TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
A closer look at invalid action masking in policy gradient algorithms
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
TARMM uses a temporal graph to model RAN dynamics and MARL with action masking for proactive mobility management in 5G O-RAN, reducing tail latency by up to 44% and packet loss by up to 56% on a multi-cell testbed for VR workloads.
citing papers explorer
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TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency
TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
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Your Loss is My Gain: Low Stake Attacks on Liquid Staking Pools
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
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TARMM: Scaling Delay-Critical Edge AI Offloading in 5G O-RAN via Temporal Graph Mobility Management
TARMM uses a temporal graph to model RAN dynamics and MARL with action masking for proactive mobility management in 5G O-RAN, reducing tail latency by up to 44% and packet loss by up to 56% on a multi-cell testbed for VR workloads.