Quantum Generative Training Using R\'enyi Divergences

Understand

Quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning.

• A major challenge in QNN development is a concentration of measure phenomenon known as a barren plateau that leads to exponentially small gradients for a range of QNNs models.
• In this work, we examine the assumptions that give rise to barren plateaus and show that an unbounded loss function can circumvent the existing no-go results.
• We propose a training algorithm that minimizes the maximal R\'enyi divergence of order two and present techniques for gradient computation.