Optimization + Rl + Training

17.0

Implement a reinforcement learning or search-based approach for CNOT circuit optimization to replace heuristic compilers. This is critical for improving efficiency in noisy quantum hardware.

emergingimplementation gap

rlquantumtrainingclassificationoptimization

Signals (4)

arXiv1d ago

AlphaCNOT: Learning CNOT Minimization with Model-Based Planning

arXiv5h ago

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

arXiv1d ago

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

arXiv5h ago

Optimization + Rl + Training

Signals (4)

AlphaCNOT: Learning CNOT Minimization with Model-Based Planning

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees

Optimization + Rl + Training

Signals (4)

AlphaCNOT: Learning CNOT Minimization with Model-Based Planning

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees