Abstract
We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit execution, and results analysis into independent, interoperable components. Supporting over 20 benchmark variants ranging from simple algorithmic tests like Bernstein-Vazirani to complex Hamiltonian simulation with observable calculations, the system integrates with multiple circuit generation APIs (Qiskit, CUDA-Q, Cirq) and enables diverse workflows. We validate the architecture through successful integration with Sandia's $\textit{pyGSTi}$ for advanced circuit analysis and CUDA-Q for multi-GPU HPC simulations. Extensibility of the system is demonstrated by implementing dynamic circuit variants of existing benchmarks and a new quantum reinforcement learning benchmark, which become readily available across multiple execution and analysis modes. Our primary contribution is identifying and formalizing modular interfaces that enable interoperability between incompatible benchmarking frameworks, demonstrating that standardized interfaces reduce ecosystem fragmentation while preserving optimization flexibility. This architecture has been developed as a key enhancement to the continually evolving QED-C Application-Oriented Performance Benchmarks for Quantum Computing suite.
Abstract (translated)
我们提出了一种平台无关的模块化架构，通过将问题生成、电路执行和结果分析分离为独立且可互操作的组件，解决了量子计算基准测试日益碎片化的局面。该系统支持超过20种不同的基准变体，从简单的算法测试（如Bernstein-Vazirani）到复杂的哈密顿量模拟和可观测量计算。系统集成了多个电路生成API（Qiskit、CUDA-Q、Cirq），并支持各种工作流程。我们通过与桑迪亚的$\textit{pyGSTi}$进行高级电路分析以及与CUDA-Q进行多GPU HPC仿真成功集成来验证此架构的有效性。系统的可扩展性在实现现有基准测试的动态电路变体和新的量子强化学习基准时得到了证明，这些新功能可以无缝应用于多种执行和分析模式中。 我们的主要贡献在于识别并形式化了模块接口，以促进不兼容基准框架之间的互操作性，并展示了标准化接口能够减少生态系统碎片化的同时保持优化灵活性。这一架构作为QED-C应用导向的量子计算性能基准套件持续发展中的一项关键增强功能而被开发出来。
URL
https://arxiv.org/abs/2510.08469
