Quantum RL

RL: Reinforcement Learning

Follow this topic Ask about this topic

Momentum

5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 25

All topics

Context-Aware Error Mitigation Orchestration for Hybrid Quantum Reinforcement Learning on NISQ Systems
Oct 1, 2026Bisma Majid, Shabir Ahmed Sofi, Mir Mohammad YousufHybrid Quantum-Classical ML Quantum RL
Department of Information Technology, National Institute of Technology Srinagar, Hazratbal, Srinagar, 190006, Jammu and Kashmir, India
Quantum Reinforcement Learning for Cost and Delay Tradeoffs in Quantum Cloud Orchestration
Sep 23, 2026An N. H. Phan, Dang Van Huynh, Muhammad Usman +1Quantum RL
Faculty of Computer Networks and Communications, University of Information Technology, Vietnam National University, Ho Chi Minh City, Quarter 34, Linh Xuan Ward, Ho Chi Minh City, Vietnam · Faculty of Information Technology, Monash University, Clayton, Victoria, Australia · Quantum Systems Team, CSIRO, Clayton, Victoria, Australia
APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study
Sep 16, 2026Shabir Ahmad Sofi, Bisma Majid, Mir Mohammad YousufQuantum RL RL for Combinatorial Optimization
Department of Information Technology NIT Srinagar J&K, India
Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
Sep 14, 2026Pablo Rodriguez-Grasa, Sofiene Jerbi, Mikel Sanz +1Reinforcement Learning Q-Learning
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain · TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany +7
Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Sep 12, 2026Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1Generative Replay Deep Q-Learning
Delft University of Technology, Delft, The Netherlands. · Quantum Computing Division, QuTech, Delft, The Netherlands. · Indian Institute of Technology (BHU), Varanasi, India. +1
Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
Aug 3, 2026Joao F. DoriguelloMarkov Decision Processes Reinforcement Learning
HUN-REN Alfr´ed R´enyi Institute of Mathematics, Budapest, Hungary
DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
Jul 31, 2026Jiayang Niu, Yan Wang, Jie Li +4World Models Quantum RL
School of Computing Technologies, RMIT University · Quantum Systems, Data61, CSIRO · RMIT University
Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
Jul 7, 2026Jian Xu, Delu Zeng, John Paisley +1PAC-Bayesian Generalization Bounds Quantum Machine Learning
1RIKEN iTHEMS · 2RIKEN AIP · 3South China University of Technology +1
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
Jul 1, 2026Chuanming Yu, Jiaming Liu, Zihao Ge +4Quantum Machine Learning Quantum RL
Hebei Normal University, Shijiazhuang 050024, China · University of Luxembourg, Kirchberg L-1359, Luxembourg · Kyushu University, Fukuoka 819-0395, Japan
Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction
Jun 16, 2026Manoranjan Gandhudi, Arunkumar V., G. R. Anil +1RUL Estimation Quantum Annealing
Central University of Karnataka, Kalaburagi, India · University College of Engineering, Anna University, Tiruchirappalli, Tamil Nadu, India · AIONOS India Pvt Ltd, Hyderabad, India +1
Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing
Jun 11, 2026Yash Vardhan Tomar, Dheeraj PeddireddyReinforcement Learning Quantum Circuit Compilation
Purdue University West Lafayette, USA
Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
Jun 9, 2026Zeyu Liu, Xuanzhi Feng, Sing Kwong Lai +6Quantitative Finance Quantum Machine Learning
The Hong Kong University of Science and Technology
QnRL: Quantum-Native Reinforcement Learning
Jun 6, 2026Alexander DeRieux, Walid SaadDistributional RL Quantum Generative Modeling
Bradley Department of Electrical and Computer Engineering, Virginia Tech Institute for Advanced Computing, Alexandria, VA 22305 USA
Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization
May 26, 2026Haftu W. Fentaw, Steve Campbell, Simon CatonRL Control Multi-Task RL
School of Computer Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland. · Centre for Quantum Engineering, Science, and Technology, University College Dublin, Belfield, Dublin 4, Dublin, Ireland. · School of Physics, University College Dublin, Belfield, Dublin 4, Dublin, Ireland.
Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing
May 20, 2026Austin Braniff, Fengqi You, Yuhe TianQuantum Machine Learning Quantum RL
Department of Chemical and Biomedical Engineering, West Virginia University · aDepartment of Chemical and Biomedical Engineering, West Virginia University, Morgantown, West Virginia, United States · R.F. Smith School of Chemical and Biomolecular Engineering, Cornell University +1
Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
May 20, 2026Mohamed Khair Altrabulsi, Nouhaila Innan, Alberto Marchisio +2Robot Navigation Robotic RL
1eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE
Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation
May 18, 2026Wenjing Xiao, Jiatai Yan, Chenglong Shi +5Semantic Communication Quantum Machine Learning
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China, and also with the Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi 530004, China · School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China, and also with the Pazhou Laboratory, Guangzhou 510330, China · Qatar Center for Quantum Computing, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar +1
QuantFPFlow: Quantum Amplitude Estimation for Fokker--Planck Policy Optimisation in Continuous Reinforcement Learning
May 14, 2026Abraham Itzhak WeinbergReinforcement Learning Quantum-Inspired ML
AI-WEINBERG, AI Experts, Tel Aviv, Israel
Quantum Advantage in Multi Agent Reinforcement Learning
May 14, 2026Simranjeet Singh Dahia, Claudia SzaboDecentralized MARL Quantum RL
Adelaide University, Australia
Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis
May 11, 2026Richie Yeung, Aleks Kissinger, Rob CornishReinforcement Learning Quantum Circuit Compilation
Department of Computer Science, University of Oxford · Quantinuum · College of Computing and Data Science, Nanyang Technological University, Singapore
Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
May 5, 2026Yu-Ting Lee, Samuel Yen-Chi Chen, Fu-Chieh ChangHierarchical RL Quantum Machine Learning
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · Wells Fargo, New York, NY, USA · MediaTek Inc., Hsinchu, Taiwan
Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization
May 2, 2026T. Satyanarayana Murthy, B. Swathi Sowmya, Santhosh Voruganti +3Quantum Machine Learning Combinatorial Optimization
Chaitanya Bharathi Institute of Technology Hyderabad, India
QAROO: AI-Driven Online Task Offloading for Energy-Efficient and Sustainable MEC Networks
Apr 28, 2026Yongtao Yao, Yao Yang, Haorui Shi +3Edge Computing Wireless Resource Allocation
Replay-buffer engineering for noise-aware quantum circuit optimization
Apr 23, 2026Akash Kundu, Sebastian FeldReinforcement Learning Experience Replay
Delft University of Technology, Delft, The Netherlands · Quantum Computing Division, QuTech, The Netherlands
Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments
Jul 24, 2025Gilberto Cunha, Alexandra Ramôa, André Sequeira +2Partially Observable RL Bayesian RL
High-Assurance Software Laboratory (HASLab), INESC TEC, Portugal · Department of Computer Science, University of Minho, Portugal · International Iberian Nanotechnology Laboratory (INL), Portugal

