Solving the Schrodinger equation using program synthesis

We demonstrate that a program synthesis approach based on a linear code representation can be used to generate algorithms that approximate the ground-state solutions of one-dimensional time-independent Schrodinger equations constructed with bound polynomial potential energy surfaces (PESs). Here, an algorithm is constructed as a linear series of instructions operating on a set of input vectors, matrices, and constants that define the problem characteristics, such as the PES. Discrete optimization is performed using simulated annealing in order to identify sequences of code-lines, operating on the program inputs that can reproduce the expected ground-state wavefunctions psi(x) for a set of target PESs. The outcome of this optimization is not simply a mathematical function approximating psi(x) but is, instead, a complete algorithm that converts the input vectors describing the system into a ground-state solution of the Schrodinger equation. These initial results point the way toward an alternative route for developing novel algorithms for quantum chemistry applications.

Automated summary

The study demonstrates that a program synthesis approach based on a linear code representation can be used to generate algorithms that approximate the ground-state solutions of one-dimensional time-independent Schrodinger equations. Discrete optimization with simulated annealing is used to identify code sequences that can reproduce the expected ground-state wavefunctions for target PESs. This alternative method shows promise for developing novel algorithms for quantum chemistry applications.