Predicting fermionic densities using a Projected Quantum Kernel method
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909759171461120 |
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| author | Perciavalle, Francesco Plastina, Francesco Pisarra, Michele Gullo, Nicola Lo |
| author_facet | Perciavalle, Francesco Plastina, Francesco Pisarra, Michele Gullo, Nicola Lo |
| contents | We use a support vector regressor based on a projected quantum kernel method to predict the density structure of 1D fermionic systems of interest in quantum chemistry and quantum matter. The kernel is built on with the observables of a quantum reservoir implementable with interacting Rydberg atoms. Training and test data of the fermionic system are generated using a Density Functional Theory approach. We test the performance of the method for several Hamiltonian parameters, finding a general common behavior of the error as a function of measurement time. At sufficiently large measurement times, we find that the method outperforms the classical linear kernel method and can be competitive with the radial basis function method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_14002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting fermionic densities using a Projected Quantum Kernel method Perciavalle, Francesco Plastina, Francesco Pisarra, Michele Gullo, Nicola Lo Quantum Physics Strongly Correlated Electrons Machine Learning We use a support vector regressor based on a projected quantum kernel method to predict the density structure of 1D fermionic systems of interest in quantum chemistry and quantum matter. The kernel is built on with the observables of a quantum reservoir implementable with interacting Rydberg atoms. Training and test data of the fermionic system are generated using a Density Functional Theory approach. We test the performance of the method for several Hamiltonian parameters, finding a general common behavior of the error as a function of measurement time. At sufficiently large measurement times, we find that the method outperforms the classical linear kernel method and can be competitive with the radial basis function method. |
| title | Predicting fermionic densities using a Projected Quantum Kernel method |
| topic | Quantum Physics Strongly Correlated Electrons Machine Learning |
| url | https://arxiv.org/abs/2504.14002 |