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

Context-Aware Error Mitigation Orchestration for Hybrid Quantum Reinforcement Learning on NISQ Systems

Bisma Majid, Shabir Ahmed Sofi, Mir Mohammad Yousuf

Department of Information Technology, National Institute of Technology Srinagar, Hazratbal, Srinagar, 190006, Jammu and Kashmir, India

Hybrid Quantum-Classical ML Quantum RL RL for Combinatorial Optimization Quantum Error Mitigation

Open paper AskPDF arXiv

Quantum Reinforcement Learning (QRL) integrates reinforcement learning with parameterized quantum circuits and is a promising approach to combinatorial optimization. On Noisy Intermediate-Scale Quantum (NISQ) devices, however, decoherence, gate imperfections, and measurement errors reduce policy quality and make learning less reliable. Existing error mitigation techniques are generally applied as fixed corrections that do not adapt to changing noise conditions or to the evolving state of training. This work presents Adaptive Policy-Guided Error Mitigation (APGEM) as a context-aware orchestration layer of the hybrid quantum-classical training loop that dynamically selects the most suitable mitigation strategy during QRL training. APGEM evaluates Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Readout Error Mitigation (REM) using policy-level indicators, including quantum-state fidelity, policy entropy, cumulative reward, and approximation ratio, and integrates the selected strategy directly into the reinforcement learning loop. The framework is evaluated on the Capacitated Vehicle Routing Problem (CVRP), a representative NP-hard problem in urban logistics, under a range of NISQ noise models and noise levels. APGEM consistently outperforms conventional static mitigation methods, reaches approximately 94% of the utility of an oracle strategy, maintains higher quantum-state fidelity as noise increases, and produces more stable learning behaviour throughout training. Ablation studies show that the framework learns context-aware mitigation policies that adapt to different noise environments and circuit execution conditions. These findings demonstrate that integrating adaptive error mitigation into the learning process substantially improves the robustness and reliability of QRL on NISQ hardware.

Momentum

Related topics

Latest papers 25