“…For example, the Hilbert space can be efficient compressed in the methods of the numerical renormalization group (NRG) [23][24][25][26][27][28], the sparse polynomial space representation (SPSR) [29], the time-dependent density matrix renormalization group (t-DMRG) [30] , etc. Additionally, the bosonic Hilbert space can be alternatively sampled by stochastic trajectories in the methods of the quantum Monte Carlo (QMC) [31,32], the path-integral Monte Carlo (PIMC) [33][34][35], the stochastic Liouville-von Neumann equation (SLN) [36], the stochastic path integral (SPI) [37,38], etc. Due to the limitation of space, a huge number of other methods are not able to be discussed here [8].…”