Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design

• Main Authors: Xiu-Zhe Luo, Jin-Guo Liu, Pan Zhang, Lei Wang
• Format: Article
• Language: English
• Published: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 2020-10-01
• Series: Quantum
• Online Access: https://quantum-journal.org/papers/q-2020-10-11-341/pdf/

We introduce $\texttt{Yao}$, an extensible, efficient open-source framework for quantum algorithm design. $\texttt{Yao}$ 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 $\texttt{Yao}$. 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 $\texttt{Yao}$ help boost innovation in quantum algorithm design.