Superconducting artificial neural networks and quantum circuits

The future of high-performance computing with reduced energy consumption is clearly addressed to technologies with lower energy dissipation. А logical solution and the most promising candidate for a radical reduction in energy consumption is the superconducting digital technology (SDT) based on Josephson junctions. The energy consumption of the SDT basic element is in the order of 10⁻¹⁹ Joule, corresponding to up to seven orders of magnitude less energy dissipation than that for their semiconductor analog.

The main goal of this thematic issue is to highlight this new area of research – artificial neural networks – based on SDT and on superconductor/ferromagnetic hybrid nanostructures, and their applications in quantum electronics and spintronics, to highlight the fascinating world of superconducting nanoelectronics. 

Research contributions to this thematic issue may include but are not limited to the following topics: 

• Artificial neural networks and qubits
• Functional superconductor/ferromagnetic nanostructures
• Proximity effect and Andreev reflection in superconductor/ferromagnetic hybrid structures
• Non-uniform superconductivity, including FFLO state and triplet pairing
• Spin-valve effect and memory cells
• Josephson effect in S/F hybrid structures

Submission deadline: July 31, 2025