Massively parallel implementation and approaches to simulate quantum dynamics using Krylov subspace techniques
arXiv:1704.02770 · doi:10.1016/j.cpc.2018.08.010
Abstract
We have developed an application and implemented parallel algorithms in order to provide a computational framework suitable for massively parallel supercomputers to study the unitary dynamics of quantum systems. We use renowned parallel libraries such as PETSc/SLEPc combined with high-performance computing approaches in order to overcome the large memory requirements to be able to study systems whose Hilbert space dimension comprises over 9 billion independent quantum states. Moreover, we provide descriptions on the parallel approach used for the three most important stages of the simulation: handling the Hilbert subspace basis, constructing a matrix representation for a generic Hamiltonian operator and the time evolution of the system by means of the Krylov subspace methods. We employ our setup to study the evolution of quasidisordered and clean many-body systems, focussing on the return probability and related dynamical exponents: the large system sizes accessible provide novel insights into their thermalization properties.
16 pages, 6 figures, 3 tables
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- K-GRAPE: A Krylov Subspace approach for the efficient control of quantum many-body dynamics
- Quantum dynamics in one and two dimensions via recursion method
- Inverted many-body mobility edge in a central qudit problem
- Computing quantum magic of state vectors
- : A Program for Time Evolution With Improved Error Bound
- Asymmetric Transport in Long-Range Interacting Chiral Spin Chains
- Transforming the Lindblad Equation into a System of Linear Equations: Performance Optimization and Parallelization of an Algorithm
- Simulating quantum dynamics: Evolution of algorithms in the HPC context
- Fast simulation for multi-photon, atomic-ensemble quantum model of linear optical systems addressing the curse of dimensionality