Title: Variational Quantum Hypernetworks for Generalized Quantum Circuit Initialization
Primary area: optimization
Keywords: Variational Quantum Algorithms, Barren Plateaus, Meta-Learning, Parameter Initialization, Quantum Machine Learning

Abstract: While vanishing gradients are a widely recognized obstacle for deep Variational Quantum Algorithms (VQAs), we demonstrate that a complementary instability—transient gradient explosion—is an equally critical, yet often overlooked, barrier to convergence. To address this more complete picture of trainability, we introduce the VQ-Hypernetwork: a meta-learning framework that generates depth- and problem-aware initial parameters.
By learning to map circuits to well-conditioned regions of the parameter space, our approach provides a robust solution. Experiments on representative benchmarks show that this learned strategy explicitly suppresses the sharp, transient spikes in gradient norm that plague standard initializations. This stabilization leads to more direct optimization trajectories and higher-quality solutions under fixed training budgets. By finding these favorable starting points, our method indirectly avoids the flat regions of barren plateaus and tames gradient instabilities, establishing learned, depth-aware initialization as a key strategy for practical quantum advantage.

TL;DR: We built a VQ-Hypernetwork that meta-learns how to find the depth-aware starting point for parameterized quantum circuit, alleviating the vanishing/exploding gradient problem to make training stable, fast, and effective.