## 4 Conclusions  
Neuromorphic device structures were investigated using a many-body description coupled with a cGAN image-to-image translation approach. We propose a reconfigurable device, which is both suitable to optimize the design process of neuromorphic systems and, also, to function as reprogrammable multi-terminal systems for charge and, potentially, spin transfer between quantum dot registers. The proposed device architecture can provide real-time solutions to intricate many-body problems, and it can be constructed using existing technologies. The confinement potential in the active layer is adjusted using a charged floating gate array, while the charging process and the read-out of the electron density can be performed using STM-like tips. The design accuracy may be influenced by the finite resolution of the gate array. The many-electron ground states are calculated in the framework of exact diagonalization, which rigorously accounts for the Coulomb interaction beyond the mean-field approaches, though limited to few electrons. The charge localization depends on the number of electrons present in the quantum interconnect, as well as on the external electric fields. Assessing the charge distribution and the switching properties becomes a difficult task, as the optimization process requires a large number of instances to be solved. This is one key point where ML techniques can bring a significant advantage. Our approach, based on cGAN mapping of the potential to the charge density, can provide an accurate description for a large set of new systems as shown by several metrics employed: \(R^{2}\), SSIM, \(L_{\infty}\). We discuss the prediction accuracy as the number of electrons is increased and during the switching process. In addition, we explore the possibility to deconvolute tunneling current maps using the same general-purpose image-to-image translation tool. The best results are obtained for the mapping performed with symmetric and asymmetric Gaussian convolution kernels, without other perturbations. However, in a more realistic picture, one has to account for imperfections of the STM tip. We considered a background noise in the input images and, applying the deconvolution method, reasonable predictions can still be achieved. The cGAN approach proves to be a promising tool for deconvoluting maps whenever the kernel is unknown.  
In conclusion, we proposed a reconfigurable device system and a cGAN-based methodology to efficiently design neuromorphic systems, which can transfer charge and spin between quantum dot registers but can also perform the functions of active switching elements. Also, the current approach can target the prediction of energy functionals for larger systems, as well as the inverse design of quantum multi-terminal devices.  
Conceptualization, A.T.P., C.-A.P.-S. and G.A.N.; methodology, A.T.P. and C.-A.P.-S.; formal analysis, M.M., D.-V.A., L.I. and A.M.; investigation, A.T.P., C.-A.P.-S. and G.A.N.; writing--original draft preparation, A.T.P., C.-A.P.-S. and G.A.N.; writing--review and editing, A.A., L.I. and A.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Romanian Ministry of Research, Innovation and Digitalization, CNCS-UEFISCDI, project number PN-III-P4-ID-PCE-2020-1142, within PNCDI III and PN 23210204.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CDM & charge density map \\ cGAN & convolutional generative adversarial neural network \\ \end{tabular}