Alleviating CoD in Renewable Energy Profile Clustering Using an Optical Quantum Computer
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915829404139520 |
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| author | Liu, Chengjun Xu, Yijun Gu, Wei Sun, Bo Wen, Kai Lu, Shuai Mili, Lamine |
| author_facet | Liu, Chengjun Xu, Yijun Gu, Wei Sun, Bo Wen, Kai Lu, Shuai Mili, Lamine |
| contents | The traditional clustering problem of renewable energy profiles is typically formulated as a combinatorial optimization that suffers from the Curse of Dimensionality (CoD) on classical computers. To address this issue, this paper first proposed a kernel-based quantum clustering method. More specifically, the kernel-based similarity between profiles with minimal intra-group distance is encoded into the ground-state of the Hamiltonian in the form of an Ising model. Then, this NP-hard problem can be reformulated into a Quadratic Unconstrained Binary Optimization (QUBO), which a Coherent Ising Machine (CIM) can naturally solve with significant improvement over classical computers. The test results from a real optical quantum computer verify the validity of the proposed method. It also demonstrates its ability to address CoD in an NP-hard clustering problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23569 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Alleviating CoD in Renewable Energy Profile Clustering Using an Optical Quantum Computer Liu, Chengjun Xu, Yijun Gu, Wei Sun, Bo Wen, Kai Lu, Shuai Mili, Lamine Quantum Physics Systems and Control The traditional clustering problem of renewable energy profiles is typically formulated as a combinatorial optimization that suffers from the Curse of Dimensionality (CoD) on classical computers. To address this issue, this paper first proposed a kernel-based quantum clustering method. More specifically, the kernel-based similarity between profiles with minimal intra-group distance is encoded into the ground-state of the Hamiltonian in the form of an Ising model. Then, this NP-hard problem can be reformulated into a Quadratic Unconstrained Binary Optimization (QUBO), which a Coherent Ising Machine (CIM) can naturally solve with significant improvement over classical computers. The test results from a real optical quantum computer verify the validity of the proposed method. It also demonstrates its ability to address CoD in an NP-hard clustering problem. |
| title | Alleviating CoD in Renewable Energy Profile Clustering Using an Optical Quantum Computer |
| topic | Quantum Physics Systems and Control |
| url | https://arxiv.org/abs/2506.23569 |