On Repairing Quantum Programs Using ChatGPT

Automated Program Repair (APR) is a vital area in software engineering aimed at generating automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, the realm of 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.Comment: The 5th International Workshop on Quantum Software Engineering (Q-SE 2024

QChecker: Detecting Bugs in Quantum Programs via Static Analysis

Static analysis is the process of analyzing software code without executing the software. It can help find bugs and potential problems in software that may only appear at runtime. Although many static analysis tools have been developed for classical software, due to the nature of quantum programs, these existing tools are unsuitable for analyzing quantum programs. This paper presents QChecker, a static analysis tool that supports finding bugs in quantum programs in Qiskit. QChecker consists of two main modules: a module for extracting program information based on abstract syntax tree (AST), and a module for detecting bugs based on patterns. We evaluate the performance of QChecker using the Bugs4Q benchmark. The evaluation results show that QChecker can effectively detect various bugs in quantum programs.Comment: This paper will be appeared in the proceedings of the 4th International Workshop on Quantum Software Engineering (Q-SE 2023) on May 14, 202

An Empirical Study of Bugs in Quantum Machine Learning Frameworks

Quantum computing has emerged as a promising domain for the machine learning (ML) area, offering significant computational advantages over classical counterparts. With the growing interest in quantum machine learning (QML), ensuring the correctness and robustness of software platforms to develop such QML programs is critical. A necessary step for ensuring the reliability of such platforms is to understand the bugs they typically suffer from. To address this need, this paper presents the first comprehensive study of bugs in QML frameworks. We inspect 391 real-world bugs collected from 22 open-source repositories of nine popular QML frameworks. We find that 1) 28% of the bugs are quantum-specific, such as erroneous unitary matrix implementation, calling for dedicated approaches to find and prevent them; 2) We manually distilled a taxonomy of five symptoms and nine root cause of bugs in QML platforms; 3) We summarized four critical challenges for QML framework developers. The study results provide researchers with insights into how to ensure QML framework quality and present several actionable suggestions for QML framework developers to improve their code quality.Comment: This paper will be appeared in the proceedings of the 2023 IEEE International Conference on Quantum Software (QSW 2023), July 2-8, 202

Time domain analysis method for aerodynamic noises from wind turbine blades

The issue of the aerodynamic noises from wind turbine blades affecting the surrounding residents life begins to attract researcher's attention. Most of the existing researches are based on CFD software or experimental data fitting method to analyze the aerodynamic noises, so it is difficult to adapt the demand to dynamic analysis of the aerodynamic noises from wind speed variation. In this paper, the operation parameters, the inflow wind speed and the receiver location are considered, and a modified model to calculate aerodynamic noises from wind turbine blades which is based on traditional acoustic formulas is established. The program to calculate the aerodynamic noises from the 2 MW wind turbine blades is compiled using a time-domain analysis method based on the Simulink modular in Matlab software. And the pressure time sequence diagrams of the aerodynamic noises from wind turbine blades are drawn. It has provided a theoretical foundation to develop low noise wind turbine blades

An abscisic acid (ABA) homeostasis regulated by its production, catabolism and transport in peanut leaves in response to drought stress.

