Avoiding local minima in Variational Quantum Algorithms with Neural Networks

Understand

Variational Quantum Algorithms have emerged as a leading paradigm for near-term quantum computation.

• In such algorithms, a parameterized quantum circuit is controlled via a classical optimization method that seeks to minimize a problem-dependent cost function.
• Although such algorithms are powerful in principle, the non-convexity of the associated cost landscapes and the prevalence of local minima means that local optimization methods such as gradient descent typically fail to reach good solutions.
• In this work we suggest a method to improve gradient-based approaches to variational quantum circuit optimization, which involves coupling the output of the quantum circuit to a classical neural network.