5 papers
Optimal copy complexity of quantum state cloning
Sangwoo Jeon, Vaughn Sohn, Changhun Oh
Quantum state cloning is the task of approximately producing additional copies of an unknown quantum state from a finite number of input copies. The optimal cloning fidelity is kno…
Bound Entanglement Is Insufficient for an Exponential Quantum Learning Advantage
Hyeongu Kang, Sangwoo Jeon, Changhun Oh
While entanglement is known to enable exponential improvements in the sample complexity of quantum learning, it remains unclear which properties of entangled resources are responsi…
Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation
Sangwoo Jeon, Juchul Shin, YeonJe Cho +2
Modern autonomous drone missions increasingly require software frameworks capable of seamlessly integrating structured symbolic planning with adaptive reinforcement learning (RL).…
Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning
Sangwoo Jeon, Juchul Shin, Gyeong-Tae Kim +2
Generalized planning using deep reinforcement learning (RL) combined with graph neural networks (GNNs) has shown promising results in various symbolic planning domains described by…
On the query complexity of unitary channel certification
Sangwoo Jeon, Changhun Oh
Certifying the correct functioning of a unitary channel is a critical step toward reliable quantum information processing. In this work, we investigate the query complexity of the…