Parametric t-stochastic neighbor embedding with quantum neural network

Kawase, Yoshiaki; Mitarai, Kosuke; Fujii, Keisuke

doi:10.1103/physrevresearch.4.043199

Cited by 7 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides PCA, DR can be achieved with a series of other algorithms, both unsupervised and supervised, including, e.g. quantum linear discriminant analysis [188][189][190], quantum slow feature analysis [191,192] and t-stochastic neighbor embedding [193,194]. DR can therefore be a powerful tool for QML, especially when used in combination with quantum simulators [119,153,177] and quantum emulators [195], provided that their limitations are taken into account.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Machine and quantum learning for diamond-based quantum applications

Stone

Bradac²

2023

Mater. Quantum. Technol.

View full text Add to dashboard Cite

In recent years, machine and quantum learning have gained considerable momentum sustained by growth in computational power and data availability and have shown exceptional aptness for solving recognition- and classification-type problems, as well as problems that require complex, strategic planning. In this work, we discuss and analyze the role machine and quantum learning are playing in the development of diamond-based quantum technologies. This matters as diamond and its optically-addressable spin defects are becoming prime hardware candidates for solid state-based applications in quantum information, computing and metrology. Through a selected number of demonstrations, we show that machine and quantum learning are leading to both practical and fundamental improvements in measurement speed and accuracy. This is crucial for quantum applications, especially for those where coherence time and signal-to-noise ratio are scarce resources. We summarize some of the most prominent machine and quantum learning approaches that have been conducive to the presented advances and discuss their potential for proposed and future quantum applications.

show abstract

Section: Dimensionality Reductionmentioning

confidence: 99%

Machine and quantum learning for diamond-based quantum applications

Stone

Bradac²

2023

Mater. Quantum. Technol.

View full text Add to dashboard Cite

show abstract

“…For example, Kawase [4] et al proposed to use the parameter t-SNE of quantum neural network to reflect the properties of high-dimensional quantum data on low-dimensional data to improve the efficiency of neural network in processing data. osakabe [5] et al proposed a Hebb rule-based learning method for quantum neural network, in which Hebb and inverse Hebb rules improve the learning performance of neural network.…”

Section: Introductionmentioning

confidence: 99%

Quantum fuzzy neural network based on fuzzy number

Yang¹

2023

FCIS

View full text Add to dashboard Cite

Neural network is one of the AI algorithms commonly used to process data, and has an extremely important position in scenarios such as image recognition, classification, and machine translation. With the increase of data volume explosion, the required computing power of neural networks is also significantly increased. The emergence of quantum neural networks improves the computational power of neural networks, but the accuracy of neural networks and quantum neural networks is not high in the face of the complexity and uncertainty of big data. In order to improve the efficiency and accuracy, the cross-fusion of "fuzzy number theory + quantum neural network" is proposed to study the quantum fuzzy neural network (FQNN) based on fuzzy number. The Gaussian fuzzy function is used to generate the corresponding fuzzy affiliation matrix to describe the uncertain information in the data. The fuzzy independent variables are trained through the FQNN model, and the model is output after changing the parameters of the quantum forward propagation layer. Simulation experiments show that the quantum fuzzy neural network model based on fuzzy number is more efficient and accurate in this study compared with the quantum neural network model.

show abstract