Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Engineering Applications of Artificial Intelligence

Design of an efficient fault-tolerant quantum-computing circuit with quantum neural network learning

Chen Lin, Rucong Xu, Yun Li

Published Apr 17, 2025• 5 citations
Publisher
DOI
10.1016/j.engappai.2025.110808

Related papers

Design of Efficient Fault-Tolerant Quantum-Computing Circuits Using a Noise-Adaptive Dissipative Quantum Neural Network

Chen Lin, Rucong Xu, Yun Li

•Jan 1, 2024•0 citations

Pieceable fault-tolerant conversion between Steane and Reed-Muller quantum codes with neural network decoders

Chen Lin, Guowu Yang, Xiaoyu Song B

Designing efficient fault-tolerant circuits for converting between two quantum codes is an effective way to realize universal fault-tolerant quantum computing. In this work, we adopt the round-robin circuit construction to design a conversion circuit between CSS 7-qubit error-correcting code and Reed-Muller 15-qubit code. We take the pieceable fault-tolerant protocol to guarantee the fault tolerance of our conversion circuit. A neural network decoding scheme is proposed to suppress the propagation of quantum errors. Our decoding scheme can effectively reduce the damaging effects of imperfect syndrome extraction operations and predict possible logical errors that occurred after the error correction process. We provide a detailed description of our syndrome data model and analyze the depolarizing noise threshold of our conversion circuit through numerical simulations of the stabilizer circuit.

•May 15, 2021•0 citations

Design of a quantum convolutional neural network on quantum circuits

Jin Yan Zheng, Qing Zhong Gao, Jinhu Lü +3 more

Journal of the Franklin Institute•Aug 3, 2022•29 citations

Quantum Neural Network Parameter Estimation for Photovoltaic Fault Detection

Glen S. Uehara, Sunil Rao, Mathew Dobson +2 more

In this paper, we describe solar array monitoring using various machine learning methods including neural networks. We study fault detection using a quantum computer system and compare against results with a classical computer. We specifically propose a quantum circuit for a neural network implementation for Photovoltaic (PV) fault detection. The quantum circuit is designed for two qubits. Results and comparisons are presented for PV fault detection using a classical and quantum implementation of neural networks. In addition, simulations of a Quantum Neural Network are carried for a different number of qubits and results are presented for PV fault detection.

International Conference on Information, Intelligence, Systems & Applications (IISA)•Jul 12, 2021•20 citations

Efficient fault-tolerant quantum computing

Andrew M. Steane

arXiv:quant-ph/9809054•Nature•May 1, 1999•182 citations

Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization

Yusen Wu, Yukun Zhang, Chuan Xi Wang +1 more

Quantum process characterization is a fundamental task in quantum information processing, yet conventional methods, such as quantum process tomography, require prohibitive resources and lack scalability. Here, we introduce an efficient quantum process learning method specifically designed for short-time Hamiltonian dynamics. Our approach reconstructs an equivalent quantum circuit representation from measurement data of unknown Hamiltonian evolution without requiring additional assumptions and achieves polynomial sample and computational efficiency. Our results have broad applications in various directions. We demonstrate applications in quantum machine learning, where our protocol enables efficient training of variational quantum neural networks by directly learning unitary transformations. Additionally, it facilitates the prediction of quantum expectation values with provable efficiency and provides a robust framework for verifying quantum computations and benchmarking realistic noisy quantum hardware. This work establishes a new theoretical foundation for practical quantum dynamics learning, paving the way for scalable quantum process characterization in both near-term and fault-tolerant quantum computing.

arXiv:2503.24171•Mar 31, 2025•1 citation
Designing efficient fault-tolerant circuits for converting between two quantum codes is an effective way to realize universal fault-tolerant quantum computing. In this work, we adopt the round-robin circuit construction to design a conversion circuit between CSS 7-qubit error-correcting code and Reed-Muller 15-qubit code. We take the pieceable fault-tolerant protocol to guarantee the fault tolerance of our conversion circuit. A neural network decoding scheme is proposed to suppress the propagation of quantum errors. Our decoding scheme can effectively reduce the damaging effects of imperfect syndrome extraction operations and predict possible logical errors that occurred after the error correction process. We provide a detailed description of our syndrome data model and analyze the depolarizing noise threshold of our conversion circuit through numerical simulations of the stabilizer circuit.
In this paper, we describe solar array monitoring using various machine learning methods including neural networks. We study fault detection using a quantum computer system and compare against results with a classical computer. We specifically propose a quantum circuit for a neural network implementation for Photovoltaic (PV) fault detection. The quantum circuit is designed for two qubits. Results and comparisons are presented for PV fault detection using a classical and quantum implementation of neural networks. In addition, simulations of a Quantum Neural Network are carried for a different number of qubits and results are presented for PV fault detection.
Quantum process characterization is a fundamental task in quantum information processing, yet conventional methods, such as quantum process tomography, require prohibitive resources and lack scalability. Here, we introduce an efficient quantum process learning method specifically designed for short-time Hamiltonian dynamics. Our approach reconstructs an equivalent quantum circuit representation from measurement data of unknown Hamiltonian evolution without requiring additional assumptions and achieves polynomial sample and computational efficiency. Our results have broad applications in various directions. We demonstrate applications in quantum machine learning, where our protocol enables efficient training of variational quantum neural networks by directly learning unitary transformations. Additionally, it facilitates the prediction of quantum expectation values with provable efficiency and provides a robust framework for verifying quantum computations and benchmarking realistic noisy quantum hardware. This work establishes a new theoretical foundation for practical quantum dynamics learning, paving the way for scalable quantum process characterization in both near-term and fault-tolerant quantum computing.