# Platform-Agnostic Modular Architecture for Quantum Benchmarking

arXiv: https://arxiv.org/abs/2510.08469 · HTML: https://mlsanity.com/paper/2510.08469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 91 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2206.09421/) (2022)
- [SupermarQ: A Scalable Quantum Benchmark Suite](https://mlsanity.com/paper/2202.11045/) (2022)
- [Benchmarking Quantum Reinforcement Learning](https://mlsanity.com/paper/2501.15893/) (2025)
- [Arline Benchmarks: Automated Benchmarking Platform for Quantum Compilers](https://mlsanity.com/paper/2202.14025/) (2022)
- [Hybrid Classical-Quantum Supercomputing: A demonstration of a multi-user, multi-QPU and multi-GPU environment](https://mlsanity.com/paper/2508.16297/) (2025)
- [Efficient charge-preserving excited state preparation with variational quantum algorithms](https://mlsanity.com/paper/2410.14357/) (2024)
- [Dynamic quantum circuit compilation](https://mlsanity.com/paper/2310.11021/) (2023)
- [Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version)](https://mlsanity.com/paper/2408.09055/) (2024)

## Abstract (arXiv)

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 textitpyGSTi 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.