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新论文 · v1arXiv:2611.13778

Polynomial Quantum Recurrent Channels for Matrix Inverse Actions: Deferred-Reset Dynamics and Chebyshev Filtering

Vahid Tavakkoli, Kabeh Mohsenzadegan, Kyandoghere Kyamakya

原文摘要

We develop completely positive, trace-preserving polynomial recurrent channels for quantum solution-state preparation and derive fixed-point and time-varying tracking bounds. Across 2,880 synthetic configurations, resident Chebyshev recurrence improves accuracy-passing snapshot counts in 608 cases, ties in 2,271, and loses in one. A separate 360-scenario cost study shows that fresh recurrence is cheaper in all 184 jointly feasible scenarios (median resident-to-fresh cost ratio: 3.12), while resident recurrence uniquely satisfies accuracy requirements in 74 cases. At a logical noise rate of $10^-4$, neither recurrent method qualifies. Based on exact reduced-density simulations and assumed cost models, our results demonstrate conditional tracking advantages but no general end-to-end speedup or total-cost benefit.

quant-ph · cs.ET

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12638

FAROS: Forecast-Aware Risk-Informed Online Reinforcement Learning for Proactive O-RAN Slicing

Peyman Eftekhari, Seyyed Kasra Mortazavi, Reza Farahani, Robert Legenstein, Kyandoghere Kyamakya, Martin Gebser

原文摘要

Proactive Open Radio Access Network (O-RAN) slicing requires decisions before an approaching quality-of-service (QoS) loss becomes difficult to reverse. We present FAROS, a Forecast-Aware Risk-Informed O-RAN Slicing framework that combines an eight-step QoS forecast, training-only PCMCI+-supported temporal risk, Multi-Agent Proximal Policy Optimization (MAPPO) under centralized training and decentralized execution (CTDE), symbolic feasibility constraints, and reliability-aware Top-K arbitration. MAPPO supplies the actor proposal, while A5v2 checks admissibility and one-step utility before execution. Evaluation uses ten validation-selected frozen checkpoints, 500 matched start indices per seed under the risky-start protocol, and 150,000 agent-level decisions. FAROS obtains mean reward 0.2778 and ten-step recovery 90.56% across ten seeds. A fixed model-matched diagnostic rule reaches reward 0.3015 and recovery 97.57% under the same transition-reward contract, so we make no superiority claim over direct threshold logic. A same-actor M4 shield control also reaches zero risky-state prohibited actions. In the M5 trace, A5v2 changes 22.61% of actor proposals and reduces the predicate-defined prohibited-action rate from 16.79% at proposal time to zero at execution; this property is enforced by the arbiter rather than learned by MAPPO. Independent ns-3 experiments test the six-action interface at packet level and remain separate from end-to-end FAROS claims.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38040

EARS-Info: Information-Aware Event-Driven Reallocation for Multi-Robot Exploration

Kabeh Mohsenzadegan, Vahid Tavakkoli, Kyandoghere Kyamakya

原文摘要

We introduce EARS-Info, an information-aware extension of Event-Driven Ant Reallocation Search for multi-robot exploration on OpenStreetMap-derived grids. It combines marginal sensing gain, feasible relocation, return-aware routing, sensing reservations, and cached shortest paths. Across 168,000 episodes comparing 30 implementations on eight city maps, EARS-Info achieves the highest mean operational score in every evaluation panel. With eight robots and 3% communication dropout, it improves the score over EARS-Safe by 5.43%, increases weighted discovery from 88.88% to 98.46%, and reduces mean energy use by 2.62%. Score differences are positive in all eight cities ($p=0.0078125$; descriptive 95% city-bootstrap interval $[0.0159,0.0694]$), with no simulated incidents across 5,600 EARS-Info episodes. These results support the complete controller; individual component advantages remain uncertain. Generalization is limited by reused geography, one training replicate, coarse maps, and adapted comparison methods.

cs.RO · cs.AI · cs.MA

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arXiv ↗PDF ↗