Xiaoyu Guo

Testing quantum programs in a black-box setting is difficult because bug-relevant behavioral differences may be missed at two levels: the chosen inputs may fail to trigger faulty behavior, and even when faults are triggered, the chosen measurements may fail to make the resulting differences observable. Existing methods have mainly strengthened either input generation or measurement design in isolation, leaving their coupling insufficiently addressed. In this paper, we present QDoctor, a probe-based black-box testing framework that wraps the program under test with a variety of property-based probes, thereby effectively extracting program information and exploring its behavior. A probe integrates two components: test-case generation and the specification of the measurement basis. To reduce false positive rate, QDoctor also supports semantic-aware partial equivalence testing through constrained probe-oracle design. Experimental results on benchmark suites show that QDoctor achieves the best balanced performance among representative black-box baselines, reaching 99.89% recall, 98.23% precision, 99.06% F1, and 0.9988 AUC. Ablation shows that jointly strengthening inputs and measurements raises recall from 85.280% to 99.728% with zero false positives, while semantic probes drive the false-positive rate to zero on SWAP-induced equivalent mutants.

Large Language Models (LLMs) have already reshaped many aspects of traditional software engineering, from code generation and debugging to automated testing and maintenance. As an important research sub-domain in the software engineering area, quantum software engineering (QSE) is poised to undergo a similar transformation. As QSE moves toward broader adoption, including the generation of quantum code, program testing, and automated program repair, LLMs will likely play a key supporting role in its development. However, despite encouraging early attempts, the community still lacks a roadmap for using LLMs in QSE. Without such a roadmap, opportunities may be missed, challenges may remain unrevealed, and risks may be overlooked. In this article, we present our vision for how LLMs can advance QSE, focusing on four core tasks: program modeling and analysis, program generation, testing, and repair. We outline the opportunities and unique challenges, identify the risks inherent in this integration, and propose a research agenda to guide the community toward realizing the full potential of LLM-powered QSE.

This paper presents the first concolic testing framework explicitly designed for quantum programs. The framework introduces quantum constraint generation methods for quantum control statements that quantify quantum states and offers a symbolization method for quantum variables. Based on this framework, we generate path constraints for each concrete execution path of a quantum program. These constraints guide the exploration of new paths, with a quantum constraint solver determining outcomes to create novel input samples, thereby enhancing branch coverage. Our framework has been implemented in Python and integrated with Qiskit for practical evaluation. Experimental results show that our concolic testing framework improves branch coverage, generates high-quality quantum input samples, and detects bugs, demonstrating its effectiveness and efficiency in quantum programming and bug detection. Regarding branch coverage, our framework achieves more than 74.27% on quantum programs with under 5 qubits.

Automated Program Repair (APR) is a vital area in software engineering that generates automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, quantum programming lacks a comparable automated repair technique. In this initial exploration, we investigate the use of ChatGPT for quantum program repair and evaluate its performance on Bugs4Q, a benchmark suite of quantum program bugs. Our findings demonstrate the feasibility of employing ChatGPT for quantum program repair. Specifically, we assess ChatGPT's ability to address bugs within the Bugs4Q benchmark, revealing its success in repairing 29 out of 38 bugs. This research represents a promising step towards automating the repair process for quantum programs.