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Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow [article]

Zhiding Liang, Zhepeng Wang, Junhuan Yang, Lei Yang, Jinjun Xiong, Yiyu Shi, Weiwen Jiang
2021 arXiv   pre-print
In this paper, we utilize the recent QNN framework, QuantumFlow, as a case study.  ...  Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.  ...  ACKNOWLEDGMENT Access to the IBM Q Network was obtained through the IBM Q Hub at NC State. Special thanks to Zhirui Hu for her extensive discussion and help with this work.  ... 
arXiv:2109.03430v2 fatcat:rc2vb6oaurfjxoe4t5yk4termu

A co-design framework of neural networks and quantum circuits towards quantum advantage

Weiwen Jiang, Jinjun Xiong, Yiyu Shi
2021 Nature Communications  
Coupled with a novel algorithm, the cost complexity of the unitary matrices-based neural computation can be reduced from O(n) in classical computing to O(polylog(n)) in quantum computing.  ...  designs a neural network suitable for quantum circuit.  ...  Source data can be accessed via https://github.com/weiwenjiang/QuantumFlow/blob/master/Quantumflow_Data.xlsx.  ... 
doi:10.1038/s41467-020-20729-5 pmid:33495480 fatcat:p24jhbix3fdjljdkj7c4tgrsdu

QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity [article]

Samuel A. Stein, Betis Baheri, Daniel Chen, Ying Mao, Qiang Guan, Ang Li, Shuai Xu, Caiwen Ding
2022 arXiv   pre-print
In this work, we propose a novel architecture QuClassi, a quantum neural network for both binary and multi-class classification.  ...  When comparing to traditional deep neural networks, QuClassi achieves a comparable performance with 97.37% fewer parameters.  ...  We thank the IBM Quantum Hub to provide the quantum-ready platform for our experiments.  ... 
arXiv:2103.11307v3 fatcat:6ejez757rncirpowzzzwgboyw4

QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates [article]

Cheng Chu, Nai-Hui Chia, Lei Jiang, Fan Chen
2022 arXiv   pre-print
Our source code is available and can be found in [1]  ...  Despite potential quantum supremacy, state-of-the-art quantum neural networks (QNNs) suffer from low inference accuracy.  ...  On the contrary, a classical 𝑛-bit register file can store only one of these 2 𝑛 states. Quantum Neural Networks.  ... 
arXiv:2206.01345v1 fatcat:kxnoyh7qznhljby7bpmt23ioge

Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs [article]

Zhepeng Wang, Zhiding Liang, Shanglin Zhou, Caiwen Ding, Yiyu Shi, Weiwen Jiang
2021 arXiv   pre-print
A fundamental question in quantum deep learning arises: what is the best quantum neural architecture?  ...  More specifically, VQC can apply real-valued weights but suffer from being extended to multiple layers, while QuantumFlow can build a multi-layer network efficiently, but is limited to use binary weights  ...  of qubits and high noise on each qubit, resulting in the QNN training on quantum computers being unstable and not scalable.  ... 
arXiv:2109.03806v2 fatcat:3g77zxuyubdarh6mtpfwd44p6m

Variational Quantum Pulse Learning [article]

Zhiding Liang, Hanrui Wang, Jinglei Cheng, Yongshan Ding, Hang Ren, Zhengqi Gao, Zhirui Hu, Duane S. Boning, Xuehai Qian, Song Han, Weiwen Jiang, Yiyu Shi
2022 arXiv   pre-print
However, VQC has limited flexibility and expressibility due to limited number of parameters, e.g. only one parameter can be trained in one rotation gate.  ...  A large body of existing works focus on using variational quantum algorithms on the gate level for machine learning tasks, such as the variational quantum circuit (VQC).  ...  We acknowledge the use of IBM Quantum services for this work.  ... 
arXiv:2203.17267v3 fatcat:rd2sm2v77rdovd2quf32suckw4

Κβαντικά νευρωνικά δίκτυα για εφαρμογές στο εγγύς μέλλον

Μαρία Κοφτερού
2020
This effort particularly focuses on Quantum Neural Networks proposed in the scheme of Variational Quantum Circuits trained with hybrid methods.  ...  Learning, with a focus on supervised learning, and follow the most recent developments.  ...  of Quantum Neural Networks and concern 16 qubits at best case.  ... 
doi:10.26262/heal.auth.ir.310769 fatcat:ovy262m7zbapbd7gjdzk44jud4

Roadmap on Signal Processing for Next Generation Measurement Systems [article]

D.K. Iakovidis, M. Ooi, Y.C. Kuang, S. Damidenko, A. Shestakov, V. Sinistin, M. Henry, A. Sciacchitano, A. Discetti, S. Donati, M. Norgia, A. Menychtas (+22 others)
2021 pre-print
Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification.  ...  The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing.  ...  Acknowledgments Acknowledgments This work was supported in part by the Royal Society of New Zealand Te Apārangi through a Rutherford Discovery Fellowship conferred to Melanie Ooi.  ... 
doi:10.1088/1361-6501/ac2dbd arXiv:2111.02493v1 fatcat:fu2ftpsdkffedaqh6xes3a4prm

Roadmap on signal processing for next generation measurement systems

D. K. Iakovidis, M. Ooi, Y. C. Kuang, S. Demidenko, A. Shestakov, V. Sinitsin, M. Henry, A. Sciacchitano, S. Discetti, S. Donati, M. Norgia, A. Menychtas (+22 others)
2022
Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification.  ...  The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing.  ...  Acknowledgments Acknowledgments This work was supported in part by the Royal Society of New Zealand Te Apārangi through a Rutherford Discovery Fellowship conferred to Melanie Ooi.  ... 
doi:10.5445/ir/1000141111 fatcat:6k4lcjmmvvfgjbgs3sxl5qnf6m