Arline Benchmarks: Automated Benchmarking Platform for Quantum Compilers
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Abstract (arXiv)
Efficient compilation of quantum algorithms is vital in the era of Noisy Intermediate-Scale Quantum (NISQ) devices. While multiple open-source quantum compilation and circuit optimization frameworks are available, e.g. IBM Qiskit, CQC Tket, Google Cirq, Rigetti Quilc, PyZX, their relative performance is not always clear to a quantum programmer. The growth of complexity and diversity of quantum circuit compilation algorithms creates a demand for a dedicated tool for cross-benchmarking and profiling of inner workflow of the quantum compilation stack. We present an open-source software package, Arline Benchmarks, that is designed to perform automated benchmarking of quantum compilers with the focus on NISQ applications. The name "Arline" was given in honour of Arline Greenbaum Feynman, the first wife of Richard Feynman, the pioneer of quantum computing. We compared several quantum compilation frameworks based on a set of important metrics such as post-optimization gate counts, circuit depth, hardware-dependent circuit cost function, compiler run time etc. with a detailed analysis of metrics for each compilation stage. We performed a variety of compiler tests for random circuits and structured quantum algorithms (VQE, Trotter decomposition, Grover search, Option Pricing via Amplitude Estimation) for several popular quantum hardware architectures. Leveraging cross-platform functionality of Arline, we propose a concept of composite compilation pipeline that combines compiler-specific circuit optimization subroutines in a single compilation stack and finds an optimized sequence of compilation passes. By providing detailed insights into the compilation flow of quantum compilers, Arline Benchmarks offers a valuable toolkit for quantum computing researchers and software developers to gain additional insights into compilers' characteristics.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Feb 2022.
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Citing papers per year: 2024: 3; 2025: 2.
5 papers.
- 2024Quantum Architecture Search: A Surveycited by 32
- 2024AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Searchcited by 8
- 2024Quantum Compiling with Reinforcement Learning on a Superconducting Processorcited by 4
- 2025Featuremetric benchmarking: Quantum computer benchmarks based on circuit featurescited by 0
- 2025Platform-Agnostic Modular Architecture for Quantum Benchmarkingcited by 0
- 2025Platform-Agnostic Modular Architecture for Quantum Benchmarking9 Oct 2025
- 2025Featuremetric benchmarking: Quantum computer benchmarks based on circuit features17 Apr 2025
- 2024AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search7 Oct 2024
- 2024Quantum Compiling with Reinforcement Learning on a Superconducting Processor18 Jun 2024
- 2024Quantum Architecture Search: A Survey10 Jun 2024