Quantum Optimization for Training Quantum Neural Networks

Understand

Training quantum neural networks (QNNs) using gradient-based or gradient-free classical optimisation approaches is severely impacted by the presence of barren plateaus in the cost landscapes.

• In this paper, we devise a framework for leveraging quantum optimisation algorithms to find optimal parameters of QNNs for certain tasks.
• To achieve this, we coherently encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters.
• The parameters are tuned with an iterative quantum optimisation structure using adaptively selected Hamiltonians.