• Electronics and Telecommunications Trends
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• v.37 no.2
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• pp.1-10
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• 2022

Similar to present computers, quantum computers comprise quantum bits (qubits) and an operating system. However, because the quantum states are fragile, we need to correct quantum errors using entangled physical qubits with quantum error correction (QEC) codes. The combination of entangled physical qubits with a QEC protocol and its computational model are called a logical qubit and fault-tolerant quantum computation, respectively. Thus, QEC is the heart of fault-tolerant quantum computing and overcomes the limitations of noisy intermediate-scale quantum computing. Therefore, in this study, we briefly survey the status of QEC codes and the physical implementation of logical qubit over various qubit technologies. In summary, we emphasize 1) the error threshold value of a quantum system depends on the configurations and 2) therefore, we cannot set only any specific theoretical and/or physical experiment suggestion.

• Korean Journal of Optics and Photonics
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• v.29 no.2
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• pp.58-63
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• 2018

As the quantum volume increases, we are about to use quantum computers for real applications. Therefore, it is necessary to investigate how much quantum-computational gain is achievable in the near future. In this work, we analyze a fault-tolerant quantum computing method for near-term applications such as the ground-state estimation problem. Based on quantitative analysis, we find that it is still necessary to improve the current fault-tolerant quantum computing. This work also discusses which parts should be improved to improve quantum computing performance.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.34-50
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• 2023

Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.