Qibolab: an open-source hybrid quantum operating system
Author:
Efthymiou Stavros1, Orgaz-Fuertes Alvaro1, Carobene Rodolfo231, Cereijo Juan14, Pasquale Andrea156, Ramos-Calderer Sergi14, Bordoni Simone178, Fuentes-Ruiz David1, Candido Alessandro569, Pedicillo Edoardo156, Robbiati Matteo59, Tan Yuanzheng Paul10, Wilkens Jadwiga1, Roth Ingo1, Latorre José Ignacio1114, Carrazza Stefano9561
Affiliation:
1. Quantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE. 2. Dipartimento di Fisica, Università di Milano-Bicocca, I-20126 Milano, Italy. 3. INFN - Sezione di Milano Bicocca, I-20126 Milano, Italy. 4. Departament de Física Quàntica i Astrofísica and Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spain. 5. TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano, Italy 6. INFN, Sezione di Milano, I-20133 Milan, Italy. 7. Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy 8. La Sapienza University of Rome, dep. of Physics, Rome, Italy 9. CERN, Theoretical Physics Department, CH-1211 Geneva 23, Switzerland. 10. Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore. 11. Centre for Quantum Technologies, National University of Singapore, Singapore.
Abstract
We present Qibolab, an open-source software library for quantum hardware control integrated with the Qibo quantum computing middleware framework. 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. 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.
Publisher
Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
Reference139 articles.
1. R. Brun and F. Rademakers, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 389, 81 (1997), new Computing Techniques in Physics Research V. 2. J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H.-S. Shao, T. Stelzer, P. Torrielli, and M. Zaro, Journal of High Energy Physics 2014, 10.1007/jhep07(2014)079 (2014). 3. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems (2015), software available from tensorflow.org. 4. Cirq, a python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits (2018). 5. M. Broughton and et al., Tensorflow quantum: A software framework for quantum machine learning (2020).
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