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

TL;DR

Yao.jl is an open-source framework that enables efficient, extensible, and differentiable quantum circuit programming, facilitating rapid development and simulation of quantum algorithms for near-term applications.

Contribution

It introduces a novel design with quantum block IR, automatic differentiation, and GPU acceleration, advancing quantum algorithm development tools.

Findings

Achieves state-of-the-art simulation performance for small to intermediate quantum circuits.

Provides a flexible, extensible platform for quantum algorithm design.

Supports differentiable programming for quantum circuits.

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.