Preventing Barren Plateaus in Continuous Quantum Generative Models

Recent developments in the field of variational quantum circuits (VQCs) have shifted the prerequisites for trainability for many barren plateau-free models onto the data encoding state fed into a classically trainable unitary. By strengthening proofs relating to small-angle initialisation, we provide a full circuit model which does not suffer from barren plateaus and is robust against current classical simulation techniques, specifically tensor network contraction and Pauli propagation. We propose this as a quantum generative model amenable towards NISQ devices and quantum-classical hybrid models, raising new questions in the debate regarding usefulness of VQCs.

Source: arXiv:2602.10049v1 - http://arxiv.org/abs/2602.10049v1 PDF: https://arxiv.org/pdf/2602.10049v1 Original Link: http://arxiv.org/abs/2602.10049v1