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

• URL: http://arxiv.org/abs/2310.00718v2
• Date: Thu, 16 May 2024 08:15:10 GMT
• Title: Analyzing Quantum Programs with LintQ: A Static Analysis Framework for Qiskit
• Authors: Matteo Paltenghi, Michael Pradel,
• Abstract summary: 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 underlying quantum computing platform. 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.
• Score: 21.351834312054844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Survey on Testing and Analysis of Quantum Software [21.351834312054844]
  We provide an extensive survey of the state of the art in testing and analysis of quantum software. We discuss literature from several research communities, including quantum computing, software engineering, programming languages, and formal methods.
  arXiv  Detail & Related papers  (2024-10-01T13:05:54Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Dynamic Runtime Assertions in Quantum Ternary Systems [1.5410557873153832]
  We investigate assertions in quantum ternary systems, which are more challenging than those in quantum binary systems. We propose quantum ternary circuit designs to assert classical, entanglement, and superposition states.
  arXiv  Detail & Related papers  (2023-12-23T17:46:51Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• Static Entanglement Analysis of Quantum Programs [1.7704011486040847]
  Entangling information has important implications for understanding the behavior of quantum programs. This paper presents the first static entanglement analysis method for quantum programs developed in the practical quantum programming language Q#.
  arXiv  Detail & Related papers  (2023-04-11T08:18:39Z)
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation [5.381727213688375]
  We are still a long way from reaching the maturity of a full-fledged quantum computer. An outstanding challenge is to come up with an application that can reliably carry out a nontrivial task. Several near-term quantum computing techniques have been proposed to characterize and mitigate errors.
  arXiv  Detail & Related papers  (2022-11-16T07:53:15Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Quantum simulation and computing with Rydberg-interacting qubits [0.0]
  We give an overview of the Rydberg quantum toolbox, emphasizing the high degree of flexibility for encoding qubits. We then review the state-of-the-art concerning high-fidelity quantum operations and logic gates. We discuss computing schemes that are particularly suited to the Rydberg platform.
  arXiv  Detail & Related papers  (2020-11-05T18:44:11Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• QUANTIFY: A framework for resource analysis and design verification of quantum circuits [69.43216268165402]
  QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits. It is based on Google Cirq and is developed with Clifford+T circuits in mind. For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
  arXiv  Detail & Related papers  (2020-07-21T15:36:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.