Error correction for depolarising noise on a quantum system using deep RL
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2019
Författare
Fitzek, David
Eliasson, Mattias
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
We implement a quantum error correction algorithm for the depolarized noise model on the
topological toric code using deep reinforcement learning. An action-value Q-function encodes
the discounted value of applying any of the three pauli operators ( x, y, z) to a specific qubit
given the entire set of excitations on the torus. The Q-network is defined by a convolutional
neural network (CNN), with one fully connected layer at the end. Considering the translational
invariance of the torics code we can center every qubit and therefore naturally simplify the state
space representation independently of the number of excitation pairs. We train the agent using
experience replay and store the state from the algorithm to use it for mini-batch updates of the
Q-network. We conclude that this approach, considering all three pauli operators, outperforms
the Minimum Weight Perfect Matching (MWPM) algorithm and for small error rates is close to
the asymptotic solution for very low error probabilites.