Quantum RL variants with state encoding solve moderate-scale flowsheet synthesis problems competitively with classical RL on per-episode performance and more efficiently per parameter.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
A dynamic-circuit framework for multi-step quantum Markov decision processes reduces physical qubit count from O(T) to O(1) while preserving trajectory fidelity and applying Grover amplification for high-return paths.
citing papers explorer
-
Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing
Quantum RL variants with state encoding solve moderate-scale flowsheet synthesis problems competitively with classical RL on per-episode performance and more efficiently per parameter.
-
Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization
A dynamic-circuit framework for multi-step quantum Markov decision processes reduces physical qubit count from O(T) to O(1) while preserving trajectory fidelity and applying Grover amplification for high-return paths.