Adaptive Minimum-Variance Phase Gradient Estimator Overcomes Trainability Barriers in Neural-Network-Based Variational Quantum Optimization
Quantum/Chemical
For quantum many-body physics and ab initio quantum chemistry, this work derives an unbiased direct gradient estimator and introduces the adaptive minimum-variance phase (AMVP) estimator to overcome training failures in weak-gradient regimes for neural-network variational optimization.
See LICENSE file Quantum Many-Body PhysicsVariational OptimizationGradient Estimation