• 전자통신동향분석
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• 제37권2호
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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.

• 한국광학회지
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• 제29권2호
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• pp.58-63
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• 2018

최근 양자프로세서와 관련한 연구개발이 본격화되면서 실제 수행가능 한 양자계산량도 계속 증가하고 있다. 이에 양자컴퓨팅은 본격적으로 활용화단계로 진입하고 있다고 볼 수 있다. 다만 아직은 큰 규모의 양자컴퓨팅이 가능하지 않기 때문에 작은 규모의 문제이지만 고전컴퓨팅으로는 해결하기 힘들고, 양자컴퓨팅으로는 효과적으로 계산할 수 있는 문제를 대상으로 하고 있다. 본 연구에서는 이와 관련하여 양자컴퓨터를 이용한 작은 크기의 양자시뮬레이션분야의 실질적인 계산성능에 대한 정량적인 분석 결과를 보고한다. 분석결과 현재까지의 결함허용 기반 양자컴퓨팅은 양자계산성능의 측면에서 다양한 문제점을 갖고 있음을 확인하였다. 본 연구에서는 이와 관련하여 향후 수행해야 할 연구개발 내용을 정리하였다.

• 전자통신동향분석
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• 제38권5호
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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.