BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
This work provides a standardized benchmark for researchers in quantum computing and machine learning, though it is incremental as it focuses on evaluation rather than introducing new algorithms.
The authors tackled the problem of evaluating reinforcement learning algorithms for quantum architecture search by creating BenchRL-QAS, a benchmarking framework that tests 9 RL agents on various quantum tasks, finding that no single method performs best universally and that a well-chosen RL algorithm can outperform baseline variational quantum classifiers.
We present BenchRL-QAS, a unified benchmarking framework for reinforcement learning (RL) in quantum architecture search (QAS) across a spectrum of variational quantum algorithm tasks on 2- to 8-qubit systems. Our study systematically evaluates 9 different RL agents, including both value-based and policy-gradient methods, on quantum problems such as variational eigensolver, quantum state diagonalization, variational quantum classification (VQC), and state preparation, under both noiseless and noisy execution settings. To ensure fair comparison, we propose a weighted ranking metric that integrates accuracy, circuit depth, gate count, and training time. Results demonstrate that no single RL method dominates universally, the performance dependents on task type, qubit count, and noise conditions providing strong evidence of no free lunch principle in RL-QAS. As a byproduct we observe that a carefully chosen RL algorithm in RL-based VQC outperforms baseline VQCs. BenchRL-QAS establishes the most extensive benchmark for RL-based QAS to date, codes and experimental made publicly available for reproducibility and future advances.