Title: Superconducting circuit design augmented with Bayesian optimization

Abstract: Superconducting qubits are electrical circuits comprising inductors, capacitors, and Josephson junctions. Although circuit parameters may in principle assume a wide range of values, existing architectures generally span only a small subset of the available design space, and design variations are often based largely on experience and engineering intuition. However, future circuit architectures – such as radically new qubit designs or many-body couplers for adiabatic quantum computing – will likely extend beyond an intuitive regime. Towards this end, we have developed a numerical search engine that screens the parameter design space to achieve circuits with desired spectral properties. The engine performs Bayesian optimization on a graphical model of arbitrary circuits with several nodes. In the first part of the talk, I will give a brief introduction to superconducting circuits. I will then move on to describe our algorithm and present first search results for a many-body coupler. At the end, I would like to pose some challenges of the optimization problem in our approach and ask for feedback from the group.