Session A47: Superconducting Qubit Software, Design Tools & Theory

Simulating superconducting circuits with scqubits... and the impact of open-source software on quantum-science related research

In recent years, open source software development has played a crucial role in fueling technological advances across a broad range of disciplines, including ones related to quantum information science (QIS). One important area of QIS research involves building software tools that allow for accurate modeling and simulations of early quantum computing devices. In this talk, besides giving a brief overview of the current landscape of such tools, I will discuss scqubits[1]: an open-source Python package for simulating and analyzing superconducting circuits - arguably one of the leading approaches to building early quantum computers. I will outline its core functionality, features, as well as limitations. I will also briefly present recently added facilities for arbitrary circuits analysis (along with coherence time estimates), and talk about our ongoing efforts related to performance enhancements as well as building tooling required to take advantage of GPU-based computing. Finally I will provide an outline of a couple real-world projects where the scqubits package was particularly useful. 

[1] https://github.com/scqubits/scqubits   

Effective master equation modeling of correlated noise

Developing tools that allow for accurate descriptions of driven quantum dynamics in the presence of non-Markovian noise is essential for understanding and possibly mitigating decoherence in realistic devices.  This problem is challenging even in the case where the noise is classical and Gaussian.  We recently introduced an approach based on a generalized cumulant expansion, which yields a time-local pseudo-Lindblad master equation (PLME)[1].  These equations have a form reminiscent of standard Lindblad master equations, except decoherence rates are time-dependent and possibly negative.  Here, we discuss how the PLME approach can be extended to describe more complicated setups involving many interacting qubits, subject to noise that is both correlated in space and time.  We show how the interplay of these elements leads to a number of phenomena that are missed if one makes standard Markovian approximations.  We discuss applications to understanding the impact of 1/f dephasing noise on the performance of superconducting quantum circuit architectures, as well as extensions to include quantum noise effects.      

Utilizing Discrete Variable Representations to Capture Phase Slips and Persistent-Current Vortices in Superconducting Circuits

The simulation of quantum superconducting circuits has become increasingly important for identifying system characteristics and modeling their relevant dynamics. Various numerical tools and packages have been developed with this purpose in mind, typically utilizing the harmonic oscillator basis and/or the charge basis to represent a Hamiltonian. In this work, we consider the use of discrete variable representations (DVRs) to construct a basis. In particular, we use so-called 'sinc DVRs' of both charge number and phase to approximate the eigenenergies of several prototypical examples, exploring their use and effectiveness in the numerical analysis of superconducting circuits. Our work suggests improvements in efficiency over standard approaches while offering a potential path to incorporating phase slips and persistent-current vortices in the simulation of superconducting circuits.   

Toward multiscale simulations for technology computer-aided design of superconducting qubits

Technology computer-aided design (TCAD) software is an important device engineering tool enabling to simulate and optimize component behavior before lengthy and costly manufacturing steps. While commercial TCAD software has been used for semiconductor chip design for decades, usage of TCAD in the superconducting qubit community is more recent, and largely based on conventional microwave engineering tools.

Going beyond established microwave engineering paradigms in predictive superconducting qubit TCAD is challenging due to the necessity to calculate parameters that are determined at vastly different length scales. While capacitances and inductances may be efficiently determined from standard finite-element simulation tools, properties of Josephson junctions are often determined by physics at the atomic scale.

We will present transmon simulations using an adaptive meshing algorithm enabling accurate capacitance extraction despite challenges posed by singularities such as corners and embedded surfaces, which are typical of superconducting-qubit structures. We will then present ab initio simulations of Josephson junctions using the Non-equlibrium Green’s Function Density Functional Theory (NEGF-DFT) technique. Together, these results pave the way to a fully integrated multiscale simulation software suite for superconducting qubit design.   

