Dear Colleagues,

The progress of digital semiconductor technology has played a decisive role in changing our everyday life in recent decades. The majority of the devices, ranging from supercomputers to personal gadgets, share a similar paradigm of operation rooting in the so-called von Neumann architecture. This approach is well suited for universal computers utilized for multipurpose tasks, implying successive solution of equations and running algorithms. However, it is usually inefficient in tackling specific problems requiring massive parallelism such as video or sound processing, mimicking intelligence, complex quantum system analysis, or economic forecasting.

The new computational paradigms are desired for broadening our capability in these fields. The major players here are neuromorphic and quantum technologies, exciting trends of the 21st century.

The idea of artificial intelligence appeared a long time ago. The development of artificial neural networks was an important milestone on this direction. While being a principle of information processing, they differ from usual algorithms by perceiving the information “holistically” and using the parallelism inherent in neuromorphic systems. It is expected that the connection of neuromorphic processors with peripheral sensors in a single center mimicking the midbrain would pave the way in achieving a semblance of consciousness in machine systems. This means the abilities of continuous learning, making decisions, responding to unforeseen circumstances, and making judgments when interacting with other machines and people. An awaited fascinating breakthrough in technological progress of modern humanity.

Another important direction in the computing revolutionization is the algorithmic quantum computing systems. Despite some skepticism about the ways and even the possibility of implementation, this area is developing at a rapid pace. In 2019, the Google team presented a superconducting quantum chip demonstrating the quantum supremacy, i.e., solving a specific problem which cannot be calculated by a state-of-the-art classical supercomputer in a reasonable time. This year, the operations with logical qubits in error correction codes are demonstrated, while the number of qubits in quantum processors exceeded a hundred. The plans to the nearest future include quantum systems containing thousands and millions of qubits and achieving fault-tolerant operation.

The success of the computing system implementation depends on the capabilities of the chosen technological platform. The superconductivity is a macroscopic quantum effect that makes it comparatively easy to establish quantum coherent states in solid-state circuits printed using standard technological approaches. The absence of resistance provides high energy efficiency of superconducting circuits operated in a classical regime. The high switching rate and high nonlinearity of the Josephson junction is well suited for implementation of neuronal function. At the same time, the possibility of Josephson junction fabrication in various layers of the integrated circuit is an excellent opportunity for creating deep neural networks in 3D architecture. These features cause the superconducting electronics to be one of the most promising bases for the development of both considered computing paradigms.

The current Special Issue of Nanomaterials is named “Nanostructures for Superconducting Electronics”. It is devoted to one of the most demanded directions of research in applied superconductivity, which is an exploration of nanostructures enabling improving the characteristics and expanding functionality of quantum and neuromorphic circuits at the very basic level of their operation. We welcome the high quality studies in this field and we expect this collection to be a fundamental basis for further applied researches.

Dr. Igor Soloviev
Guest Editor

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• superconducting electronics
• Josephson junction
• heterostructure
• nanowire
• superconducting quantum circuit
• qubit
• artificial neural network
• artificial neuron
• artificial synapse