  Currently, quantum reservoir computing is one of the most promising and
experimentally accessible techniques for hybrid, quantum-classical machine
learning. However, its applications are limited due to practical restrictions
on the size of quantum systems and the influence of noise. Here we propose a
novel approach to connect two quantum reservoirs in a network to overcome these
issues and enhance their computing performance. To transfer information between
quantum reservoirs, we perform optimized two-qubit non-selective measurements.
We suggest a general heuristic optimization strategy based on tensor network
language and matrix representation of two-qubit quantum channels specified for
quantum reservoir computing. In addition, we introduce a single qubit
purification channel and its optimization for further enhancement of quantum
reservoir computing. We also demonstrate that the optimized channels applied to
a seven-qubit network can efficiently transfer information between its parts
with the resulting performance comparable to a network up to twenty-five qubits
connected via a classical information link.
