Quantum error correction for the toric code using deep reinforcement learning

Philip Andreasson, Joel Johansson, Simon Liljestrand, and Mats Granath

Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden

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We implement a quantum error correction algorithm for bit-flip errors on the topological toric code using deep reinforcement learning. An action-value Q-function encodes the discounted value of moving a defect to a neighboring site on the square grid (the action) depending on the full set of defects on the torus (the syndrome or state). The Q-function is represented by a deep convolutional neural network. Using the translational invariance on the torus allows for viewing each defect from a central perspective which significantly simplifies the state space representation independently of the number of defect pairs. The training is done using experience replay, where data from the algorithm being played out is stored and used for mini-batch upgrade of the Q-network. We find performance which is close to, and for small error rates asymptotically equivalent to, that achieved by the Minimum Weight Perfect Matching algorithm for code distances up to $d=7$. Our results show that it is possible for a self-trained agent without supervision or support algorithms to find a decoding scheme that performs on par with hand-made algorithms, opening up for future machine engineered decoders for more general error models and error correcting codes.

Quantum computers are much more susceptible to noise than present day classical computers. To construct a universal quantum computer it will be necessary to incorporate an auxiliary system for error correction, otherwise errors would quickly accumulate and ruin the calculation. Errors of the quantum bits, or qubits, are the quantum analogs of bit flip errors that also occur in a classical computer. However, in contrast to the classical bits, it is not possible to get an exact diagnosis of qubit errors without destroying the stored quantum information. Instead the error correction has to rely on partial information known as the syndrome and based on this suggest the best way to correct errors. Because of the incomplete information this is a very challenging problem requiring sophisticated algorithms known as error decoders.
In this paper we develop an error decoder based on artificial intelligence. We use deep reinforcement learning, which is the same framework that has recently achieved super-human performance in playing computer and board games. By exploration, experience is gathered and used to train an artificial neural network that can suggest the best error correction to perform for any given syndrome. Our results show that it is possible for a self-trained agent without supervision or support algorithms to find a decoding scheme that performs on par with hand-made algorithms, opening up for future machine engineered decoders for more general types of noise and error correcting codes.

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