New framework enhances quantum reinforcement learning on noisy hardware

Researchers have developed an adaptive error mitigation framework called APGEM for hybrid quantum reinforcement learning on noisy intermediate-scale quantum (NISQ) systems. This system dynamically selects the most suitable error mitigation strategy during the quantum reinforcement learning training loop, adapting to changing noise conditions. APGEM integrates Zero-Noise Extrapolation, Probabilistic Error Cancellation, Clifford Data Regression, and Readout Error Mitigation, and has shown improved robustness and reliability in experiments on the Capacitated Vehicle Routing Problem. AI

IMPACT Improves the robustness and reliability of quantum reinforcement learning on NISQ hardware, potentially accelerating progress in quantum AI applications.

RANK_REASON The cluster contains a research paper detailing a new method for quantum reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• APGEM
• Capacitated Vehicle Routing Problem
• Clifford Data Regression
• Mir Mohammad Yousuf
• noisy intermediate-scale quantum era
• Probabilistic Error Cancellation
• Quantum reinforcement learning
• Readout Error Mitigation
• Zero-noise extrapolation for quantum-gate error mitigation with identity insertions

• paper
• infra

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