2023

  19. [19]

    Sinhué García Gil, Luis Llana Díaz, and José Ignacio Requeno Jarabo. 2024. QCRMut: Quantum Circuit Random Mutant generator tool.CoRRabs/2410.01415 (2024). https://doi.org/10.48550/ARXIV.2410.01415 arXiv:2410.01415

    work page doi:10.48550/arxiv.2410.01415 2024

  20. [20]

    Maxwell Henderson, Samriddhi Shakya, Shashindra Pradhan, and Tristan Cook. 2020. Quanvolutional neural networks: powering image recognition with quantum circuits.Quantum Mach. Intell.2, 1 (2020), 1–9. https://doi.org/10.1007/ S42484-020-00012-Y

    2020

  21. [21]

    Wassily Hoeffding. 1963. Probability inequalities for sums of bounded random variables.Journal of the American statistical association58, 301 (1963), 13–30

    1963

  22. [22]

    Shahin Honarvar, Mohammad Reza Mousavi, and Rajagopal Nagarajan. 2020. Property-based testing of quantum programs in Q#. InProceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops. 430–435. Proc. ACM Softw. Eng., Vol. 3, No. FSE, Article FSE128. Publication date: July 2026. FSE128:22 Minqi Shao, Shangzhou Xia, and Jianjun Zhao

    2020

  23. [23]

    Nargiz Humbatova, Gunel Jahangirova, and Paolo Tonella. 2021. DeepCrime: mutation testing of deep learning systems based on real faults. InISSTA ’21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, Denmark, July 11-17, 2021, Cristian Cadar and Xiangyu Zhang (Eds.). ACM, 67–78. https: //doi.org/10.1145/3460319.3464825

    work page doi:10.1145/3460319.3464825 2021

  24. [24]

    Tak Hur, Leeseok Kim, and Daniel K. Park. 2022. Quantum convolutional neural network for classical data classification. Quantum Mach. Intell.4, 1 (2022). https://doi.org/10.1007/S42484-021-00061-X

    work page doi:10.1007/s42484-021-00061-x 2022

  25. [25]

    Gunel Jahangirova and Paolo Tonella. 2020. An Empirical Evaluation of Mutation Operators for Deep Learning Systems. In13th IEEE International Conference on Software Testing, Validation and Verification, ICST 2020, Porto, Portugal, October 24-28, 2020. IEEE, 74–84. https://doi.org/10.1109/ICST46399.2020.00018

    work page doi:10.1109/icst46399.2020.00018 2020

  26. [26]

    Yue Jia and Mark Harman. 2011. An Analysis and Survey of the Development of Mutation Testing.IEEE Trans. Software Eng.37, 5 (2011), 649–678. https://doi.org/10.1109/TSE.2010.62

    work page doi:10.1109/tse.2010.62 2011

  27. [27]

    Tiancheng Jin, Shangzhou Xia, and Jianjun Zhao. 2025. NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation.arXiv preprint arXiv:2509.04763(2025)

    work page arXiv 2025

  28. [28]

    Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova, Shin Yoo, and Paolo Tonella. 2025. MuFF: Stable and Sensitive Post- training Mutation Testing for Deep Learning.CoRRabs/2501.09846 (2025). https://doi.org/10.48550/ARXIV.2501.09846 arXiv:2501.09846

    work page doi:10.48550/arxiv.2501.09846 2025

  29. [29]

    Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition.Proc. IEEE86, 11 (1998), 2278–2324. https://doi.org/10.1109/5.726791

    work page doi:10.1109/5.726791 1998

  30. [30]

    Gushu Li, Yufei Ding, and Yuan Xie. 2019. Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2019, Providence, RI, USA, April 13-17, 2019, Iris Bahar, Maurice Herlihy, Emmett Witchel, and Alvin R. Lebe...

    work page doi:10.1145/3297858.3304023 2019

  31. [31]

    Gushu Li, Li Zhou, Nengkun Yu, Yufei Ding, Mingsheng Ying, and Yuan Xie. 2020. Projection-based runtime assertions for testing and debugging quantum programs.Proceedings of the ACM on Programming Languages4, OOPSLA (2020), 1–29

    2020

  32. [32]

    Weikang Li, Zhide Lu, and Dong-Ling Deng. 2022. Quantum Neural Network Classifiers: A Tutorial.CoRR abs/2206.02806 (2022). https://doi.org/10.48550/ARXIV.2206.02806 arXiv:2206.02806

    work page doi:10.48550/arxiv.2206.02806 2022

  33. [33]

