Analyzing Quantum Programs with LintQ: A Static Analysis Framework for Qiskit

TL;DR

LintQ is a static analysis framework designed to detect quantum-specific bugs in Qiskit programs, significantly improving bug detection accuracy over existing tools by leveraging abstractions of quantum concepts.

Contribution

The paper introduces LintQ, a novel extensible static analysis framework that effectively detects quantum programming bugs using abstractions of quantum concepts, outperforming prior tools.

Findings

LintQ achieves 91.0% precision in bug detection.

Almost 92.1% of bugs found by LintQ are missed by previous tools.

Applied to 7,568 real-world quantum programs, LintQ effectively identifies diverse bugs.

Abstract

As quantum computing is rising in popularity, the amount of quantum programs and the number of developers writing them are increasing rapidly. Unfortunately, writing correct quantum programs is challenging due to various subtle rules developers need to be aware of. Empirical studies show that 40-82% of all bugs in quantum software are specific to the quantum domain. Yet, existing static bug detection frameworks are mostly unaware of quantum-specific concepts, such as circuits, gates, and qubits, and hence miss many bugs. This paper presents LintQ, a comprehensive static analysis framework for detecting bugs in quantum programs. Our approach is enabled by a set of abstractions designed to reason about common concepts in quantum computing without referring to the details of the underlying quantum computing platform. Built on top of these abstractions, LintQ offers an extensible set of ten analyses that detect likely bugs, such as operating on corrupted quantum states, redundant measurements, and incorrect compositions of sub-circuits. We apply the approach to a newly collected dataset of 7,568 real-world Qiskit-based quantum programs, showing that LintQ effectively identifies various programming problems, with a precision of 91.0% in its default configuration with the six best performing analyses. Comparing to a general-purpose linter and two existing quantum-aware techniques shows that almost all problems (92.1%) found by LintQ during our evaluation are missed by prior work. LintQ hence takes an important step toward reliable software in the growing field of quantum computing.