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Paper

MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments

by Abhishek Sawaika arxiv/2604.11131

Free2AITools Nexus Index

35.9

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 68

Q: Quality 60

Tech Context

Vital Performance —

Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable strategy in the field of artificial intelligence. Most of the environments used for RL are often high-dimensional, and traditional RL algorithms becomes computationally expensive and challenging to effectively learn from such systems. Recent advancements in practical demonstration of quantum computing (QC) theories, s...

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Paper Information Summary
Entity Passport
Registry ID	2604.11131
License	arXiv
Provider	arxiv

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Academic & Research Attribution

BibTeX

@misc{arxiv_2604_11131,
  author = {Abhishek Sawaika},
  title = {MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.11131}},
  note = {Accessed via Free2AITools.}
}

APA Style

Abhishek Sawaika. (2026). MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments [Paper]. Free2AITools. https://arxiv.org/abs/2604.11131

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 68

Quality (Q) 60

💬 Index Insight

FNI V2.0 for MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments: Authority (A:0), Popularity (P:0), Recency (R:68), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable strategy in the field of artificial intelligence. Most of the environments used for RL are often high-dimensional, and traditional RL algorithms becomes computationally expensive and challenging to effectively learn from such systems. Recent advancements in practical demonstration of quantum computing (QC) theories, s..."

❝ Cite Node

@article{Sawaika2026MADQRL:,
  title={MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments},
  author={Abhishek Sawaika},
  journal={arXiv preprint arXiv:2604.11131},
  year={2026}
}

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Abhishek Sawaika

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Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️68RecencyFNI pillar

✅60QualityFNI pillar

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🆔 Identity & Source

id: 2604.11131
slug: 2604.11131
source: arxiv
author: Abhishek Sawaika
license: arXiv
tags: arxiv:cs.AI, arxiv:cs.LG, arxiv:cs.MA, reinforcement

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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