Qibolab: An Open-source Hybrid Quantum Operating System

Stavros Efthymiou¹, Alvaro Orgaz-Fuertes¹, Rodolfo Carobene^2,3,1, Juan Cereijo^1,4, Andrea Pasquale^1,5,6, Sergi Ramos-Calderer^1,4, Simone Bordoni^1,7,8, David Fuentes-Ruiz¹, Alessandro Candido^5,6,9, Edoardo Pedicillo^1,5,6, Matteo Robbiati^5,9, Yuanzheng Paul Tan¹⁰, Jadwiga Wilkens¹, Ingo Roth¹, José Ignacio Latorre^1,11,4, and Stefano Carrazza^9,5,6,1

¹Quantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE.
²Dipartimento di Fisica, Università di Milano-Bicocca, I-20126 Milano, Italy.
³INFN – Sezione di Milano Bicocca, I-20126 Milano, Italy.
⁴Departament de Física Quàntica i Astrofísica and Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spain.
⁵TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano, Italy
⁶INFN, Sezione di Milano, I-20133 Milan, Italy.
⁷Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
⁸La Sapienza University of Rome, dep. of Physics, Rome, Italy
⁹CERN, Theoretical Physics Department, CH-1211 Geneva 23, Switzerland.
¹⁰Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.
¹¹Centre for Quantum Technologies, National University of Singapore, Singapore.

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Abstract

We present $texttt{Qibolab}$, an open-source software library for quantum hardware control integrated with the $texttt{Qibo}$ quantum computing middleware framework. $texttt{Qibolab}$ provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. $texttt{Qibolab}$ enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.

Featured image: Qibo software ecosystem.

Popular summary

We present Qibolab, an open-source software library for quantum hardware control integrated with the Qibo, a hybrid quantum operating system. Qibolab provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. This software enables experimentalists and quantum software developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way.

► BibTeX data

@article{Efthymiou2024qibolabopensource, doi = {10.22331/q-2024-02-12-1247}, url = {https://doi.org/10.22331/q-2024-02-12-1247}, title = {Qibolab: an open-source hybrid quantum operating system}, author = {Efthymiou, Stavros and Orgaz-Fuertes, Alvaro and Carobene, Rodolfo and Cereijo, Juan and Pasquale, Andrea and Ramos-Calderer, Sergi and Bordoni, Simone and Fuentes-Ruiz, David and Candido, Alessandro and Pedicillo, Edoardo and Robbiati, Matteo and Tan, Yuanzheng Paul and Wilkens, Jadwiga and Roth, Ingo and Latorre, Jos{‘{e}} Ignacio and Carrazza, Stefano}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {8}, pages = {1247}, month = feb, year = {2024} }

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Cited by

[1] Jorge J. Martínez de Lejarza, Leandro Cieri, Michele Grossi, Sofia Vallecorsa, and Germán Rodrigo, “Loop Feynman integration on a quantum computer”, arXiv:2401.03023, (2024).

[2] Chunyang Ding, Martin Di Federico, Michael Hatridge, Andrew Houck, Sebastien Leger, Jeronimo Martinez, Connie Miao, David I. Schuster, Leandro Stefanazzi, Chris Stoughton, Sara Sussman, Ken Treptow, Sho Uemura, Neal Wilcer, Helin Zhang, Chao Zhou, and Gustavo Cancelo, “Experimental advances with the QICK (Quantum Instrumentation Control Kit) for superconducting quantum hardware”, arXiv:2311.17171, (2023).

[3] Alessandro D’Elia, Boulos Alfakes, Anas Alkhazaleh, Leonardo Banchi, Matteo Beretta, Stefano Carrazza, Fabio Chiarello, Daniele Di Gioacchino, Andrea Giachero, Felix Henrich, Alex Stephane Piedjou Komnang, Carlo Ligi, Giovanni Maccarrone, Massimo Macucci, Emanuele Palumbo, Andrea Pasquale, Luca Piersanti, Florent Ravaux, Alessio Rettaroli, Matteo Robbiati, Simone Tocci, and Claudio Gatti, “Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting”, arXiv:2402.04322, (2024).

[4] Steve Abel, Juan Carlos Criado, and Michael Spannowsky, “Training Neural Networks with Universal Adiabatic Quantum Computing”, arXiv:2308.13028, (2023).

[5] Matteo Robbiati, Alejandro Sopena, Andrea Papaluca, and Stefano Carrazza, “Real-time error mitigation for variational optimization on quantum hardware”, arXiv:2311.05680, (2023).

[6] Edoardo Pedicillo, Andrea Pasquale, and Stefano Carrazza, “Benchmarking machine learning models for quantum state classification”, arXiv:2309.07679, (2023).

The above citations are from SAO/NASA ADS (last updated successfully 2024-02-15 02:03:05). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2024-02-15 02:03:03).

This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.

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