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Quantum Machine Learning
Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design
arXiv
Authors: Xiu-Zhe Luo, Jin-Guo Liu, Pan Zhang, Lei Wang
Year
2019
Paper ID
39638
Status
Preprint
Abstract Read
~2 min
Abstract Words
87
Citations
N/A
Abstract
We introduce Yao, an extensible, efficient open-source framework for quantum algorithm design. Yao features generic and differentiable programming of quantum circuits. It achieves state-of-the-art performance in simulating small to intermediate-sized quantum circuits that are relevant to near-term applications. We introduce the design principles and critical techniques behind Yao. These include the quantum block intermediate representation of quantum circuits, a builtin automatic differentiation engine optimized for reversible computing, and batched quantum registers with GPU acceleration. The extensibility and efficiency of Yao help boost innovation in quantum algorithm design.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2019 reference point for readers tracking recent quantum research.
- We introduce Yao, an extensible, efficient open-source framework for quantum algorithm design.
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