Document Type
Article
Publication Date
12-15-2025
Abstract
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 opensource Python package, qCHeff, 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.
Recommended Citation
Abhishek Chakraborty, Taylor L. Patti, Brucek Khailany, Andrew N. Jordan, and Anima Anandkumar. "GPU-accelerated Effective Hamiltonian Calculator". Quantum 9, 1946 (2025). https://doi.org/10.22331/q-2025-12-15-1946
Peer Reviewed
1
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
This article was originally published in Quantum, volume 9, in 2025. https://doi.org/10.22331/q-2025-12-15-1946