Analysis of arbitrary superconducting quantum circuits accompanied by a Python package: SQcircuit

TL;DR

This paper introduces SQcircuit, an open-source Python package that provides a systematic framework for analyzing complex superconducting quantum circuits, including Hamiltonian construction, diagonalization, and property calculation, to accelerate quantum hardware development.

Contribution

The paper presents a novel, comprehensive computational framework and an accessible Python tool for analyzing and designing superconducting quantum circuits, addressing complexity and efficiency challenges.

Findings

Efficient basis selection for Hamiltonian diagonalization

Calculation of key circuit properties like spectrum and coherence times

Demonstrations on various quantum circuits using SQcircuit

Abstract

Superconducting quantum circuits are a promising hardware platform for realizing a fault-tolerant quantum computer. Accelerating progress in this field of research demands general approaches and computational tools to analyze and design more complex superconducting circuits. We develop a framework to systematically construct a superconducting quantum circuit's quantized Hamiltonian from its physical description. As is often the case with quantum descriptions of multicoordinate systems, the complexity rises rapidly with the number of variables. Therefore, we introduce a set of coordinate transformations with which we can find bases to diagonalize the Hamiltonian efficiently. Furthermore, we broaden our framework's scope to calculate the circuit's key properties required for optimizing and discovering novel qubits. We implement the methods described in this work in an open-source Python package SQcircuit. In this manuscript, we introduce the reader to the SQcircuit environment and functionality. We show through a series of examples how to analyze a number of interesting quantum circuits and obtain features such as the spectrum, coherence times, transition matrix elements, coupling operators, and the phase coordinate representation of eigenfunctions.