Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning
Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays. However, if parameter cross-talk is strong, a non-stationary environment from the perspective of any individual agent can destabilize learning - the same effect that plagues manual tuning of such systems. We propose using a factored representation of the action space, learned online, to decouple agents and minimize their interference. Our framework, QADAPT, uses this factorization to efficientl
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- PossiblePossibly related (embedding) · 51%mit-han-lab/torchquantum →
- PossiblePossibly related (embedding) · 49%netket/netket →
- PossiblePossibly related (embedding) · 48%MIT and IBM Project Quantum Unity Operators into Language Model Latent Spaces for Multimodal Circuit Synthesis - Quantum Computing Report →
- PossiblePossibly related (embedding) · 48%jaimasih05-commits/swarm-foraging-qlearn →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “Action-Factored Multi-Agent Reinforcement Learning for Scala” ≈ “AgentCore-8B””
- LinkedLinked via arxiv author · 85%Edwin De Nicolo →
“Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning”
- LinkedLinked via arxiv author · 85%Rahul Marchand →
“Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning”
- LinkedLinked via arxiv author · 85%Cornelius Carlsson →
“Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning”