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Article
Physics, Multidisciplinary
Xiaoyun He et al.
Quantum Information Processing
(2022)
Review
Physics, Multidisciplinary
Kishor Bharti et al.
Summary: NISQ computers, composed of noisy qubits, are already being used in various fields. This review provides a comprehensive summary of NISQ computational paradigms and algorithms and introduces various benchmarking and software tools for programming and testing NISQ devices.
REVIEWS OF MODERN PHYSICS
(2022)
Article
Quantum Science & Technology
Xiaoyan Zhang
Summary: In this paper, we propose three methods for constructing quantum error-correcting codes (QECCs) through the quantum construction X of the Euclidean dual over finite fields. We then construct six new classes of QECCs. Additionally, the obtained QECCs have a higher rate than those available in the literature.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Nannan Lu et al.
Summary: The paper discusses the challenges of data driven fault diagnosis and proposes a deep domain adaptation architecture to address the issues related to labeled data and distribution discrepancy. Through adapting both conditional and marginal distributions with an adaptively weighted strategy, the proposed model demonstrates superiority over other intelligent fault diagnosis methods.
Article
Quantum Science & Technology
Tyler Volkoff et al.
Summary: This study demonstrates that reducing the dimensionality of parameter space by utilizing circuit modules containing spatially or temporally correlated gate layers can help avoid the vanishing gradient phenomenon. In the variational versions of Grover's algorithm, as the number of layers increases towards O(2n/2), the bounds on cost function variation suggest a transition from vanishing gradients to efficient trainability.
QUANTUM SCIENCE AND TECHNOLOGY
(2021)
Review
Computer Science, Artificial Intelligence
Wouter M. Kouw et al.
Summary: This review categorizes approaches in domain adaptation into sample-based, feature-based, and inference-based methods, highlighting the importance of conditions for formulating bounds on cross-domain generalization error.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Quantum Science & Technology
Xu Zhou et al.
Summary: Blind quantum machine learning is a novel secure quantum computation protocol that can protect client's privacy information and perform machine learning tasks using quantum technology. Researchers have designed two BQML protocols based on the quantum circuit model, one being half-blind and the other blind, effectively protecting Alice's privacy.
QUANTUM INFORMATION PROCESSING
(2021)
Article
Quantum Science & Technology
Avinash Chalumuri et al.
Summary: This study introduces the Quantum Multi-Class Classifier (QMCC), a hybrid model based on both quantum and classical computers for machine learning tasks, utilizing quantum properties such as superposition and entanglement to achieve high classification accuracy. Quantum simulations on benchmark datasets demonstrate that the proposed QMCC model achieved classification accuracy of 92.10% for the Iris dataset, 89.50% for the Banknote Authentication dataset, and 91.73% for the Wireless Indoor Localization dataset.
QUANTUM INFORMATION PROCESSING
(2021)
Article
Quantum Science & Technology
Kun Zhang et al.
Summary: This study successfully implemented optimized quantum search algorithms using three strategies, achieving higher success probabilities than previous works and demonstrating the first successful five-qubit search on the IBM quantum processor. The fast decay of the degraded ratio supported the divide-and-conquer strategy, indicating that the proposed strategies are beneficial for implementing quantum search algorithms in the post-NISQ era.
QUANTUM INFORMATION PROCESSING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yuang Liu et al.
Summary: SFDA is a source-free domain adaptation framework for semantic segmentation, which recovers and preserves source domain knowledge through knowledge transfer, extracts valuable information from the target domain for self-supervised learning, and seamlessly integrates pixel- and patch-level optimization objectives tailored for semantic segmentation in the framework.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Quantum Science & Technology
Yuxuan Du et al.
Summary: The study examines the learning potential of parameterized quantum circuits with gradient-based classical optimizers on noisy intermediate-scale quantum devices and finds that high gate noise, limited quantum measurements, and deep circuit depth may result in slower learning. It also proves that certain concept classes can be efficiently learned through this method.
Article
Computer Science, Interdisciplinary Applications
Amira Abbas et al.
Summary: This study investigates the advantage of near-term quantum computers for machine learning tasks by comparing the power and trainability of quantum machine learning models with classical neural networks. The effective dimension, a data-dependent measure based on Fisher information, is proposed to evaluate a model's ability to generalize on new data. Numerical demonstrations show that quantum neural networks outperform comparable feedforward networks in effective dimension and training speed, indicating an advantage for quantum machine learning validated on real quantum hardware.
NATURE COMPUTATIONAL SCIENCE
(2021)
Review
Computer Science, Artificial Intelligence
Wen Guan et al.
Summary: Machine learning has been widely used in high energy physics for supervised classification, while quantum computing is providing new possibilities for machine learning applications by exploiting hardware capacity. Research on quantum machine learning in high energy physics is expected to have broader applications in the future.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2021)
Article
Quantum Science & Technology
Margarite L. LaBorde et al.
QUANTUM INFORMATION PROCESSING
(2020)
Article
Quantum Science & Technology
Andrea Mari et al.
Article
Quantum Science & Technology
Pengcheng Zhu et al.
QUANTUM INFORMATION PROCESSING
(2020)
Article
Optics
Maria Schuld et al.
Article
Computer Science, Information Systems
Samuel Yen-Chi Chen et al.
Article
Physics, Multidisciplinary
Iris Cong et al.
Article
Optics
Maria Schuld et al.
Article
Computer Science, Artificial Intelligence
Mei Wang et al.
Article
Optics
K. Mitarai et al.
Article
Quantum Science & Technology
John Preskill
Review
Multidisciplinary Sciences
Jacob Biamonte et al.
Article
Optics
Guoming Wang
Review
Quantum Science & Technology
Maria Schuld et al.
QUANTUM INFORMATION PROCESSING
(2014)