Related papers: GPU-accelerated Effective Hamiltonian Calculator

GPU-accelerated Effective Hamiltonian Calculator

URL: http://arxiv.org/abs/2411.09982v1
Date: Fri, 15 Nov 2024 06:33:40 GMT
Title: GPU-accelerated Effective Hamiltonian Calculator
Authors: Abhishek Chakraborty, Taylor L. Patti, Brucek Khailany, Andrew N. Jordan, Anima Anandkumar,
Abstract summary: We present numerical techniques inspired by Nonperturbative Analytical Diagonalization (NPAD) and the Magnus expansion for the efficient calculation of effective Hamiltonians. Our numerical techniques are available as an open-source Python package, $rm qCH_eff$.
Score: 70.12254823574538
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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 open-source Python package, ${\rm qCH_{eff}}$ (https://github.com/NVlabs/qCHeff), which uses the CuPy library for GPU-acceleration. We 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.

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