FR-PPO leverages Fisher-Rao geometry to provide monotonic policy improvement guarantees and sub-linear convergence without dependence on state or action space dimensions.
Optimal transport conditional flow matching equals exact proximal operators via extended Brenier potentials without density assumptions, yields explicit vector fields, converges with batch size, and contracts exponentially normal to manifold-supported targets.
TNQE uses structured unitary tensor networks to learn shallow, resource-efficient quantum data encoding circuits that achieve 0.04x the depth of amplitude encoding and scale to high-resolution images on real hardware.