    Yuechen Li, Kai-Yuan Cai, and Beibei Yin. 2025. Preparation and Utilization of Mixed States for Testing Quantum Programs.ACM Transactions on Software Engineering and Methodology34, 8 (2025), 1–44

    2025

  34. [34]

    Peixun Long and Jianjun Zhao. 2024. Equivalence, identity, and unitarity checking in black-box testing of quantum programs.Journal of Systems and Software211 (2024), 112000

    2024

  35. [35]

    Peixun Long and Jianjun Zhao. 2024. Testing multi-subroutine quantum programs: From unit testing to integration testing.ACM Transactions on Software Engineering and Methodology33, 6 (2024), 1–61

    2024

  36. [36]

    Peixun Long and Jianjun Zhao. 2025. A black-box testing framework for oracle quantum programs.arXiv preprint arXiv:2505.07243(2025)

    work page arXiv 2025

  37. [37]

    Sirui Lu, Lu-Ming Duan, and Dong-Ling Deng. 2020. Quantum Adversarial Machine Learning.CoRRabs/2001.00030 (2020). arXiv:2001.00030 http://arxiv.org/abs/2001.00030

    work page arXiv 2020

  38. [38]

    Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang. 2018. DeepMutation: Mutation Testing of Deep Learning Systems. In29th IEEE International Symposium on Software Reliability Engineering, ISSRE 2018, Memphis, TN, USA, October 15-18, 2018, Sudipto Ghosh, Roberto Natella, Bojan Cuki...

    work page doi:10.1109/issre.2018.00021 2018

  39. [39]

    Barren plateaus in quantum neural network training landscapes,

    Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven. 2018. Barren plateaus in quantum neural network training landscapes.CoRRabs/1803.11173 (2018). arXiv:1803.11173 http://arxiv.org/abs/1803. 11173

    work page arXiv 2018

  40. [40]

    Eñaut Mendiluze, Shaukat Ali, Paolo Arcaini, and Tao Yue. 2021. Muskit: A Mutation Analysis Tool for Quantum Software Testing. In36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, Melbourne, Australia, November 15-19, 2021. IEEE, 1266–1270. https://doi.org/10.1109/ASE51524.2021.9678563

    work page doi:10.1109/ase51524.2021.9678563 2021

  41. [41]

    Andriy Miranskyy and Lei Zhang. 2019. On testing quantum programs. In2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). IEEE, 57–60

    2019

  42. [42]

    Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii. 2018. Quantum circuit learning.Physical Review A(2018)

    2018

  43. [43]

    2010.Quantum computation and quantum information

    Michael A Nielsen and Isaac L Chuang. 2010.Quantum computation and quantum information. Cambridge university press

    2010

  44. [44]

    Shyambabu Pandey, Nihar Jyoti Basisth, Tushar Sachan, Neha Kumari, and Partha Pakray. 2023. Quantum machine learning for natural language processing application.Physica A: Statistical Mechanics and its Applications627 (2023), 129123. Proc. ACM Softw. Eng., Vol. 3, No. FSE, Article FSE128. Publication date: July 2026. QuanForge: A Mutation Testing Framewor...

    2023

  45. [45]

    Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, H...

    2019

  46. [46]

    Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre. 2020. Data re-uploading for a universal quantum classifier.Quantum4 (2020), 226. https://doi.org/10.22331/Q-2020-02-06-226

    work page doi:10.22331/q-2020-02-06-226 2020

  47. [47]

    Volkoff, Andrew T

    Arthur Pesah, Marco Cerezo, Samson Wang, Tyler J. Volkoff, Andrew T. Sornborger, and Patrick J. Coles. 2020. Absence of Barren Plateaus in Quantum Convolutional Neural Networks.CoRRabs/2011.02966 (2020). arXiv:2011.02966 https://arxiv.org/abs/2011.02966

    work page arXiv 2020

  48. [48]

    Weijun Shen, Jun Wan, and Zhenyu Chen. 2018. MuNN: Mutation Analysis of Neural Networks. In2018 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS Companion 2018, Lisbon, Portugal, July 16-20, 2018. IEEE, 108–115. https://doi.org/10.1109/QRS-C.2018.00032

    work page doi:10.1109/qrs-c.2018.00032 2018

  49. [49]

