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

A Comparative Analysis on Volatility and Scalability Properties of Blockchain Compression Protocols

Increasing popularity of trading digital assets can lead to significant delays in Blockchain networks when processing transactions. When transaction fees become miners' primary revenue, an imbalance in reward may lead to miners adopting deviant mining strategies. Scaling the block capacity is one of the potential approaches to alleviate the problem. To address this issue, this paper reviews and evaluates six state-of-the-art compression protocols for Blockchains. Specifically, we designed a Monte Carlo simulation to simulate two of the six protocols to observe their compression performance under larger block capacities. Furthermore, extensive simulation experiments were conducted to observe the mining behaviour when the block capacity is increased. Experimental results reveal an interesting trade-off between volatility and scalability. When the throughput is higher than a critical point, it worsens the volatility and threatens Blockchain security. In the experiments, we further analyzed the relationship between volatility and scalability properties with respect to the distribution of transaction values. Based on the analysis results, we proposed the recommended maximum block size for each protocol. At last, we discuss the further improvement of the compression protocols

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

Minimizing Block Incentive Volatility Through Verkle Tree-Based Dynamic Transaction Storage

Transaction fees are a crucial revenue source for miners in public and consortium blockchains. However, while public blockchains have additional revenue streams, transaction fees serve as the primary income for miners in consortium blockchains formed by various financial institutions. These miners allocate different levels of computing resources to process transactions and earn corresponding fees. Nonetheless, relying solely on transaction fees can lead to significant volatility and encourage non-standard mining behaviors, thereby posing threats to the blockchain's security and integrity. Despite previous attempts to mitigate the impact of transaction fees on illicit mining behaviors, a comprehensive solution to this vulnerability is yet to be established. To address this gap, we introduce a novel approach that leverages Dynamic Transaction Storage (DTS) strategies to effectively minimize block incentive volatility. Our solution implements a Verkle tree-based storage mechanism to reduce bandwidth consumption. Moreover, to configure the DTS strategies, we evaluate several optimization algorithms and formulate the challenge as a Vehicle Routing Problem. Our experiments conducted using historical transactions from Bitcoin and remittance data from the Industrial and Commercial Bank of China reveal that the strategy focusing on time-based transaction incorporation priority, while excluding a designated space for small-fee transactions, as discovered by the gradient-based optimizer algorithm, proves most effective in reducing volatility. Hence, the DTS strategy can sustain stable block incentives irrespective of transaction types or user bidding behavior. Furthermore, the inclusion of higher-fee transactions, often smaller in size, can alleviate propagation delays and the occurrence of forks