  Physical neuromorphic computing, exploiting the complex dynamics of physical
systems, has seen rapid advancements in implementation sophistication and
performance. Physical reservoir computing, a subset of neuromorphic computing
where only the output layer is trained, faces limitations due to its reliance
on single systems. This constrains output dimensionality and dynamic range,
limiting performance to a narrow range of tasks and posing a significant
challenge to the field's versatility.
  We address this issue by engineering a suite of nanomagnetic arrays as
physical reservoirs, demonstrating that tuning microstate space and geometry
can significantly broaden dynamics and enhance performance. We interconnect
these reservoirs in parallel and series, where each node is a distinct system
to create a multilayer neural network architecture. The output of one reservoir
is recorded, scaled and virtually fed as input to the next reservoir. All data
is experimentally measured with outputs combined offline. This networked
approach increases output dimensionality, internal dynamics and computational
performance. We demonstrate that a physical neuromorphic system is capable of
achieving an overparameterised state. This state facilitates meta-learning on
small training sets, yielding strong performance across a wide range of tasks.
Our approach's efficacy is further demonstrated through few-shot learning,
where the system rapidly adapts to new tasks.
