Topological quantum error-correcting codes are a promising candidate for building fault-tolerant quantum computers. Decoding topological codes optimally, however, is known to be a computationally hard problem. Various decoders have been proposed that achieve approximately optimal error thresholds. Due to practical constraints, it is not known if there exists an obvious choice for a decoder. In this paper, we introduce a framework which can combine arbitrary decoders for any given code to significantly reduce the logical error rates. We rely on the crucial observation that two different decoding techniques, while possibly having similar logical error rates, can perform differently on the same error syndrome. We use machine learning techniques to assign a given error syndrome to the decoder which is likely to decode it correctly. We apply our framework to an ensemble of Minimum-Weight Perfect Matching (MWPM) and Hard-Decision Re-normalization Group (HDRG) decoders for the surface code in the depolarizing noise model. Our simulations show an improvement of 38.4%, 14.6%, and 7.1% over the pseudo-threshold of MWPM in the instance of distance 5, 7, and 9 codes, respectively. Lastly, we discuss the advantages and limitations of our framework and applicability to other error-correcting codes. Our framework can provide a significant boost to error correction by combining the strengths of various decoders. In particular, it may allow for combining very fast decoders with moderate error-correcting capability to create a very fast ensemble decoder with high error-correcting capability.

Nowadays a major class of wireless sensor network (WSN) applications required a minimum quality of service parameters to be satisfied while the wireless sensor nodes might be mobile. Most of the standard WSN routing algorithms greedily choose the neighbor node with the best quality of service (QoS) parameter(s) as a next hop. However, the data packet might be able to be routed through other neighbors as it might require less QoS. So the energy of the neighbor node with the best QoS will deplete earlier than other nodes which will result in the reduction of network lifetime. Therefore, it is important for WSN QoS routing protocols to efficiently balance energy and other resources consumption throughout the network. In this paper, we proposed EQR-RL, energy-aware QoS routing protocol in WSNs using reinforcement learning. We compare the network performance of our proposed protocol with three other protocols (QoS-AODV, RSSI and RL-QRP). The packet delivery ratio, average end-to-end delay and impact of the different traffic load on average end-to-end delay are investigated. Simulation results indicate the superiority of our proposed protocol over two others by considering different network traffic load and node mobility in terms of average end-toend delay and packet delivery ratio.

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