Abstract
AbstractWith noisy intermediate scale quantum computers (NISQ) becoming larger in scale and more reliable, quantum circuits are growing in size and complexity. In order to face the challenge of achieving optimal circuits, design automation approaches for improving and mapping quantum circuits on different architectures have been proposed, each one characterized by a specific optimization strategy. In this article, the use of a template-based approach for quantum circuits optimization purposes is explored, and the proposal of a modular compilation toolchain, which supports three quantum technologies (nuclear magnetic resonance, trapped ions and superconducting qubits), is presented. The toolchain tackles the task of implementing logic synthesis for single-qubit and multi-qubit gates in the compilation process and it is structured with multiple steps and modular libraries. The toolchain was tested through a benchmarking procedure, and the results for a subset of complex quantum circuits as inputs are here reported, alongside a comparison with those provided by the compilers of IBM’s Qiskit and Cambridge Quantum Computing’s $$ \mathrm{\left. {t|ket} \right\rangle } $$
t
|
ket
. The current toolchain prototype was crafted to be an easily expandable and reliable core for future developments, which could lead it to support even more quantum technologies and a fully fledged layout synthesis. Nonetheless, the obtained results are quite encouraging, and they prove that in certain conditions the Toolchain can be competitive in quantum circuits optimization, especially when dealing with single-qubit gates.
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Modeling and Simulation,Signal Processing,Theoretical Computer Science,Statistical and Nonlinear Physics,Electronic, Optical and Magnetic Materials
Reference64 articles.
1. Soeken, M., Häner, T., Roetteler, M.: Programming quantum computers using design automation. (2018). arXiv:1803.01022
2. Preskill, J.: Lecture notes on quantum information and computation. http://theory.caltech.edu/~preskill/ph229/. Accessed 3-January-2022
3. Loredo, R.: Learn Quantum computing with python and IBM Quantum Experience: A hands-on introduction to quantum computing and writing your own quantum programs with Python. Packt Publishing Ltd, 2020. https://www.packtpub.com/product/learn-quantum-computing-with-python-and-ibm-quantum-experience/9781838981006
4. Jang, W., Terashi, K., Saito, M., Bauer, C. W., Nachman, B., Iiyama, Y., Kishimoto, T., Okubo, R., Sawada, R., Tanaka, J.: Quantum gate pattern recognition and circuit optimization for scientific applications. In: EPJ Web Conf., vol. 251, p. 03023, 2021. https://doi.org/10.1051/epjconf/202125103023
5. Munoz-Coreas, E.: Resource efficient design of quantum circuits for cryptanalysis and scientific computing applications. PhD thesis, University of Kentucky, Electrical and Computer Engineering (2020). https://doi.org/10.13023/etd.2020.365
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献