This program is tentative and subject to change.

Thu 18 Jul 2024 11:36 - 11:54 at Baobá 2 - Program Analysis and Performance 2

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.

This program is tentative and subject to change.

Thu 18 Jul

Displayed time zone: Brasilia, Distrito Federal, Brazil change

11:00 - 12:30
Program Analysis and Performance 2Research Papers at Baobá 2
11:00
18m
Talk
Adapting Multi-objectivized Software Configuration Tuning
Research Papers
Tao Chen University of Birmingham, Miqing Li University of Birmingham
Pre-print
11:18
18m
Talk
Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?
Research Papers
Madeline Endres University of Michgain, Sarah Fakhoury Microsoft Research, Saikat Chakraborty Microsoft Research, Shuvendu Lahiri Microsoft Research
11:36
18m
Talk
Analyzing Quantum Programs with LintQ: A Static Analysis Framework for Qiskit
Research Papers
Matteo Paltenghi University of Stuttgart, Michael Pradel University of Stuttgart
Pre-print
11:54
18m
Talk
Abstraction-Aware Inference of Metamorphic Relations
Research Papers
Agustin Nolasco University of Rio Cuarto, Facundo Molina IMDEA Software Institute, Renzo Degiovanni Luxembourg Institute of Science and Technology, Alessandra Gorla IMDEA Software Institute, Diego Garbervetsky Departamento de Computación, FCEyN, UBA, Mike Papadakis University of Luxembourg, Sebastian Uchitel Imperial College and University of Buenos Aires, Nazareno Aguirre University of Rio Cuarto and CONICET, Marcelo F. Frias Dept. of Software Engineering Instituto Tecnológico de Buenos Aires
12:12
18m
Talk
Predicting Configuration Performance in Multiple Environments with Sequential Meta-Learning
Research Papers
Jingzhi Gong Loughborough University, Tao Chen University of Birmingham
Pre-print