machine learning quantum error correction

MACHINE LEARNING QUANTUM ERROR CORRECTION CODE

2018 Chamberland and Ronagh 2018 Ni 2018 Davaasuren et al. Recently, there is an increase in the development of neural network–based decoders that either consist exclusively of neural networks (Torlai and Melko 2017 Krastanov and Jiang 2017) or a classical module working together with neural networks (Varsamopoulos et al. 2014), and the renormalization group (RG) algorithm (Duclos-Cianci and Poulin 2010a Duclos-Cianci and Poulin 2010b).

Various classical decoding algorithms have been proposed over the years with a few examples of classical decoding algorithms being the Blossom algorithm (Edmonds 1965 Kolmogorov 2009 Fowler 2015 Fowler 2013), the maximum-likelihood algorithm (Bravyi et al. Therefore, if the process of decoding exceeds the error correction time budget, errors will accumulate to the point that the error-free state cannot be retrieved. The importance of high-speed and accurate decoding lies in the fact that the time budget allowed for error correction is small, since qubits lose their state rapidly. Decoding involves the process of identifying the errors that occur in the quantum system and proposing corrections that keep the quantum state error-free. Note that, in fault-tolerant techniques, errors can occur in the quantum system, but do not affect the quantum state in a catastrophic manner (Nielsen and Chuang 2002).Ī critical sub-routine of QEC is decoding. Through active error correction and fault-tolerant mechanisms that control error propagation and keep the error rates low, we can have the error-free desired state. Altering the quantum state is perceived as errors generated in the quantum system. The need for QEC arises from the unavoidable coupling of the quantum system with the environment, which causes the qubit state to be altered (decohere). However, the way that quantum computing is currently envisioned, QEC is necessary for reliable quantum computation and storage. Quantum error correction (QEC) is for now considered to be the most time- and resource-consuming procedure in quantum computation. We show that a similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network–based decoders.

MACHINE LEARNING QUANTUM ERROR CORRECTION CODE

We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the blossom algorithm and a neural network–based decoder.

In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network–based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the renormalization group (RG) decoders. Note that successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network–based decoders.

However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. There has been a rise in decoding quantum error correction codes with neural network–based decoders, due to the good decoding performance achieved and adaptability to any noise model.

Machine learning quantum error correction