A Survey of Quantum Error Correction

Matsumoto, Ryutaroh; Hagiwara, Manabu

doi:10.1587/transfun.2021eai0001

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…which implies that the size of qualified set is the same as (28), that is, that of our proposed quantum secret sharing constructed from the Reed-Solomon code. The bit-size of a secret and each share and the size of qualified sets, forbidden sets and advance-shareable sets in our proposed quantum secret sharing scheme constructed from the Reed-Solomon codes and a ramp version [30] of Shamir's scheme can be summarized in Table 1.…”

Section: Our Proposed Encoding Methods Constructed From the Reed-solo...mentioning

confidence: 99%

Advance Sharing of Quantum Shares for Classical Secrets

Miyajima

Matsumoto

2022

IEEE Access

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Secret sharing schemes for classical secrets can be classified into classical secret sharing schemes and quantum secret sharing schemes. Classical secret sharing has been known to be able to distribute some shares before a given secret. On the other hand, quantum mechanics extends the capabilities of secret sharing beyond those of classical secret sharing. We propose quantum secret sharing with the capabilities in designing of access structures more flexibly and realizing higher efficiency beyond those of classical secret sharing, that can distribute some shares before a given secret.

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Section: Our Proposed Encoding Methods Constructed From the Reed-solo...mentioning

confidence: 99%

Advance Sharing of Quantum Shares for Classical Secrets

Miyajima

Matsumoto

2022

IEEE Access

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“…взаимодействие: состоит из двух гамильтонианов и равняется H in = H overlap + H pair . Первый гамильтониан «определяет, перекрываются ли какие-либо два участка молекулы, и впоследствии добавляет штраф за перекрытие», а второй «учитывает взаимодействие между двумя не связанными ближайшими соседними участками» [15].…”

Section: теоретический анализunclassified

“…В работе [15] Новизна. Данная работа описывает алгоритм оптимизации квантового контура, кодирующего часть пептида азурина [18].…”

Section: теоретический анализunclassified

Protein folding quantum circuit quantum circuit for bio material modelling compression

Lisnichenko

Protasov

2023

Izv. vysš. učebn. zaved., Mater. èlektron. teh.

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Computational material science aims to simulate substances to understand their physical properties. Bioelectronics is an interdisciplinary field that studies biological material from the conductivity point of view. In case of proteins, the folding is an important feature that directly influences physical and chemical properties. The folding modelling is a hard task. The enormous number of degrees of freedom makes modelling impossible for classical computation due to resource limits. Quantum computations aim to process multidimensional data with logarithmic growth of quantum bits. Quantum operators (gates) form quantum programs, known as circuits that process the input data. In real quantum computers, the gates are noisy and expensive to execute. Thus, it is essential to reduce the number of quantum gates both for the quality of the result and the cost of computations. This work describes an approach to decrease the number of quantum gates based on their mathematical property. The matrix properties form the first optimization technique. In this case, the optimized quantum circuit predicts precisely the same protein folding as the not optimized circuit predicts. This happens because both of the circuits are mathematically equivalent. The removal of weakly-parametrized gates forms the second optimization technique. In such case the optimized quantum circuit calculates the approximate protein folding. The error depends on parameter’s amplitude of the gates. The first technique allows to decrease the circuit depth from 631 to 629 gates while modelling the part of Azurin peptide. The second technique allows to decrease the depth to 314 gates with the threshold parameter value 0.4 radians.

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“…Devitt et al [100], Endo et al [101], and Matsumoto and Hagiwara [102] survey different quantum error handling methods. However, they focus on quantum error correction and quantum gate error mitigation, and only briefly describe REM.…”

Section: Related Workmentioning

confidence: 99%

Configurable Readout Error Mitigation in Quantum Workflows

et al. 2022

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Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, configuration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-specific configuration options. Quantum applications comprise many classical pre- and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workflow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum workflows comprising configurable readout error mitigation tasks. Based on the method configuration, these workflows can then be automatically refined into executable workflow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain.

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A Survey of Quantum Error Correction

Abstract: This paper surveys development of quantum error correction. With the familiarity with conventional coding theory and tensor product in multi-linear algebra, this paper can be read in a self-contained manner.

Cited by 8 publications

References 44 publications

Advance Sharing of Quantum Shares for Classical Secrets

Advance Sharing of Quantum Shares for Classical Secrets

Protein folding quantum circuit quantum circuit for bio material modelling compression

Configurable Readout Error Mitigation in Quantum Workflows

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