Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2405.05892 · Quantum Information Processing

An RNN–policy gradient approach for quantum architecture search

Gang Wang, Bang-Hai Wang, Shao-Ming Fei

Published May 13, 2024• 9 citations
arXivHTMLPDF
DOI
10.1007/s11128-024-04393-y

Related papers

Gradient-based optimization for quantum architecture search

Zhimin He, Jiachun Wei, Chuangtao Chen +3 more

Neural Networks•Jul 8, 2024•21 citations

Advanced Quantum Policy Approximation in Policy Gradient Reinforcement Learning

Leonhard Kunczik

Springer eBooks•Jan 1, 2022•0 citations

Quantum architecture search via truly proximal policy optimization

Xianchao Zhu, Xiaokai Hou

Quantum Architecture Search (QAS) is a process of voluntarily designing quantum circuit architectures using intelligent algorithms. Recently, Kuo et al. (Quantum architecture search via deep reinforcement learning. arXiv preprint arXiv:2104.07715, 2021) proposed a deep reinforcement learning-based QAS (QAS-PPO) method, which used the Proximal Policy Optimization (PPO) algorithm to automatically generate the quantum circuit without any expert knowledge in physics. However, QAS-PPO can neither strictly limit the probability ratio between old and new policies nor enforce well-defined trust domain constraints, resulting in poor performance. In this paper, we present a new deep reinforcement learning-based QAS method, called Trust Region-based PPO with Rollback for QAS (QAS-TR-PPO-RB), to automatically build the quantum gates sequence from the density matrix only. Specifically, inspired by the research work of Wang, we employ an improved clipping function to implement the rollback behavior to limit the probability ratio between the new strategy and the old strategy. In addition, we use the triggering condition of the clipping based on the trust domain to optimize the policy by restricting the policy within the trust domain, which leads to guaranteed monotone improvement. Experiments on several multi-qubit circuits demonstrate that our presented method achieves better policy performance and lower algorithm running time than the original deep reinforcement learning-based QAS method.

Scientific Reports•Mar 29, 2023•29 citations

Quantum Architecture Search with Meta‐Learning

Zhimin He, Chuangtao Chen, Lvzhou Li +2 more

Abstract Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling and quantum machine learning models. The performances of VQAs largely depend on the architecture of parameterized quantum circuits (PQCs). Quantum architecture search (QAS) aims to automate the design of PQCs in different VQAs with classical optimization algorithms. However, current QAS algorithms do not use prior experiences and search the quantum architecture from scratch for each new task, which is inefficient and time consuming. In this paper, a meta quantum architecture search (MetaQAS) algorithm is proposed, which learns good initialization heuristics of the architecture (i.e., meta‐architecture), along with the meta‐parameters of quantum gates from a number of training tasks such that they can adapt to new tasks with fewer gradient updates, which leads to fast learning on new tasks. The proposed MetaQAS can be used with arbitrary gradient‐based QAS algorithms. Simulation results on variational quantum compiling (VQC) and quantum approximate optimization algorithm (QAOA) show that the architectures optimized by MetaQAS converge faster than a state‐of‐the‐art gradient‐based QAS algorithm, namely DQAS. MetaQAS also achieves a better solution than DQAS after fine‐tuning of gate parameters.

arXiv:2106.06248•Advanced Quantum Technologies•Jun 17, 2022•38 citations

A GNN-based predictor for quantum architecture search

Zhimin He, Xuefen Zhang, Chuangtao Chen +3 more

Quantum Information Processing•Feb 25, 2023•30 citations

BenchRL-QAS: Benchmarking Reinforcement Learning Algorithms for Quantum Architecture Search

Azhar Ikhtiarudin, Aditi Das, Param Thakkar +1 more

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.

arXiv:2507.12189•Proceedings of the AAAI Symposium Series•Nov 23, 2025•2 citations
Quantum Architecture Search (QAS) is a process of voluntarily designing quantum circuit architectures using intelligent algorithms. Recently, Kuo et al. (Quantum architecture search via deep reinforcement learning. arXiv preprint arXiv:2104.07715, 2021) proposed a deep reinforcement learning-based QAS (QAS-PPO) method, which used the Proximal Policy Optimization (PPO) algorithm to automatically generate the quantum circuit without any expert knowledge in physics. However, QAS-PPO can neither strictly limit the probability ratio between old and new policies nor enforce well-defined trust domain constraints, resulting in poor performance. In this paper, we present a new deep reinforcement learning-based QAS method, called Trust Region-based PPO with Rollback for QAS (QAS-TR-PPO-RB), to automatically build the quantum gates sequence from the density matrix only. Specifically, inspired by the research work of Wang, we employ an improved clipping function to implement the rollback behavior to limit the probability ratio between the new strategy and the old strategy. In addition, we use the triggering condition of the clipping based on the trust domain to optimize the policy by restricting the policy within the trust domain, which leads to guaranteed monotone improvement. Experiments on several multi-qubit circuits demonstrate that our presented method achieves better policy performance and lower algorithm running time than the original deep reinforcement learning-based QAS method.
Abstract Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling and quantum machine learning models. The performances of VQAs largely depend on the architecture of parameterized quantum circuits (PQCs). Quantum architecture search (QAS) aims to automate the design of PQCs in different VQAs with classical optimization algorithms. However, current QAS algorithms do not use prior experiences and search the quantum architecture from scratch for each new task, which is inefficient and time consuming. In this paper, a meta quantum architecture search (MetaQAS) algorithm is proposed, which learns good initialization heuristics of the architecture (i.e., meta‐architecture), along with the meta‐parameters of quantum gates from a number of training tasks such that they can adapt to new tasks with fewer gradient updates, which leads to fast learning on new tasks. The proposed MetaQAS can be used with arbitrary gradient‐based QAS algorithms. Simulation results on variational quantum compiling (VQC) and quantum approximate optimization algorithm (QAOA) show that the architectures optimized by MetaQAS converge faster than a state‐of‐the‐art gradient‐based QAS algorithm, namely DQAS. MetaQAS also achieves a better solution than DQAS after fine‐tuning of gate parameters.
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.