A review of design principles and fabrication techniques in superconducting and trapped ion quantum devices
Year: 2026
Type: article
Abstract: Quantum computing is rapidly evolving from theoretical frameworks to functional hardware, with physical qubits now being realized across multiple experimental platforms. Precision nanofabrication has enabled the development of low-loss, high-coherence quantum devices. Among the various approaches, superconducting circuits and trapped-ion systems have emerged as the most advanced and experimentally successful platforms for realizing controllable and scalable qubits. This review presents an overview of nanoscale fabrication techniques employed in these two architectures. We first outline the operating principles that define qubit behavior, followed by a discussion of representative device designs and fabrication workflows. In superconducting circuits, we examine processes such as thin-film deposition, lithographic patterning, and Josephson-junction fabrication that determine key parameters including qubit frequency, anharmonicity, and coherence. For trapped-ion systems, we describe the transition from bulk electrode assemblies to microfabricated surface traps produced using photolithography, metal deposition, and dielectric layering, where electrode geometry and surface treatment directly influence ion confinement and motional heating. We further discuss fabrication-related challenges, including dielectric loss, surface contamination, and material defects that limit coherence and reproducibility, along with emerging mitigation strategies such as advanced cleaning, low-loss materials, and improved surface engineering. By drawing parallels between these two leading platforms, this review highlights that continued progress in nanofabrication remains central to realizing scalable, high-fidelity quantum processors.
Source: Nanotechnology
Authors: Aashay Pandharpatte, Manpreet Singh, Amit Saxena
Institution: Centre for Development of Advanced Computing
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
OpenAlex ID: W7164924445
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