    Mingrui Shi, Haozhen Situ, and Cai Zhang. 2024. Hybrid quantum neural network structures for image multi- classification.Physica Scripta99, 5 (2024), 056012

    2024

  50. [50]

    InProceedings of the 44th International Conference on Software Engineering(Pittsburgh, Pennsylvania) (ICSE ’22)

    Bing Sun, Jun Sun, Long H. Pham, and Tie Shi. 2022. Causality-Based Neural Network Repair. In44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022. ACM, 338–349. https://doi.org/10.1145/3510003.3510080

    work page doi:10.1145/3510003.3510080 2022

  51. [51]

    Eñaut Mendiluze Usandizaga, Tao Yue, Paolo Arcaini, and Shaukat Ali. 2023. Which Quantum Circuit Mutants Shall Be Used? An Empirical Evaluation of Quantum Circuit Mutations.CoRRabs/2311.16913 (2023). https://doi.org/10. 48550/ARXIV.2311.16913 arXiv:2311.16913

    work page arXiv 2023

  52. [52]

    Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, and Peixin Zhang. 2019. Adversarial sample detection for deep neural network through model mutation testing. InProceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019, Joanne M. Atlee, Tevfik Bultan, and Jon Whittle (Eds.). IEEE / ACM, 1245–...

    work page doi:10.1109/icse.2019.00126 2019

  53. [53]

    Ruixia Wang, Peng Zhao, and Haifeng Yu. 2022. Universality of universal single-qubit-gate decomposition with coherent errors.arXiv preprint arXiv:2211.00365(2022)

    work page arXiv 2022

  54. [54]

    Xinyi Wang, Paolo Arcaini, Tao Yue, and Shaukat Ali. 2021. Application of combinatorial testing to quantum programs. In2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS). IEEE, 179–188

    2021

  55. [55]

    Xinyi Wang, Paolo Arcaini, Tao Yue, and Shaukat Ali. 2021. Generating failing test suites for quantum programs with search. InInternational symposium on search based software engineering. Springer, 9–25

    2021

  56. [56]

    West, Shu Lok Tsang, Jia Shun Low, Charles D

    Maxwell T. West, Shu Lok Tsang, Jia Shun Low, Charles D. Hill, Christopher Leckie, Lloyd C. L. Hollenberg, Sarah M. Erfani, and Muhammad Usman. 2023. Towards quantum enhanced adversarial robustness in machine learning.Nat. Mac. Intell.5, 6 (2023), 581–589. https://doi.org/10.1038/S42256-023-00661-1

    work page doi:10.1038/s42256-023-00661-1 2023

  57. [57]

    Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber. 2014. Natural evolution strategies.J. Mach. Learn. Res.15, 1 (2014), 949–980. https://doi.org/10.5555/2627435.2638566

    work page doi:10.5555/2627435.2638566 2014

  58. [58]

    Huanhuan Wu, Zheng Li, Zhanqi Cui, and Jianbin Liu. 2022. GenMuNN: A mutation-based approach to repair deep neural network models.Int. J. Model. Simul. Sci. Comput.13, 2 (2022), 2341008:1–2341008:17. https://doi.org/10.1142/ S1793962323410088

    2022

  59. [59]

    Shangzhou Xia, Jianjun Zhao, Fuyuan Zhang, and Xiaoyu Guo. 2025. Quantum Concolic Testing.Proc. ACM Softw. Eng.2, ISSTA (2025), 1146–1166. https://doi.org/10.1145/3728926

    work page doi:10.1145/3728926 2025

  60. [60]

    Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.CoRRabs/1708.07747 (2017). arXiv:1708.07747 http://arxiv.org/abs/1708.07747

    work page internal anchor Pith review arXiv 2017

  61. [61]

    Jiaqi Yang, Wei Xie, and Xiaohua Xu. 2025. Stability and Generalization of Quantum Neural Networks.CoRR abs/2501.12737 (2025). https://doi.org/10.48550/ARXIV.2501.12737 arXiv:2501.12737

    work page doi:10.48550/arxiv.2501.12737 2025

  62. [62]

    Jiaming Ye, Shangzhou Xia, Fuyuan Zhang, Paolo Arcaini, Lei Ma, Jianjun Zhao, and Fuyuki Ishikawa. 2023. QuraTest: Integrating Quantum Specific Features in Quantum Program Testing. In38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023, Luxembourg, September 11-15, 2023. IEEE, 1149–1161. https://doi.org/10. 1109/ASE56229.2023...

    work page arXiv 2023