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APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study — arXiv:2609.18219

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18219
License (as archived): CC0-1.0
Archived: 2026-10-15T15:04:39.427Z
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How to cite

APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study — arXiv:2609.18219 — arXiv (en), https://arxiv.org/abs/2609.18219 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_754f5ffd4292 (Metraipolis archive dc_754f5ffd4292, sha256 5ca28a0fa11d5d3fd5dee04d99df8961c4dfa50dc014fc3ddca594cae8029fc9)

BibTeX

@misc{dc754f5ffd4292,
  title = {APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Study — arXiv:2609.18219},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18219},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_754f5ffd4292; sha256 5ca28a0fa11d5d3fd5dee04d99df8961c4dfa50dc014fc3ddca594cae8029fc9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18219. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Quantum Reinforcement Learning (QRL) represents policies as variational quantum circuits (VQCs), making it attractive for combinatorial optimization such as the Capacitated Vehicle Routing Problem (CVRP). On noisy intermediate-scale quantum (NISQ) hardware, however, decoherence degrades fidelity and destabilizes learning, and conventional error mitigation is applied statically without regard to the learning context. We introduce Adaptive Policy-Guided Error Mitigation (APGEM), a controller that selects among Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Readout Error Mitigation (REM) online, driven by a fidelity, entropy, and cost aware utility function and an epsilon-greedy rule over temporal-difference Q-scores. We evaluate on a realistic urban-logistics testbed, a Delhi-based CVRP over real landmarks with geodesic inter-node costs, exercised across five noise families and four severity levels. On this instance, the QRL agent outperforms constructive heuristics and approaches metaheuristics, while mitigation restores approximation ratios from 0.84-0.87 to 0.92-0.94 under high noise. The controller shifts from a CDR-dominated regime under short training horizons to a balanced deployment across all four techniques under longer horizons, indicating genuine regime-dependent selection. These preliminary results position adaptive, learning-aware mitigation as a practical route to noise-resilient QRL.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET).
Pagina del lavoro: https://arxiv.org/abs/2609.18219 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T14:08:19.112Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).