## 1 Introduction  
The ability to interconnect quantum devices is essential for achieving efficient and reliable quantum computing architectures. A basic prerequisite is the utilization of quantum registers, which can be implemented as superconducting circuits containing Josephson junctions [1, 2], trapped ions [3, 4], quantum dots (QDs) [5, 6], coupled nanowires [7, 8], vibrational states of cold molecules [9, 10], or endohedral fullerenes [11]. The transfer of the encoded quantum information between registers is accomplished by additional components, which are generically termed quantum interconnects (QIs). Depending on the nature of transmitted information, these can be implemented as photonic, electronic or magnetic waveguides and can be used as active switching elements as well. From a technological perspective, a reconfigurable device with quantum interconnects could be engineered with the aid of floating gates. These are electrically isolated components, which trap the electrons and allow for the design of floating gate transistors that can store electrical charge and create non-volatile memory devices. A common approach is to embed the floating gate with metallic or semiconducting particles, such as quantum dots, which become charged through quantum tunneling. In this way, the electrostatic potential map of the active layer found below can be controlled. A lot of effort is dedicated to identifying the optimal materials for the engineering of a durable floating gate memory cell, with nanoparticles that exhibit good charge trapping characteristics and have the potential for large-scale fabrication [12]. Theoretically, these devices have been described using [PERSON]'s approach, by numerically solving the Schrodinger and Poisson equations [13]. They have gained a considerable amount of interest in the research community due to high scalability, small sizes fit for the design of nanodevices, and increased stability in data retention [12]. Along with the rapid development of organic semiconductor materials, engineering organic field effect transistors with floating gates has become feasible. To this end, researchers have proposed devices based on perovskite quantum dots that work as phototransistor memory devices, in which optical radiation can be turned into tunable electrical signals to improve storage capacity [14]. Also, organic field effect transistors could bring the advantage of erasing stored data by eliminating the collected charges by using a light-controlled bias, which is achieved by a process of photoinduced charge transfer across the surface of the floating gate [15]. One recognized advantage of quantum-dot-based spin qubits is represented by the possible integration with CMOS technology [16]. For these systems, the qubit manipulation requires an in-depth description of interacting few-electron systems within the electron waveguides, which can be obtained by employing a many-body approach. Of particular interest are the neuromorphic interconnects, enabling a hardware implementation of artificial neural networks, which reduces power consumption and speeds up the computations. The emerging field of quantum neuromorphic computing has gained more and more interest, as one can see from the recent review [17] and the references therein. Moreover, quantum neural networks, which benefit from superposition and entanglement, have certain advantages with respect to their classical counterparts, e.g., faster training and expressing more functions than comparable classical models, while other potential advantages are still to be explored [18]. Interacting electron systems are rigorously investigated by the exact diagonalization (ED) method [19; 20; 21], where the Coulomb interaction is fully taken into account. This is in contrast to other approaches for many-body problems, like Hartree-Fock and density functional theory, where a mean-field description is provided. Whenever the class of systems to be analyzed grows very large, exhaustive high-throughput ED calculations become prohibitive.