6 papers
DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
Jiayang Niu, Yan Wang, Jie Li +4
Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construct…
HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search
Jiayang Niu, Akib Karim, Yan Wang +5
Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular…
Hybrid Action Reinforcement Learning for Quantum Architecture Search
Jiayang Niu, Yan Wang, Jie Li +4
Reinforcement learning-based Quantum Architecture Search (QAS) offers a promising avenue for automating the design of variational quantum circuits, but existing methods typically d…
Multi-Agent Cross-Entropy Method with Monotonic Nonlinear Critic Decomposition
Yan Wang, Ke Deng, Yongli Ren
Cooperative multi-agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution (CTDE), where centralized critics leverage global informatio…
Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes
Sishun Liu, Ke Deng, Yongli Ren +2
Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the ne…
Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning
Sishun Liu, Ke Deng, Xiuzhen Zhang +1
Neural network-based Marked Temporal Point Process (MTPP) models have been widely adopted to model event sequences in high-stakes applications, raising concerns about the trustwort…