As alluded to previously, it may appear that the memory requirements for storing the weights for Q w (s, a) are prohibitive. To show that memory need not be a limiting factor, Fig. shows an example of the distribution of magnitudes for the learned weights in an H 12 ring with a subspace dimension of k = 10, 000, same as that used in Tab. 1, which recovered around 96% of the FCI correlation energy. In this case, nearly 87% of the determinants have zero weight and therefore need not be explicitly stored. Furthermore, of the weights with non-zero magnitude, nearly half fall below 10 -5 , suggesting that cutoffs may be employed to further reduce demands on the weight storage. Thus, sparse storage coupled with pruning techniques may be employed to efficiently manipulate Q w (s, a). Moreover, the weights need not be stored in memory: as the number of elements that are updated at any given time according to Eq. ( ) go as subspace dimension k, the full weights may instead utilize scratch disk space. An analogous argument extends to the handling of auxiliary weights v.