Quantum Error Mitigation

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Quantum error mitigation studies how to recover useful output probabilities or expectation values from noisy quantum circuits, especially on near-term hardware. Common approaches include zero-noise extrapolation (ZNE), probabilistic error cancellation (PEC), circuit-dependent correction (CDR), readout mitigation, and learned models of device noise. Recent work also adapts mitigation choices to changing noise and workload conditions, but gains must be weighed against extra measurements, computational cost, and whether models transfer across devices or circuit sizes. A central challenge is establishing when mitigation improves task-relevant results rather than merely matching ideal outputs, since some interventions can add error on large or poorly characterized circuits.

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12 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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
Weakly Supervised Quantum Error Mitigation
Sep 22, 2026Seyed Mohamad Ali Tousi, G. N. DeSouzaWeakly Supervised Learning Quantum Error Mitigation
Vision-Guided and Intelligent Robotics Lab (ViGIR) University of Missouri Columbia, US
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
QEMScore: How Much Does the Measurement Add to Learned Quantum Error Mitigation?
Sep 15, 2026Yue Zhao, Huayue Gu, Yushun Dong +1Quantum Machine Learning Quantum Error Mitigation
University of Southern California · Kennesaw State University · Florida State University +1
Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations
Jul 22, 2026Rostyslav SipakovQuantum Feature Maps Quantum Kernel Methods
Department of Environmental Protection and Occupational Safety Technologies, Kyiv National University of Construction and Architecture, Kyiv, Ukraine
Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware
Jun 19, 2026Nandika Goyal, Glen Uehara, Andreas SpaniasEdge Inference Hybrid Quantum-Classical ML
SenSIP Center, School of ECEE · Arizona State University
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
May 28, 2026Hoang M. Ngo, Quan Nguyen, Wanli Xing +1Federated Learning Aggregation Quantum Machine Learning
Department of Computer & Information Science & Engineering University of Florida · Frost Institute for Data Science and Computing University of Miami
GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation
Apr 27, 2026Steven Szachara, Sheeraja Rajakrishnan, Dylan Jay Van Allen +3Quantum Error Mitigation
Department of Software Engineering, Rochester Institute of Technology, Rochester, USA · Institute for Quantum & Information Sciences, Syracuse University, Syracuse, USA
Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
Apr 27, 2026Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman AnikFew-Shot Learning Cross-Domain Transfer Learning
United International University · Dhaka, Bangladesh
Scalable Quantum Error Mitigation with Physically Informed Graph Neural Networks
Apr 18, 2026Huaxin Wang, Xinge Wu, Jiajun Liu +4Quantum Error Mitigation
National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China · School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China · School of Communication and Artificial Intelligence, School of Integrated Circuits, Nanjing Institute of Technology, Nanjing 211167, China
Noise Models Impacts and Mitigation Strategies in Photonic Quantum Machine Learning
Mar 10, 2026A. M. A. S. D. Alagiyawanna, Asoka KarunanandaQuantum Machine Learning Photonic Computing
Department of Computational Mathematics, University of Moratuwa, Sri Lanka
QANTIS: A Hardware-Validated Quantum Platform for POMDP Planning and Multi-Target Data Association
Feb 28, 2026Bayram Yüksel Eker, Suayb S. Arslan, Özgür Nazlı +2Partially Observable Markov Decision Processes Multi-Object Tracking
Neura Parse Ltd., London, United Kingdom · Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey · İzmir Ekonomi Üniversitesi, İzmir, Turkey +2

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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

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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.