#Machine learning quantum error correction how to#
At present, one of the research focuses of quantum error correction is how to decode quickly and stably.įor color codes, Sarvepalli proposed an alternating iterative error correction method, with an error threshold of 7.8%.
#Machine learning quantum error correction code#
Another widely concerned topological code is the color code, which allows the horizontal implementation of the whole Clifford gate group, and it can expand from 2D to higher dimensions. The surface code proposed by Mariantoni belongs to topological coding.
In terms of decoding, the topological stabilizer code can combine its topological structure with the mapped geometry to construct a variety of decoding methods. The topological stabilizer code is an error correction code based on the stabilizer code, which can be mapped to the geometric model. The stabilizer composed of Pauli matrix can encode and detect errors more quickly. Most of the current quantum error correction schemes focus on a coding scheme called stabilizer code proposed by Gottesman.
However, Shor’s structure is not convenient to expand, and it is difficult to find a structure to protect a specified number of qubits. Nine qubits were successfully used to encode one logic bit. Studying the efficiency and algorithm of quantum error correction coding is an important topic in the field of quantum error correction, and it is a difficult problem that must be solved in the construction of quantum computer.īased on the achievements and experience in the field of traditional communication, Shor proposed a quantum error correction scheme in 1994 to reduce the decoherence of qubits during storage. To ensure that the information of the quantum state will not change after transmission or can still be corrected after error, we must find a fast and accurate method of error correction qubits to protect the quantum information from irresistible external interference and ensure that the information transmission can be completed within the decoherence time. As an important part of quantum computing, error correction provides the guarantee of quantum information transmission for the new theory in the field of quantum information and quantum computing. IntroductionĬolor code is a kind of quantum error correction topological coding. We numerically show that our decoding method can achieve a fast prediction speed after training and a better error correction threshold. Our results show that through unsupervised learning, when iterative training is at least 300 times, a self-trained model can improve the error correction accuracy to 96.5%, and the error correction speed is about 13.8% higher than that of the traditional algorithm. We project the color code onto the surface code, use the deep Q network to iteratively train the decoding process of the color code and obtain the relationship between the inversion error rate and the logical error rate of the trained model and the performance of error correction. Machine learning is considered one of the most suitable solutions for decoding task of color code. There are many decoding methods that have been suggested to solve this problem. Traditional decoding methods are limited by computing power and data scale, which restrict the decoding efficiency of color codes.
Solving for quantum error correction remains one of the key challenges of quantum computing.
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