Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

In order to implement large scale quantum computations it is necessary to be able to store and manipulate quantum information in a manner that is robust to the unavoidable errors introduced through interaction of the physical qubits with a noisy environment. The known strategy for achieving such robustness is to encode a single logical qubit into the state of many physical qubits, via a quantum error correcting code, from which it is possible to actively diagnose and correct errors that may occur [1, 2]. While many quantum error correcting codes exist, topological quantum codes [1-8], in which only local operations are required to diagnose and correct errors, are of particular interest as a result of their experimental feasibility [9-15]. In particular, the surface code has emerged as an especially promising candidate for large-scale fault-tolerant quantum computation, due to the combination of its comparatively low overhead and locality requirements, coupled with the availability of convenient strategies for the implementation of all required logical gates [16, 17]. In fact, current road maps towards the realization of robust quantum computing have identified surface code based approaches as the most feasible methodology for achieving this goal [18].