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.
Highlights
The agent must select an action that updates the unitary representing the state of the system
AlphaZero is a policy improvement algorithm that combines a neural network with a Monte Carlo Tree Search (MCTS) as depicted in Fig. 1b, c
A MCTS is a way of looking several steps ahead by only visiting a small subset of possible future states
Summary
The agent must select an action that updates the unitary representing the state of the system. Similar to ref., 45 we used a population size of 70 and a mutation probability of 0.001. AlphaZero is a policy improvement algorithm that combines a neural network with a Monte Carlo Tree Search (MCTS) as depicted, c.47,48 The neural network maps from states to policies p 1⁄4 ðp[1]; p2; 1⁄4 Þ and values v.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
