QuForge让多能级量子计算模拟更高效，支持任意维度量子电路。
QuForge: A Library for Qudits Simulation
- 基于Python构建，支持任意维度的量子门操作
- 利用可微分图结构加速模拟，支持GPU/TPU加速
- 适合研究量子机器学习与高维量子系统的研究者
相较于基于量子比特的量子计算，基于多能级量子态（qudits）的量子计算仍处于较不成熟阶段。然而，使用更少的分离组件即可表示信息，使该方法具有潜在优势。本文提出QuForge，一个基于Python的量子电路模拟库，支持任意选定维度的量子门操作。该库建立在可微分框架之上，可在GPU和TPU等加速设备上运行，显著提升模拟速度；同时支持稀疏运算，相比其他库有效降低内存消耗。通过将量子电路构造成可微分图，支持量子机器学习算法实现，增强了量子计算研究的灵活性与能力。
原文摘要 · Abstract (English)
Quantum computing with qudits, an extension of qubits to multiple levels, is a research field less mature than qubit-based quantum computing. However, qudits can offer some advantages over qubits, by representing information with fewer separated components. In this article, we present QuForge, a Python-based library designed to simulate quantum circuits with qudits. This library provides the necessary quantum gates for implementing quantum algorithms, tailored to any chosen qudit dimension. Built on top of differentiable frameworks, QuForge supports execution on accelerating devices such as GPUs and TPUs, significantly speeding up simulations. It also supports sparse operations, leading to a reduction in memory consumption compared to other libraries. Additionally, by constructing quantum circuits as differentiable graphs, QuForge facilitates the implementation of quantum machine learning algorithms, enhancing the capabilities and flexibility of quantum computing research.
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