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.

翻译：基于量子dit的量子计算是量子比特向多能级扩展的研究领域，其发展成熟度目前尚不及基于量子比特的量子计算。然而，量子dit通过以更少的分离组件表示信息，相较于量子比特具有若干潜在优势。本文介绍QuForge——一个基于Python、专为模拟量子dit量子电路设计的程序库。该库提供了实现量子算法所需的全套量子门操作，并可适配任意选定的量子dit维度。QuForge构建于可微分框架之上，支持在GPU和TPU等加速设备上运行，从而显著提升模拟速度。同时，该库支持稀疏运算，与其他程序库相比可有效降低内存消耗。此外，通过将量子电路构建为可微分计算图，QuForge为量子机器学习算法的实现提供了便利，进一步拓展了量子计算研究的应用能力与灵活性。