  In the field of dissipative systems, the non-Hermitian skin effect has
generated significant interest due to its unexpected implications. A system is
said to exhibit a skin effect if its properties are largely affected by the
boundary conditions. Despite the burgeoning interest, the potential impact of
this phenomenon on emerging quantum technologies remains unexplored. In this
work, we address this gap by demonstrating that quantum neural networks can
exhibit this behavior and that skin effects, beyond their fundamental interest,
can also be exploited in computational tasks. Specifically, we show that the
performance of a given complex network used as a quantum reservoir computer is
dictated solely by the boundary conditions of a dissipative line within its
architecture. The closure of one (edge) link is found to drastically change the
performance in time series processing proving the possibility to exploit skin
effects for machine learning.
