BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search

• URL: http://arxiv.org/abs/2507.12189v1
• Date: Wed, 16 Jul 2025 12:43:25 GMT
• Title: BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
• Authors: Azhar Ikhtiarudin, Aditi Das, Param Thakkar, Akash Kundu,
• Abstract summary: We introduce BenchRL-QAS, a unified benchmarking framework for evaluating reinforcement learning (RL) algorithms in quantum architecture search (QAS)<n>Our study benchmarks nine RL agents including both value-based and policy-gradient methods on representative quantum problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce BenchRL-QAS, a unified benchmarking framework for systematically evaluating reinforcement learning (RL) algorithms in quantum architecture search (QAS) across diverse variational quantum algorithm tasks and system sizes ranging from 2- to 8-qubit. Our study benchmarks nine RL agents including both value-based and policy-gradient methods on representative quantum problems such as variational quantum eigensolver, variational quantum state diagonalization, quantum classification, and state preparation, spanning both noiseless and realistic noisy regimes. We propose a weighted ranking metric that balances accuracy, circuit depth, gate count, and computational efficiency, enabling fair and comprehensive comparison. Our results first reveal that RL-based quantum classifier outperforms baseline variational classifiers. Then we conclude that no single RL algorithm is universally optimal when considering a set of QAS tasks; algorithmic performance is highly context-dependent, varying with task structure, qubit count, and noise. This empirical finding provides strong evidence for the "no free lunch" principle in RL-based quantum circuit design and highlights the necessity of tailored algorithm selection and systematic benchmarking for advancing quantum circuit synthesis. This work represents the most comprehensive RL-QAS benchmarking effort to date, and BenchRL-QAS along with all experimental data are made publicly available to support reproducibility and future research https://github.com/azhar-ikhtiarudin/bench-rlqas.

Related papers

• CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms [2.536162003546062]
  CleanQRL is a library that offers single-script implementations of many Quantum Reinforcement Learning algorithms.<n>Our library provides clear and easy to understand scripts that researchers can quickly adapt to their own needs.
  arXiv  Detail & Related papers  (2025-07-10T09:53:39Z)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [48.670876200492415]
  Variational Quantum Algorithms (VQAs) are a promising approach for leveraging powerful Noisy Intermediate-Scale Quantum (NISQ) computers.<n>We propose $rho$DARTS, a differentiable Quantum Architecture Search (QAS) algorithm that models the search process as the evolution of a quantum mixed state.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches [0.0]
  This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems. Our benchmark includes noisy intermediate-scale quantum (NISQ) algorithms, such as the variational quantum eigensolver. Our findings reveal that no single non-FTQC algorithm performs optimally across all problem types, underscoring the need for tailored algorithmic strategies.
  arXiv  Detail & Related papers  (2024-10-30T08:41:29Z)
• A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search [0.0]
  This study investigates RL-QAS for crafting ansatz tailored to variational quantum state diagonalisation problem. We leverage these insights to devise an entanglement-guided admissible ansatz in QAS to diagonalise random quantum states using optimal resources.
  arXiv  Detail & Related papers  (2024-04-09T09:54:59Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms [65.268245109828]
  We take inspiration from Kearns' SQ oracle and Valiant's weak evaluation oracle. We introduce an extensive yet intuitive framework that yields unconditional lower bounds for learning from evaluation queries.
  arXiv  Detail & Related papers  (2023-10-26T18:23:21Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Reinforcement Learning Quantum Local Search [0.0]
  We propose a reinforcement learning (RL) based approach to train an agent for improved subproblem selection in Quantum Local Search (QLS) Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems.
  arXiv  Detail & Related papers  (2023-04-13T13:07:19Z)
• Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning [0.0]
  We introduce a training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces. We investigate which architectural choices for quantum Q-learning agents are most important for successfully solving certain types of environments.
  arXiv  Detail & Related papers  (2021-03-28T08:57:22Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning [102.78958681141577]
  We present SUNRISE, a simple unified ensemble method, which is compatible with various off-policy deep reinforcement learning algorithms. SUNRISE integrates two key ingredients: (a) ensemble-based weighted Bellman backups, which re-weight target Q-values based on uncertainty estimates from a Q-ensemble, and (b) an inference method that selects actions using the highest upper-confidence bounds for efficient exploration.
  arXiv  Detail & Related papers  (2020-07-09T17:08:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.