Design of Nanoscale Quantum Interconnects Aided by Conditional Generative Adversarial Networks

• Published in: Applied sciences Vol. 14; no. 3; p. 1111
• Main Authors: Preda, Amanda Teodora, Pantis-Simut, Calin-Andrei, Marciu, Mihai, Anghel, Dragos-Victor, Allosh, Alaa, Ion, Lucian, Manolescu, Andrei, Nemnes, George Alexandru
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Artificial intelligence; conditional generative adversarial network; Electrons; Localization; Machine learning; Neural networks; neuromorphic devices; Physics; Point defects; quantum computing; Quantum dots; quantum interconnects; Research methodology; Specific gravity; Transistors
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14031111

Interconnecting nanodevices with the aim of assembling quantum computing architectures is one of the current outstanding challenges. At the nanoscale, the quantum interconnects become comparable in complexity with the active devices and should be treated on equal footing. In addition, they can play an active role in the switching properties. Here, we investigate the charge localization in neuromorphic bi-dimensional systems, which serve as quantum interconnects (QIs) between quantum dot registers. We define a device structure where, by manipulating the charging of a floating gate array, one defines the QI potential map, which can host a few interacting electrons. The ground state charge density may be extracted by measuring the tunneling current perpendicular to the device surface, yielding a convoluted image of the electron distribution. Using image-to-image translation methods, we achieve the mapping of the charge density from the confinement potential, as well as by deconvoluting the tunneling current map, which can be obtained by a direct measurement. Thus, we provide a proof-of-concept for a reconfigurable device, which can be used to design quantum many-electron devices.