Advanced superconducting devices for high-performance computing

Shamiul Alam, Ahmedullah Aziz

Superconducting electronics have garnered significant attention recently due to their exceptional speed and energy efficiency. They play a vital role in scaling quantum computers to thousands of qubits and offer unique advantages for high-performance computing and aerospace exploration. However, conventional Josephson junction-based superconducting circuits face scalability challenges due to flux trapping and struggle in driving high load impedances. To address these issues, three-terminal cryotron devices have emerged as a promising alternative. Here, we present a novel approach utilizing two heater cryotron devices to construct an XNOR gate, significantly enhancing the area efficiency of existing superconducting XNOR gates. XNOR logic plays a pivotal role in various applications including adder design, data center operations like copy verification and data encryption/decryption. Our designed XNOR logic gate boasts a substantial improvement in area efficiency compared to existing designs, requiring only two heater cryotron devices instead of the previous requirement of ten cryotron devices, leading to an 80% improvement in device count. This advancement holds promise for further optimizing superconducting circuitry for various applications.

T. M. Klapwijk, D. R. Heslinga, Wim M. van Huffelen

J.C. Gallop, Luning Hao, J.C. Macfarlane

Nirmal Tej Kumar

[ Julia+Flux-ML Library+Polly-LLVM+QRNG Services/Quantum Devices+qrng library ] in the Context of Understanding [ Machine Learning-ML/Internet of Things-IoT/HPC -High Performance Computing ] based Advanced Medical Imaging Software R&D – A Simple & Useful Suggestion.

Jose L Pacheco, Ajit Hira, Amanda Trujillo

David Sommers, Zach Degnan, Divita Gautam +4 more

Electromagnetic simulations form an indispensable part of the design and optimization process for superconducting quantum devices. Although several commercial platforms exist, open-source alternatives optimized for high-performance computing remain limited. To address this gap, we introduce SQDMetal, a Python-based API that integrates Qiskit Metal (IBM), Gmsh, Palace (AWS), and Paraview (Kitware) into an open-source, highly parallel simulation workflow for superconducting quantum circuits. SQDMetal enables accurate, efficient, and scalable simulations while remaining community-driven and free from commercial constraints. In this work, we validate SQDMetal through mesh convergence studies which benchmark SQDMetal against COMSOL Multiphysics and Ansys, demonstrating excellent agreement for both eigenmode and electrostatic (capacitance) simulations. Furthermore, we simulate superconducting resonators and transmon qubits, showing reasonable agreement with experimental measurements. SQDMetal also supports advanced capabilities, including Hamiltonian extraction via the energy participation ratio (EPR) method, incorporation of kinetic inductance effects, and full 3D modelling of device geometry for improved predictive accuracy. By unifying open-source tools into a single framework, SQDMetal lowers the barriers to entry for community members seeking to access high-performance simulations to assist in the design and optimization of their devices.