Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network. Kawase, Y., Mitarai, K., & Fujii, K. February, 2022. arXiv:2202.04238 [quant-ph]Paper abstract bibtex t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.
@misc{kawase_parametric_2022,
title = {Parametric t-{Stochastic} {Neighbor} {Embedding} {With} {Quantum} {Neural} {Network}},
url = {http://arxiv.org/abs/2202.04238},
abstract = {t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.},
language = {en},
urldate = {2023-06-27},
publisher = {arXiv},
author = {Kawase, Yoshiaki and Mitarai, Kosuke and Fujii, Keisuke},
month = feb,
year = {2022},
note = {arXiv:2202.04238 [quant-ph]},
keywords = {Quantum Physics, Computer Science - Machine Learning},
annote = {Comment: 9 pages, 7 figures},
file = {Kawase et al. - 2022 - Parametric t-Stochastic Neighbor Embedding With Qu.pdf:/Users/georgehuang/Zotero/storage/QZA7B2B2/Kawase et al. - 2022 - Parametric t-Stochastic Neighbor Embedding With Qu.pdf:application/pdf},
}
Downloads: 0
{"_id":"6wyvBG56R7Qtd9ybc","bibbaseid":"kawase-mitarai-fujii-parametrictstochasticneighborembeddingwithquantumneuralnetwork-2022","author_short":["Kawase, Y.","Mitarai, K.","Fujii, K."],"bibdata":{"bibtype":"misc","type":"misc","title":"Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network","url":"http://arxiv.org/abs/2202.04238","abstract":"t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.","language":"en","urldate":"2023-06-27","publisher":"arXiv","author":[{"propositions":[],"lastnames":["Kawase"],"firstnames":["Yoshiaki"],"suffixes":[]},{"propositions":[],"lastnames":["Mitarai"],"firstnames":["Kosuke"],"suffixes":[]},{"propositions":[],"lastnames":["Fujii"],"firstnames":["Keisuke"],"suffixes":[]}],"month":"February","year":"2022","note":"arXiv:2202.04238 [quant-ph]","keywords":"Quantum Physics, Computer Science - Machine Learning","annote":"Comment: 9 pages, 7 figures","file":"Kawase et al. - 2022 - Parametric t-Stochastic Neighbor Embedding With Qu.pdf:/Users/georgehuang/Zotero/storage/QZA7B2B2/Kawase et al. - 2022 - Parametric t-Stochastic Neighbor Embedding With Qu.pdf:application/pdf","bibtex":"@misc{kawase_parametric_2022,\n\ttitle = {Parametric t-{Stochastic} {Neighbor} {Embedding} {With} {Quantum} {Neural} {Network}},\n\turl = {http://arxiv.org/abs/2202.04238},\n\tabstract = {t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. We visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.},\n\tlanguage = {en},\n\turldate = {2023-06-27},\n\tpublisher = {arXiv},\n\tauthor = {Kawase, Yoshiaki and Mitarai, Kosuke and Fujii, Keisuke},\n\tmonth = feb,\n\tyear = {2022},\n\tnote = {arXiv:2202.04238 [quant-ph]},\n\tkeywords = {Quantum Physics, Computer Science - Machine Learning},\n\tannote = {Comment: 9 pages, 7 figures},\n\tfile = {Kawase et al. - 2022 - Parametric t-Stochastic Neighbor Embedding With Qu.pdf:/Users/georgehuang/Zotero/storage/QZA7B2B2/Kawase et al. - 2022 - Parametric t-Stochastic Neighbor Embedding With Qu.pdf:application/pdf},\n}\n\n","author_short":["Kawase, Y.","Mitarai, K.","Fujii, K."],"key":"kawase_parametric_2022","id":"kawase_parametric_2022","bibbaseid":"kawase-mitarai-fujii-parametrictstochasticneighborembeddingwithquantumneuralnetwork-2022","role":"author","urls":{"Paper":"http://arxiv.org/abs/2202.04238"},"keyword":["Quantum Physics","Computer Science - Machine Learning"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"misc","biburl":"https://bibbase.org/network/files/MdCywnfEcRNyDtvne","dataSources":["yoC7aEqiuoMyGb9he"],"keywords":["quantum physics","computer science - machine learning"],"search_terms":["parametric","stochastic","neighbor","embedding","quantum","neural","network","kawase","mitarai","fujii"],"title":"Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network","year":2022}