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Origins of microwave losses in superconducting circuits made with silicon-on-insulator substrates

Simon Messelot, Nicolás Aparicio, Kazi Rafsanjani Amin, Eric Eyraud, Bruno Fain, Guillaume Jourdan, F. Nemouchi, Sébastien Hentz, F. Gustavo, F. Lefloch, Nicolas Roch, J. J. Viennot, Julien Renard

Published Jul 29, 2026• 0 citations
Publisher

Abstract

This repository contains data and a jupyter notebook that were used to analyze them and create the figures for the manuscript: "Origins of microwave losses in superconducting circuits made with silicon-on-insulator substrates". The Qcodes databases (.db) contain the raw data. csv files contain analyzed and fitted data used for generating the figures. Python scripts (.py) used for the analysis are included. txt files contains the results of simulations that are used for Figure 4.The notebook (.ipynb) calls the different files and generate the figures.

DOI
10.5281/zenodo.21676678

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This repository contains data and a jupyter notebook that were used to analyze them and create the figures for the manuscript: "Origins of microwave losses in superconducting circuits made with silicon-on-insulator substrates". The Qcodes databases (.db) contain the raw data. csv files contain analyzed and fitted data used for generating the figures. Python scripts (.py) used for the analysis are included. txt files contains the results of simulations that are used for Figure 4.The notebook (.ipynb) calls the different files and generate the figures.
Silicon-on-insulator technology is widely used to fabricate silicon based devices, from advanced transistors to photonic circuits or nanomechanical systems. Integrating low loss superconducting quantum circuits with silicon-on-insulator substrates enables to couple the advantages offered by the mature silicon technology to the exquisite sensitivity of superconducting circuits. The natural approach, inherited from research in superconducting microwave devices, is to use a substrate made with highly resistive silicon, known for its low level of microwave losses. In this work, using superconducting microwave resonators, we show that counterintuitively, standard resistivity silicon-on-insulator substrates perform better than high resistivity silicon-on-insulator substrates at cryogenic temperatures. In the latter case, the presence of a parasitic sheet conduction at the interface between bulk silicon and silicon oxide acts as the dominant loss mechanism. This parasitic sheet can be suppressed using substrates with intentionally induced traps. In such substrates, losses are ultimately limited by the dielectric losses of the silicon oxide layer. These substrates offer interesting perspectives for the development of superconducting nanoelectromechanical systems. First, the release, i.e. the removal of the silicon oxide, could be limited to the moving parts, thereby maintaining the mechanical integrity of the rest of the device. Additionally, such structure would enhance heat evacuation into the bulk of the substrate which is an issue in current devices such as microwave-to-optics converters.
This repository contains the numerical data and computational codes supporting the results presented in the manuscript: "Engineered non-Gaussian Coherence as a Thermodynamic Resource for Quantum Batteries". The archive (data_QB.zip) includes all datasets, Jupyter notebooks, and figure files required to reproduce the results reported in both the main text and the Supplemental Material. Each figure is organised in a separate directory containing the corresponding data (.txt), code (.ipynb), and generated plots (.png/.pdf). The provided materials are sufficient to reproduce all figures and results without restriction. Detailed instructions and directory structure are described in the accompanying README.txt file. This dataset is made openly available to ensure transparency and reproducibility of the reported results.
Overview of data provided in support of Towards near-term quantum simulation of materials Contents of this folder: `analyse_materials_results.py`: script used to generate the summary tables and figures presented in the manuscript. `towards_quantum_simulation_data`: raw data used and produced when studying various 3D materials. `towards_quantum_simulation_analysis`: tables (in `.tex` format) and figures (as PDFs) presented in the manuscript. The contents of `towards_quantum_simulation_analysis` were generated by `analyse_materials_results.py`, i.e. the user need not run the script to produce the output files. Users can generate these analyses directly by running ```python analyse_materials_results.py```, though note the following packages will first need to be installed: `pandas >= 1.2.3` `numpy >= 1.23` `seaborn >= 0.11.1` `lfig >= 0.1.3` `matplotlib > 3.7.0` Included data Within `towards_quantum_simulation_data`, there are subfolders for each of the materials described in the manuscript, i.e. `SrVO3`, `GaAs`, `H3S`, `Si`, `Li2CuO2`. Within each material's folder are further subfolders for the `hamiltonian` and `encoding` used to represent the material, as well as subfolders for each of the `algorithms` studied. `hamiltonian`: files which specify the Hamiltonian for the material under study. There are a number of files `interactions.json` Hamiltonian terms in terms of Majorana monomials. `map_majorana_to_mode.json` keys are Majorana indices; values are the mode index to which they are associated. `map_mode_to_group.json` keys are mode indices; values are the group (or site) index to which they are associated. `map_group_to_position.json` keys are group indices; values are the corresponding 3D Cartesian coordinates of the lattice used to represent the material. `stage_data.json` contains key/value pairs of any other fields of interest. `encoding` files which specify the fermionic encoding which is customised for the material under study. `encoding.json` which details the edges of the hybrid compact encoding described in Section VI of the supplementary material. `precompiler.json` contains all the information which permits the encoding construction, including the Hamiltonian terms (`_interactions`) which match those in `hamiltonian/interactions.json`. The same mappings as present in the Hamiltonian data(a.g. `map_group_to_position`). `stage_data.json` contains key/value pairs of any other fields of interest. `algorithms` contains subfolders for each of the algorithms desribed in the manuscript Those explored for circuits depths: `TDSSplitTermsPriorityCircuitDepth` (TDS in the manuscript) `TDSSplitTermsPriorityCircuitDepthNoSwapNetwork` (TDS\*) `VQESplitTerms` (VQE) `VQESplitTermsNoSwapNetwork` (VQE*) each of which contain the files, inside the `circuitry` folder: `circuit_terms.csv`, which lists each individual term, together with their Pauli string and rotation angle, required to construct the corresponding quantum circuit `circuit_layers_to_implement.csv` groups the same terms into layers to achieve parallelism in the circuit. `stage_data.json` contains key/value pairs of any other fields of interest. and those used to compose measurement layers, as outlined in Section VII D of the supplementary material: `MeasurementCommutativity` `MeasurementNaiveQubitwise` `MeasurementNonCrossing` each of which contain the files, inside the `compilation` folder: `layers.csv` lists the terms which may be measured simultaneously to achieve the measurement strategies shown in Table S14. `stage_data.json` contains key/value pairs of any other fields of interest. CSV files In `towards_quantum_simulation_data`, there are unified CSV files containing the results of applying the procedures described in the manuscript to the target materials. `circuit_costs.csv`: results of running the circuit compiler described in the manuscript. `measurements.csv`: results of running the measurement compiler described in the manuscript. These CSVs are used in the analysis script `analyse_materials_results.py` to produce the figures and tables presented in the manuscript.
Underlying data and plotting for the publication 'Mid-Circuit correction of correlated phase errors using an array of spectator qubits', K. Singh, C.E. Bradley, S. Anand, V. Ramesh, R. White and H. Bernien, 2022. This directory contains: - "Data Analysis.ipynb": A jupyter notebook from which all plots presented in the main text and supplementary materials can be reproduced. - "Data.zip": A zip file containing the json files which are loaded into "Data Analysis.ipynb". These files contain x, y, and where appropriate, y error values for each plot. The notebook should be executed using python3.