GPU-accelerated Effective Hamiltonian Calculator
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911323030290432 |
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| author | Chakraborty, Abhishek Patti, Taylor L. Khailany, Brucek Jordan, Andrew N. Anandkumar, Anima |
| author_facet | Chakraborty, Abhishek Patti, Taylor L. Khailany, Brucek Jordan, Andrew N. Anandkumar, Anima |
| contents | Effective Hamiltonian calculations for large quantum systems can be both analytically intractable and numerically expensive using standard techniques. In this manuscript, we present numerical techniques inspired by Nonperturbative Analytical Diagonalization (NPAD) and the Magnus expansion for the efficient calculation of effective Hamiltonians. While these tools are appropriate for a wide array of applications, we here demonstrate their utility for models that can be realized in circuit-QED settings. Our numerical techniques are available as an open-source Python package, ${\rm qCH_{eff}}$, which is available on GitHub (https://github.com/NVlabs/qCHeff) and PyPI (https://pypi.org/project/qcheff/). We use the CuPy library for GPU-acceleration and report up to 15x speedup on GPU over CPU for NPAD, and up to 42x speedup for the Magnus expansion (compared to QuTiP), for large system sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09982 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPU-accelerated Effective Hamiltonian Calculator Chakraborty, Abhishek Patti, Taylor L. Khailany, Brucek Jordan, Andrew N. Anandkumar, Anima Quantum Physics Effective Hamiltonian calculations for large quantum systems can be both analytically intractable and numerically expensive using standard techniques. In this manuscript, we present numerical techniques inspired by Nonperturbative Analytical Diagonalization (NPAD) and the Magnus expansion for the efficient calculation of effective Hamiltonians. While these tools are appropriate for a wide array of applications, we here demonstrate their utility for models that can be realized in circuit-QED settings. Our numerical techniques are available as an open-source Python package, ${\rm qCH_{eff}}$, which is available on GitHub (https://github.com/NVlabs/qCHeff) and PyPI (https://pypi.org/project/qcheff/). We use the CuPy library for GPU-acceleration and report up to 15x speedup on GPU over CPU for NPAD, and up to 42x speedup for the Magnus expansion (compared to QuTiP), for large system sizes. |
| title | GPU-accelerated Effective Hamiltonian Calculator |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2411.09982 |