Vector Ising Spin Annealer for Minimizing Ising Hamiltonians

We introduce the Vector Ising Spin Annealer (VISA), a framework in gain-based computing that
harnesses light-matter interactions to solve complex optimization problems encoded in spin Hamil-
tonians. Traditional driven-dissipative systems often select excited states due to limitations in spin
movement. VISA transcends these constraints by enabling spins to operate in a three-dimensional
space, offering a robust solution to minimize Ising Hamiltonians effectively. Our comparative analy-
sis reveals VISA’s superior performance over conventional single-dimension spin optimizers, demon-
strating its ability to bridge substantial energy barriers in complex landscapes. Through detailed
studies on cyclic and random graphs, we show VISA’s proficiency in dynamically evolving the en-
ergy landscape with time-dependent gain and penalty annealing, illustrating its potential to redefine
optimization in physical systems.