  Today, the competition to build a quantum computer continues, and the number
of qubits in hardware is increasing rapidly. However, the quantum noise that
comes with this process reduces the performance of algorithmic applications, so
alternative ways in quantum computer architecture and implementation of
algorithms are discussed on the one hand. One of these alternative ways is the
hybridization of the circuit-based quantum computing model with the
dissipative-based computing model. Here, the goal is to apply the part of the
algorithm that provides the quantum advantage with the quantum circuit model,
and the remaining part with the dissipative model, which is less affected by
noise. This scheme is of importance to quantum machine learning algorithms that
involve highly repetitive processes and are thus susceptible to noise. In this
study, we examine dissipative information transfer to a qubit model called
Cat-Qubit. This model is especially important for the dissipative-based version
of the binary quantum classification, which is the basic processing unit of
quantum machine learning algorithms. On the other hand, Cat-Qubit architecture,
which has the potential to easily implement activation-like functions in
artificial neural networks due to its rich physics, also offers an alternative
hardware opportunity for quantum artificial neural networks. Numerical
calculations exhibit successful transfer of quantum information from reservoir
qubits by a repeated-interactions-based dissipative scheme.