QFit: simplifying calibration and parameter fitting for superconducting circuits

We present QFit, a Python-based application for extracting parameters of superconducting circuits from measured spectroscopy data. Through its streamlined graphical user interface, users interactively calibrate measurement data, extract data points, and perform parameter fitting. QFit supports modeling of a wide range of circuit quantum electrodynamic systems, leveraging the Python library scqubits as its backend simulator. The application also encompasses a suite of features to enhance the precision of parameter fitting, including: assisted flux crosstalk calibration, automatic identification of resonance peaks, and interactive fitting-by-eye prior to numerical fitting. The extracted parameters can be seamlessly passed on to scqubits, facilitating subsequent numerical simulations. QFit provides a convenient and efficient pipeline from experimental measurements to theoretical simulations, accelerating the process of developing novel superconducting circuit systems.   

Coupled scattering parameter calculation for superconducting quantum circuit simulation

Microwave simulation of superconducting circuits is often done with conventional 3D full-wave analysis such as the finite element method (FEM). However, accurately representing large circuit structures using FEM is computationally expensive which can pose limits on the design complexity and number of qubits in simulations. In classical microwave simulation, coupled scattering parameter calculation (CSC) addresses this problem by decomposing the circuit structure into smaller individual blocks. In this talk, we bridge the gap between CSC methods and traditional quantum analysis to provide a modular and computationally efficient method for superconducting circuits simulation. Provided some assumptions on the circuit design, we successfully predict mode frequencies, loss rates and Hamiltonian parameters to the same degree of accuracy as previous methods with a reduced computational cost. This acceleration enables fast design parameter optimization using desktop-level computer hardware.   

An Integrated Workflow for the Seamless Design of Superconducting Qubits – QuantumPro

Qubits and quantum amplifiers are the cornerstones of quantum computing systems. As the demand for scaling to thousands of qubits grows, it becomes imperative to incorporate systematic engineering and design automation. In this talk, we introduce an elegant solution that consolidates five essential functionalities into a single platform for the very first time. These functionalities encompass schematic design, layout creation, electromagnetic (EM) analysis, nonlinear circuit simulation, and the extraction of quantum parameters. We highlight the specialized quantum artwork library, and the extraction of quantum parameters using both the black-box quantization and energy participation methods. Additionally, we demonstrate how the electromagnetic and circuit flows can be connected through co-simulation to analyze the power-dependent behavior of quantum chips. Lastly, we explore the potential for modeling the kinetic inductance of superconductors within the electromagnetic workflow, as well as the prospect of employing Python to automate the entire workflow.   

Going from Hamiltonian to GDS File: An Open Source Package for Generating Qubit Designs

We introduce SQuADDS, a Superconducting Qubit And Device Design Simulation database, an open-source design database and simulation package engineered for the rapid design of superconducting qubit architectures. Utilizing physics-driven algorithms and a large database of validated designs, the platform uses Qiskit Metal to programmatically generate device designs in alignment with user-specified Hamiltonian objectives. We discuss the database setup and demonstrate the workflow that allows a user to specify device characteristics and generate a full device design.   

Gradient-Based Optimization of Superconducting Quantum Circuit Designs -- part 1

Superconducting circuits are among the most versatile emerging platforms for quantum information processing. However, designing and discovering these circuits can be challenging due to the vast parameter space and the numerous criteria that need to be satisfied for these circuits to operate optimally. Therefore, we believe that using optimization methods can automate and facilitate the design and discovery process.In this presentation, we highlight the new auto-differentiation features of our software, SQcircuit. This open-source package is designed for simulating superconducting quantum circuits. SQcircuit is not only capable of calculating circuit properties, but it can also compute the gradient of these properties with respect to circuit parameters. This is achieved using the back-propagation mechanism, which is an essential building block for gradient-based optimization.Automating the calculation of the gradient enables the optimization of arbitrary metrics derived from the eigenspectrum and circuit parameters. These metrics include (but are not limited to) operation frequency, anharmonicity, flux and charge sensitivity, and decoherence times. We demonstrate the continuous improvement of these metrics, framed as a scalar loss function, across a variety of circuit topologies targeting an experimentally realistic parameter range. Furthermore, the circuit discovery problem can also be formulated as an optimization problem. We illustrate how to design and discover qubits using SQcircuit's auto-differentiation capabilities.

Part 1: In this part of a multi-part talk, we discuss background and the implementation of optimization routines.   

