Post-variational quantum neural networks

arXiv:2307.10560v2 Announce Type: replace-cross
Abstract: Hybrid quantum-classical computing in the noisy intermediate-scale quantum (NISQ) era with variational algorithms can exhibit barren plateau issues, causing difficult convergence of gradient-based optimization techniques. In this paper, we discuss "post-variational strategies", which shift tunable parameters from the quantum computer to the classical computer, opting for ensemble strategies when optimizing quantum models. We discuss various strategies and design principles for constructing individual quantum circuits, where the resulting ensembles can be optimized with convex programming. Further, …

abstract algorithms arxiv computer computing convergence cs.lg discuss ensemble gradient hybrid intermediate networks neural networks nisq optimization paper parameters quant-ph quantum quantum computer quantum neural networks scale shift strategies type