Engineering superconducting quantum circuits

Dat Thanh Le

Gao-xiang Deng, Haoqiang Ai, Wei Shao +2 more

The three-level system represents the smallest quantum system capable of autonomous cycling in quantum heat engines. This study proposes a method to simulate the steady-state dynamics of a three-level quantum heat engine by designing and implementing superconducting quantum circuits. Following error mitigation, the outcomes from the quantum circuit model designed in this study, when executed on a real quantum device, closely align with theoretical predictions, thereby validating the effectiveness of the circuit model. This study offers a novel approach for investigating three-level quantum heat engines, enabling the verification of theoretical research findings while also reducing the complexity and cost of experimental procedures.

Michael Stern

Leszek Jaroszynski

Benjamin Lienhard, Jochen Braumüller, Wayne H. Woods +8 more

Over the past two decades, the performance of superconducting quantum circuits has tremendously improved. The progress of superconducting qubits enabled a new industry branch to emerge from global technology enterprises to quantum computing startups. Here, an overview of superconducting quantum circuit microwave control is presented. Furthermore, we discuss one of the persistent engineering challenges in the field—how to control the electromagnetic environment of increasingly complex superconducting circuits such that they are simultaneously protected and efficiently controllable.

Seong Hyeon Park, Bumsung Lee, Kyungsik Seo +1 more

To realize a practical quantum computer using superconducting qubits, engineering the superconducting quantum circuit with precise calculations of qubit energy levels and coupling strengths between components should be conducted. However, as superconducting quantum circuits scale up, identifying the discrepancy between designed and manufactured parameters becomes a major challenge. To estimate changes in key parameters due to the manufacturing errors in superconducting quantum circuits, we introduce a method for modeling manufacturing uncertainty as Gaussian random sampling and perform sensitivity analysis on selected key parameters of the circuit. Our proposed method combines circuit geometry with physics-based finite element analysis simulations to characterize the most sensitive input parameters on the key parameters. Overall, the results demonstrate that the proposed uncertainty and sensitivity analysis method can play an essential role in reducing discrepancies between the design and manufacturing processes in superconducting quantum circuits.