ABA is an important messenger that acts as a signaling mediator for regulating the adaptive response of plants to drought stress. Two production pathways, de novo biosynthesis and hydrolysis of glucose-conjugated ABA by β-glucosidase (BG), increase cellular ABA levels in plants. ABA catabolism via hydroxylation by 8'-hydroxylase (CYP707A), or conjugation by uridine diphosphate glucosyltransferase (UGT), decreases cellular ABA levels. The transport of ABA through ATP-binding cassette (ABC)-containing transporter proteins, members of ABC transporter G family (ABCG), across plasma membrane (PM) is another important pathway to regulate cellular ABA levels. In this study, based on our previously constructed transcriptome of peanut leaves in response to drought stress, fourteen candidate genes involved in ABA production (including AhZEP, AhNCED1 and AhNCED3, AhABA2, AhAAO1 and AhAAO2, AhABA3, AhBG11 and AhBG24), catabolism (including AhCYP707A3, AhUGT71K1 and AhUGT73B4) and transport (including AhABCG22-1 and AhABCG22-2), were identified homologously and phylogenetically, and further analyzed at the transcriptional level by real-time RT-PCR, simultaneously determining ABA levels in peanut leaves in response to drought. The high sequence identity and very similar subcellular localization of the proteins deduced from 14 identified genes involved in ABA production, catabolism and transport with the reported corresponding enzymes in databases suggest their similar roles in regulating cellular ABA levels. The expression analysis showed that the transcripts of AhZEP, AhNCED1, AhAAO2 and AhABA3 instead of AhABA2, AhNCED3 and AhAAO1 in peanut leaves increased significantly in response to drought stress; and that the AhBG11 and AhBG24 mRNA levels were rapidly and significantly up-regulated, with a 4.83- and 4.58-fold increase, respectively at 2-h of drought stress. The genes involved in ABA catabolism AhCYP707A3, AhUGT71K1 instead of AhUGT73B4 were significantly induced in response to drought stress. The expression of two closely related peanut ABCG genes, AhABCG22.1 and AhABCG22.2, was significantly up-regulated in response to drought stress. The ABA levels rapidly began to accumulate within 2 h (a 56.6-fold increase) from the start of drought stress, and peaked at 10 h of the stress. The highly and rapidly stress up-regulated expressions of genes involved in ABA production and transport, particularly AhNCED1, AhBG11 and AhBG24, and AhABCG22.1 and AhABCG22.2, might contribute to the rapid ABA accumulation in peanut leaves in response to drought. In response to drought stress, ABA accumulation levels in peanut leaves agree well with the up-regulated expressions of ABA-producing genes (AhZEP, AhNCED1, AhAAO2, AhABA3, AhBG11 and AhBG24) and PM-localized ABA importer genes (AhABCG22-1 and AhABCG22-2), in spite of the simultaneously induced ABA catabolic genes (AhCYP707A3 and AhUGT71K1), although the induction of catabolic genes was much lower than that of biosynthetic gene (AhNCED1). This difference in induction kinetics of gene expression may define the significant accumulation of drought-induced ABA levels. These results suggest that ABA homeostasis in peanut leaves in response to drought maintained through a balance between the production, catabolism and transport, rather than simply by the biosynthesis

In Situ Photodeposition of Cobalt Phosphate (CoH_xPO_y) on CdIn₂S₄ Photocatalyst for Accelerated Hole Extraction and Improved Hydrogen Evolution

The ternary metal sulfide CdIn2S4 (CIS) has great application potential in solar-to-hydrogen conversion due to its suitable band gap, good stability and low cost. However, the photocatalytic hydrogen (H2) evolution performance of CIS is severely limited by the rapid electron–hole recombination originating from the slow photogenerated hole transfer kinetics. Herein, by simply depositing cobalt phosphate (CoHxPOy, noted as Co-Pi), a non-precious co-catalyst, an efficient pathway for accelerating the hole transfer process and subsequently promoting the H2 evolution reaction (HER) activity of CIS nanosheets is developed. X-ray photoelectron spectroscopy (XPS) reveals that the Co atoms of Co-Pi preferentially combine with the unsaturated S atoms of CIS to form Co-S bonds, which act as channels for fast hole extraction from CIS to Co-Pi. Electron paramagnetic resonance (EPR) and time-resolved photoluminescence (TRPL) showed that the introduction of Co-Pi on ultrathin CIS surface not only increases the probability of photogenerated holes arriving the catalyst surface, but also prolongs the charge carrier’s lifetime by reducing the recombination of electrons and holes. Therefore, Co-Pi/CIS exhibits a satisfactory photocatalytic H2 evolution rate of 7.28 mmol g−1 h−1 under visible light, which is superior to the pristine CIS (2.62 mmol g−1 h−1) and Pt modified CIS (3.73 mmol g−1 h−1)