  Ising machines are novel computing devices for the energy minimization of
Ising models. These combinatorial optimization problems are of paramount
importance for science and technology, but remain difficult to tackle on large
scale by conventional electronics. Recently, various photonics-based Ising
machines demonstrated ultra-fast computing of Ising ground state by data
processing through multiple temporal or spatial optical channels. Experimental
noise acts as a detrimental effect in many of these devices. On the contrary,
we here demonstrate that an optimal noise level enhances the performance of
spatial-photonic Ising machines on frustrated spin problems. By controlling the
error rate at the detection, we introduce a noisy-feedback mechanism in an
Ising machine based on spatial light modulation. We investigate the device
performance on systems with hundreds of individually-addressable spins with
all-to-all couplings and we found an increased success probability at a
specific noise level. The optimal noise amplitude depends on graph properties
and size, thus indicating an additional tunable parameter helpful in exploring
complex energy landscapes and in avoiding trapping into local minima. The
result points out noise as a resource for optical computing. This concept,
which also holds in different nanophotonic neural networks, may be crucial in
developing novel hardware with optics-enabled parallel architecture for
large-scale optimizations.
