Academic Journal
Simulation of Quantum Many-Body Dynamics with Tensor Processing Units: Floquet Prethermalization
| العنوان: | Simulation of Quantum Many-Body Dynamics with Tensor Processing Units: Floquet Prethermalization |
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| المؤلفون: | Alan Morningstar, Markus Hauru, Jackson Beall, Martin Ganahl, Adam G.M. Lewis, Vedika Khemani, Guifre Vidal |
| المصدر: | PRX Quantum, Vol 3, Iss 2, p 020331 (2022) |
| بيانات النشر: | American Physical Society, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Physics LCC:Computer software |
| مصطلحات موضوعية: | Physics, QC1-999, Computer software, QA76.75-76.765 |
| الوصف: | Tensor processing units (TPUs) are specialized hardware accelerators developed by Google to support large-scale machine-learning tasks but they can also be leveraged to accelerate and scale other linear-algebra-intensive computations. In this paper, we demonstrate the usage of TPUs for massively parallel classical simulations of quantum many-body dynamics on long time scales. We apply our methods to study the phenomenon of Floquet prethermalization, i.e., exponentially slow heating in quantum spin chains subject to high-frequency periodic driving. We simulate the dynamics of L=34 qubits for over 10^{5} Floquet periods, corresponding to circuits with 4×10^{6} nearest-neighbor two-qubit gates. The circuits simulated have no additional symmetries and represent a pure-state evolution in the full 2^{L}-dimensional Hilbert space. This is achieved by distributing the computation over 128 TPU cores. On that size TPU cluster, we find speed-ups in wall-clock run time of 230 times and 15 times when compared to reference CPU and single-graphics-processing-unit (GPU) simulations, respectively, for shorter-time 30-qubit simulations that can be handled by all three platforms. We study the computational cost of the simulations, as a function of both the number of qubits and the number of TPU cores used, up to our maximum capacity of L=40 qubits, which requires a “full pod” of 2048 TPU cores with tens of terabytes of memory in total. For these simulations, an eight-TPU-core machine is comparable to a single A100 GPU and thus the full TPU pod is comparable to a machine with hundreds of top-of-the-line GPUs. However, the TPU pod is more energy and cost efficient and readily accessible (via Google Cloud), unlike such large many-GPU configurations. We also study the accumulation of numerical error as a function of circuit depth in very deep circuits. Our work demonstrates that TPUs can offer significant advantages for state-of-the-art simulations of quantum many-body dynamics. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2691-3399 |
| Relation: | https://doaj.org/toc/2691-3399 |
| DOI: | 10.1103/PRXQuantum.3.020331 |
| URL الوصول: | https://doaj.org/article/841bc66db73c40ae978a37e1d4e0f8dd |
| رقم الانضمام: | edsdoj.841bc66db73c40ae978a37e1d4e0f8dd |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 26913399 |
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| DOI: | 10.1103/PRXQuantum.3.020331 |