Piecewise Polynomial Tensor Network Quantum Feature Encoding
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913633651392512 |
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| author | Ali, Mazen Kabel, Matthias |
| author_facet | Ali, Mazen Kabel, Matthias |
| contents | This work introduces a novel method for embedding continuous variables into quantum circuits via piecewise polynomial features, utilizing low-rank tensor networks. Our approach, termed Piecewise Polynomial Tensor Network Quantum Feature Encoding (PPTNQFE), aims to broaden the applicability of quantum algorithms by incorporating spatially localized representations suited for numerical applications like partial differential equations and function regression. We demonstrate the potential of PPTNQFE through efficient point evaluations of solutions of discretized differential equations and in modeling functions with localized features such as jump discontinuities. While promising, challenges such as unexplored noise impact and design of trainable circuits remain. This study opens new avenues for enhancing quantum models with novel feature embeddings and leveraging TN representations for a wider array of function types in quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07671 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Piecewise Polynomial Tensor Network Quantum Feature Encoding Ali, Mazen Kabel, Matthias Quantum Physics This work introduces a novel method for embedding continuous variables into quantum circuits via piecewise polynomial features, utilizing low-rank tensor networks. Our approach, termed Piecewise Polynomial Tensor Network Quantum Feature Encoding (PPTNQFE), aims to broaden the applicability of quantum algorithms by incorporating spatially localized representations suited for numerical applications like partial differential equations and function regression. We demonstrate the potential of PPTNQFE through efficient point evaluations of solutions of discretized differential equations and in modeling functions with localized features such as jump discontinuities. While promising, challenges such as unexplored noise impact and design of trainable circuits remain. This study opens new avenues for enhancing quantum models with novel feature embeddings and leveraging TN representations for a wider array of function types in quantum machine learning. |
| title | Piecewise Polynomial Tensor Network Quantum Feature Encoding |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2402.07671 |