A review of Quantum Neural Networks: Methods, Models, Dilemma

Zhao, Renxin; Wang, Shi

doi:10.48550/arxiv.2109.01840

Cited by 7 publications

(2 citation statements)

References 58 publications

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“…The VQA approaches the target function using learning parameters with quantum characteristics, such as reversible linear gate operations and multi-layer structures that use layers of engagement. VQC has been used to replace existing Convolutional Neural Networks (CNNs) [60,61], with QNNs being defined as a subset of VQA, and a general expression of the QNN quantum circuit is presented in Figure 5 [62,63]. While VQA continues to be a prominent approach for designing QNNs, it also inherits some of its drawbacks.…”

Section: Quantum Machine Learning-the Basic Conceptmentioning

confidence: 99%

Quantum Machine Learning—An Overview

2023

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Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning.

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Section: Quantum Machine Learning-the Basic Conceptmentioning

confidence: 99%

Quantum Machine Learning—An Overview

2023

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“…Recently, topologically protected modes have been demonstrated using waveguide arrays on a polymer chip [15]. Our method could be of use in the implementation of quantum Boson sampling [16] and quantum neural networks [17], as well as opening avenues to explore complex topological geometries [18], [19], [34] in multi-dimensional, multi-port waveguide systems.…”

Section: Introductionmentioning

confidence: 96%

Compressive Single-Pixel Read-Out of Single-Photon Quantum Walks on a Polymer Photonic Chip

Chandra

Frank

et al. 2023

IEEE Photonics J.

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Quantum photonic devices operating in the single photon regime require the detection and characterization of quantum states of light. Chip-scale, waveguide-based devices are a key enabling technology for increasing the scale and complexity of such systems. Collecting single photons from multiple outputs at the end-face of such a chip is a core task that is frequently non-trivial, especially when output ports are densely spaced. We demonstrate a novel, inexpensive method to efficiently image and route individual output modes of a polymer photonic chip, where single photons undergo a quantum walk. The method makes use of single-pixel imaging (SPI) with a digital micromirror device (DMD). By implementing a series of masks on the DMD and collecting the reflected signal into single-photon detectors, the spatial distribution of the single photons can be reconstructed with high accuracy. We also demonstrate the feasibility of optimization strategies based on compressive sensing.

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Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges

Groppe,

Çalıkyılmaz

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A review of Quantum Neural Networks: Methods, Models, Dilemma

Cited by 7 publications

References 58 publications

Quantum Machine Learning—An Overview

Quantum Machine Learning—An Overview

Compressive Single-Pixel Read-Out of Single-Photon Quantum Walks on a Polymer Photonic Chip

Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges

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