Gradient-Based Optimization of Superconducting Quantum Circuit Designs -- Part 2

Superconducting circuits are among the most versatile emerging platforms for quantum information processing. However, designing and discovering these circuits can be challenging due to the vast parameter space and the numerous criteria that need to be satisfied for these circuits to operate optimally. Therefore, we believe that using optimization methods can automate and facilitate the design and discovery process.In this presentation, we highlight the new auto-differentiation features of our software, SQcircuit. This open-source package is designed for simulating superconducting quantum circuits. SQcircuit is not only capable of calculating circuit properties, but it can also compute the gradient of these properties with respect to circuit parameters. This is achieved using the back-propagation mechanism, which is an essential building block for gradient-based optimization.Automating the calculation of the gradient enables the optimization of arbitrary metrics derived from the eigenspectrum and circuit parameters. These metrics include (but are not limited to) operation frequency, anharmonicity, flux and charge sensitivity, and decoherence times. We demonstrate the continuous improvement of these metrics, framed as a scalar loss function, across a variety of circuit topologies targeting an experimentally realistic parameter range. Furthermore, the circuit discovery problem can also be formulated as an optimization problem. We illustrate how to design and discover qubits using SQcircuit's auto-differentiation capabilities.

Part 2: In this part of a multi-part talk, we showcase trends and results from performing optimization over a selection of circuit topologies.   

Analysis of Josephson junction arrays beyond the single-mode approximation

SNAIL and fluxonium devices belong to a group of superconducting circuits that consist of an array of identical Josephson junctions forming a loop with an additional junction that is distinct from the others. An accurate description of such circuits is fundamental for their use in quantum processors as they continue to be scaled up. These circuits are often studied using an approximate one-mode model which disregards higher-energy array modes. As a result, the physics arising from such array modes has remained largely unexplored. We discuss diagnostics to test the range of validity of the one-mode model and we analyze the impact of the array modes on the low-energy physics of the system. We further examine the parameter regime in which the odd junction has the larger Josephson energy, a scenario that goes beyond the conventional one-mode model. We discuss the symmetry, spectral and decoherence properties of the circuit in this parameter regime with a view to potential applications.   

Fast, optimal circuit design of multiplexed readout resonators with individual Purcell filters.

Multiplexed readout circuits with individual Purcell filters have emerged as a promising architecture satisfying the speed, fidelity, and scalability necessary for large-scale quantum error correction. At relevant system sizes, however, simultaneously designing for all target parameters quickly becomes intractable using typical Finite-Element-Method-based approaches. Instead, we present a circuit-based simulation approach together with a computationally efficient, closed-loop optimization of the entire readout circuit. We further study the hybridization dynamics of the readout and filter resonator and observe parameter regimes where the Purcell protection from the filter breaks down. Finally, we discuss the experimental realization of circuits in different parameter regimes as well as the readout performance of these systems.   

Charge-parity switching effects and optimisation of transmon-qubit design parameters

Enhancing the performance of noisy quantum processors requires improving our understanding of error mechanisms and the ways to overcome them. A judicious selection of qubit design parameters, guided by an accurate error model, plays a pivotal role in improving the performance of quantum processors. In this study, we identify optimal ranges for qubit design parameters, grounded in comprehensive noise modeling. To this end, we commence by analyzing a previously unexplored error mechanism that can perturb diabatic two-qubit gates due to charge-parity switches caused by quasiparticles. We show that such charge-parity switching can be the dominant quasiparticle-related error source in a controlled-Z gate between two qubits. Moreover, we also demonstrate that quasiparticle dynamics, resulting in uncontrolled charge-parity switches, induce a residual longitudinal interaction between qubits in a tunable-coupler circuit. Our analysis of optimal design parameters is based on a performance metric for quantum circuit execution that takes into account the fidelity and frequencies of the appearance of both single and two-qubit gates in the circuit. This performance metric together with a detailed noise model enables us to find an optimal range for the qubit design parameters. Substantiating our findings through exact numerical simulations, we establish that fabricating quantum chips within this optimal parameter range not only augments the performance metric but also ensures its continued improvement with the enhancement of individual qubit coherence properties. Conversely, straying from the optimal parameter range can lead to the saturation of the performance metric. Our systematic analysis offers insights and serves as a guiding framework for the development of the next generation of transmon-based quantum processors.