Let's say that I have a system of $n$ particles $p_1,\ldots,p_n\in\mathbb{R}^3$ (where $n$ here is on the order of 10,000). Furthermore, suppose we have a graph $G=(V,E)$ describing some network, where the set of vertices $V$ is the set of particles and the set of edges $E$ satisfies $|E|\ll n^2$. Each edge in $G$ will represent a spring between the corresponding pair of particles holding them together. I will also give each particle $p_i$ its own scalar potential $V_i$.
The wavefunction of this system will be of very large dimension: $\Psi(x_1,\ldots,x_n;t)$. We can write the Hamiltonian as $$H=\sum_{i=1}^n\left(-\frac{h^2}{2m} \nabla_i^2+V_i(x_i)\right)+\sum_{(p_i,p_j)\in E} c\|x_i-x_j\|^2$$
Obviously I cannot step the Schrodinger equation for this huge system forward in time using any standard discrete simulation method, nor can I even write down a discretized version of $\Psi$ due to its large dimensionality.
Are there techniques for computing approximate low-energy states of this system? What approximations are reasonable here? Can one find likely positions of each particle $p_i$?