JCO: Optimization Framework for Nonlinear Superconducting Circuits Using a Lumped-Element Approach and Harmonic Balance
Emanuele Palumbo, Alessandro Alocco, Andrea Celotto, Luca Fasolo, Bernardo Galvano, Patrizia Livreri, and Emanuele Enrico

TL;DR
JCO is a Julia-based framework that models and optimizes nonlinear superconducting circuits using harmonic balance and Bayesian methods, demonstrated on a Josephson Traveling-Wave Parametric Amplifier.
Contribution
It introduces a simulation and optimization framework combining harmonic balance with Bayesian optimization for nonlinear superconducting circuits.
Findings
Efficiently models circuits with strong nonlinearity and high-dimensional parameters.
Successfully optimizes a Josephson Traveling-Wave Parametric Amplifier.
Supports systematic development of superconducting circuits.
Abstract
In this contribution we present JosephsonCircuitsOptimizer.jl (JCO), a simulation and optimization framework based on the JosephsonCircuits.jl library for Julia. It models superconducting circuits that include Josephson junctions (JJs) and other nonlinear elements within a lumped-element approach, leveraging harmonic balance, a frequency-domain technique that provides a computationally efficient alternative to traditional time-domain simulations. JCO automates the evaluation of optimal circuit parameters by implementing Bayesian optimization with Gaussian processes through a device-specific metric and identifying the optimal working point to achieve a defined performance function. This makes it well suited for circuits with strong nonlinearity and a high-dimensional set of coupled design parameters. To demonstrate its capabilities, we focus on optimizing a Josephson Traveling-Wave…
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