A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection

• URL: http://arxiv.org/abs/2504.18955v2
• Date: Wed, 30 Apr 2025 17:10:50 GMT
• Title: A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection
• Authors: Antonio Trovato, Martin Beseda, Dario Di Nucci,
• Abstract summary: This work envisions the usage of Quantum Approximate Optimization Algorithms (QAOAs) for test case selection.<n>QAOAs merge the potential of gate-based quantum machines with the optimization capabilities of the adiabatic evolution.<n>Our results show that QAOAs perform better than the baseline algorithms in effectiveness while being comparable to SelectQA in terms of efficiency.
• Score: 2.1929683225837078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Regression testing is key in verifying that software works correctly after changes. However, running the entire regression test suite can be impractical and expensive, especially for large-scale systems. Test suite optimization methods are highly effective but often become infeasible due to their high computational demands. In previous work, Trovato et al. proposed SelectQA, an approach based on quantum annealing that outperforms the traditional state-of-the-art methods, i.e., Additional Greedy and DIV-GA, in efficiency. This work envisions the usage of Quantum Approximate Optimization Algorithms (QAOAs) for test case selection by proposing QAOA-TCS. QAOAs merge the potential of gate-based quantum machines with the optimization capabilities of the adiabatic evolution. To prove the effectiveness of QAOAs for test case selection, we preliminarily investigate QAOA-TCS leveraging an ideal environment simulation before evaluating it on real quantum machines. Our results show that QAOAs perform better than the baseline algorithms in effectiveness while being comparable to SelectQA in terms of efficiency. These results encourage us to continue our experimentation with noisy environment simulations and real quantum machines.

Related papers

• Using quantum annealing to generate test cases for cyber-physical systems [35.26972474219581]
  We propose a mutation-based approach to enhance test case generation for Cyber-Physical Systems. We use quantum annealing to identify and target critical regions of the test cases for improvement. Our approach mechanises this process into an algorithm that uses D-Wave's quantum annealer to find the solution.
  arXiv  Detail & Related papers  (2025-04-30T14:20:58Z)
• Reformulating Regression Test Suite Optimization using Quantum Annealing -- an Empirical Study [8.11562964318067]
  Regression testing ensures that software works as expected after changes are implemented.<n>Traditional test suite optimization techniques are often impractical in resource-constrained scenarios.<n>We propose reformulating the regression test case selection problem to use quantum computation techniques better.
  arXiv  Detail & Related papers  (2024-11-24T19:30:22Z)
• A coherent approach to quantum-classical optimization [0.0]
  Hybrid quantum-classical optimization techniques have been shown to allow for the reduction of quantum computational resources. We identify the coherence entropy as a crucial metric in determining the suitability of quantum states. We propose a quantum-classical optimization protocol that significantly improves on previous approaches for such tasks.
  arXiv  Detail & Related papers  (2024-09-20T22:22:53Z)
• Solving Combinatorial Optimization Problems with a Block Encoding Quantum Optimizer [0.0]
  Block ENcoding Quantum (BEQO) is a hybrid quantum solver that uses block encoding to represent the cost function.<n>Our findings confirm that BENQO performs significantly better than QAOA and competes with VQE across a variety of performance metrics.
  arXiv  Detail & Related papers  (2024-04-22T10:10:29Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems [65.268245109828]
  We compare the performance of classicals across a series of partially-randomized tasks. We focus on local zeroth-orders due to their generally favorable performance and query-efficiency on quantum systems.
  arXiv  Detail & Related papers  (2023-10-14T02:13:26Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• A Review on Quantum Approximate Optimization Algorithm and its Variants [47.89542334125886]
  The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve intractable optimization problems. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios. We conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm.
  arXiv  Detail & Related papers  (2023-06-15T15:28:12Z)
• Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection [80.28858481461418]
  Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices. Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
  arXiv  Detail & Related papers  (2021-07-11T10:56:24Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Classical Optimizers for Noisy Intermediate-Scale Quantum Devices [1.43494686131174]
  We present a collection of tunings tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices. We analyze the efficiency and effectiveness of different minimizes in a VQE case study. While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results.
  arXiv  Detail & Related papers  (2020-04-06T21:31:22Z)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.