Qibo: a framework for quantum simulation with hardware acceleration
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Abstract (arXiv)
We present Qibo, a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interest in quantum computing and the recent developments of quantum hardware devices motivates the development of new advanced computational tools focused on performance and usage simplicity. In this work we introduce a new quantum simulation framework that enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem and quantum algorithms at hand. This software is designed from scratch with simulation performance, code simplicity and user friendly interface as target goals. It takes advantage of hardware acceleration such as multi-threading CPU, single GPU and multi-GPU devices.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2020.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 5.
10 papers.
- 2021Theory of overparametrization in quantum neural networkscited by 100
- 2023Understanding quantum machine learning also requires rethinking generalizationcited by 48
- 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
- 2021Style-based quantum generative adversarial networks for Monte Carlo eventscited by 19
- 2022Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learningcited by 11
- 2023Effects of noise on the overparametrization of quantum neural networkscited by 9
- 2021A Leap among Quantum Computing and Quantum Neural Networks: A Surveycited by 8
- 2021quantum Case-Based Reasoning (qCBR)cited by 4
- 2023Training Neural Networks with Universal Adiabatic Quantum Computingcited by 1
- 2023Benchmarking machine learning models for quantum state classificationcited by 0
- 2023Benchmarking machine learning models for quantum state classification14 Sep 2023
- 2023Training Neural Networks with Universal Adiabatic Quantum Computing24 Aug 2023
- 2023Understanding quantum machine learning also requires rethinking generalization23 Jun 2023
- 2023Effects of noise on the overparametrization of quantum neural networks10 Feb 2023
- 2023Quantum anomaly detection in the latent space of proton collision events at the LHC25 Jan 2023
- 2022Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning30 Jun 2022
- 2021Style-based quantum generative adversarial networks for Monte Carlo events13 Oct 2021
- 2021Theory of overparametrization in quantum neural networks23 Sep 2021
- 2021A Leap among Quantum Computing and Quantum Neural Networks: A Survey6 Jul 2021
- 2021quantum Case-Based Reasoning (qCBR)1 Apr 2021