BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912610993045504 |
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| author | Ikhtiarudin, Azhar Das, Aditi Thakkar, Param Kundu, Akash |
| author_facet | Ikhtiarudin, Azhar Das, Aditi Thakkar, Param Kundu, Akash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search Ikhtiarudin, Azhar Das, Aditi Thakkar, Param Kundu, Akash Quantum Physics Artificial Intelligence Machine Learning Performance 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. |
| title | BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search |
| topic | Quantum Physics Artificial Intelligence Machine Learning Performance |
| url | https://arxiv.org/abs/2507.12189 |