Quantum Platforms

Matteo Paltenghi, Michael Pradel

Quantum computing platforms are susceptible to quantum-specific bugs (e.g., incorrect ordering of qubits or incorrect implementation of quantum abstractions), which are difficult to detect and require specialized expertise. The field faces challenges due to a fragmented landscape of platforms and rapid development cycles that often prioritize features over the development of robust platform testing frameworks, severely hindering the reliability of quantum software. To address these challenges, we present QITE, the first cross-platform testing framework for quantum computing platforms, which leverages QASM, an assembly-level representation, to ensure consistency across different platforms. QITE introduces the novel ITE process to generate equivalent quantum programs by iteratively (I)mporting assembly into platform representations, (T)ransforming via platform optimization and gate conversion, and (E)xporting back to assembly. It uses a crash oracle to detect failures during cross-platform transformations and an equivalence oracle to validate the semantic consistency of the final sets of assembly programs, which are expected to be equivalent by construction. We evaluate QITE on four widely-used quantum computing platforms: Qiskit, PennyLane, Pytket, and BQSKit, revealing 17 bugs, 14 of which are already confirmed or even fixed. Our results demonstrate QITE's effectiveness, its complementarity to existing quantum fuzzers in terms of code coverage, and its ability to expose bugs that have been out of reach for existing testing techniques.

Günter Mahler A, Volker A. Weberruß

Günter Mahler A, Volker A. Weberruß

Matteo Paltenghi, Michael Pradel

Quantum computing platforms are susceptible to quantum-specific bugs, such as incorrectly ordering qubits or incorrectly implementing quantum abstractions. These bugs are difficult to detect and require specialized expertise. The field faces challenges due to a fragmented landscape of platforms and rapid development cycles that often prioritize new features over thorough testing, severely hindering the reliability of quantum software. To address these challenges, we present IterTestQ, a novel cross-platform testing approach for quantum computing platforms. The key technical contribution is our novel ITE process, which generates equivalent quantum programs by iteratively (I)mporting them into platform-specific representations, (T)ransforming the program via optimizations and gate conversions, and (E)xporting the program again. To transfer programs across platforms and test cross-platform consistency, IterTestQ leverages QASM, an assembly-level representation supported by most platforms. The approach uses a crash oracle to detect failures during cross-platform transformations and an equivalence oracle to validate the semantic consistency of the generated assembly programs, which are expected to be equivalent by construction. We evaluate IterTestQ on widely-used quantum computing platforms, including Qiskit, PennyLane, Pytket, BQSKit, and Cirq, revealing 23 bugs, 17 of which are already confirmed or fixed. Our results also demonstrate that IterTestQ complements existing quantum fuzzers (covering tens of thousands of otherwise uncovered lines), is efficient (with 0.00089 seconds per generated program), and that the ITE process is crucial for its effectiveness.

Balwinder Sodhi

As the practical Quantum Computing Platforms (QCPs) rapidly become a reality, it is desirable to harness their true potential in software applications. Thus it becomes important to determine the implications of QCPs for software architecture. In this paper we present the in-depth examination of state-of-the-art QCPs for identifying all such characteristics of a QCP that are relevant from software architecture perspective. Lack of a native quantum operating system, a hard dependency on quantum algorithms, the lower level of programming abstractions are few, out of many, examples of QCP characteristics which may affect architecture of quantum software applications. Key contributions of this paper include identifying: i) The general architecture of a QCP, ii) The programming model which is typically used when developing software for a QCP, iii) Architecturally significant characteristics of QCPs and iv) The impact of these characteristics on various Quality Attributes (QAs). We show that except performance and scalability, most of the other QAs (e.g. maintainability, testability, reliability etc.) are adversely affected by different characteristics of a QCP.

Peter P. Rohde