# Global optimization of quantum dynamics with AlphaZero deep exploration

## TL;DR

This paper introduces a novel AlphaZero-based deep exploration algorithm for quantum control optimization, eliminating the need for initial guesses and discovering hidden structures in solutions.

## Contribution

It adapts AlphaZero for quantum dynamics, enabling systematic, transferably optimized solutions without heuristics, and uncovers hidden symmetries in quantum control landscapes.

## Key findings

- Significant improvement in solution quality and quantity.
- Ability to discover hidden structures and symmetries.
- Effective across multiple quantum control problems.

## Abstract

While a large number of algorithms for optimizing quantum dynamics for different objectives have been developed, a common limitation is the reliance on good initial guesses, being either random or based on heuristics and intuitions. Here we implement a tabula rasa deep quantum exploration version of the Deepmind AlphaZero algorithm for systematically averting this limitation. AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters. AlphaZero achieves substantial improvements in both the quality and quantity of good solution clusters compared to earlier methods. It is able to spontaneously learn unexpected hidden structure and global symmetry in the solutions, going beyond even human heuristics.