QuForge: A library for qudits simulation

Quantum computing with qudits, an extension of qubits to multiple levels, offers promising advantages in information representation and computational density. Despite its potential, tools for qudit-based quantum computation remain underdeveloped. This article presents QuForge, a Python-based library that introduces a novel computational framework for simulating quantum circuits with arbitrary qudit dimensions. QuForge distinguishes itself by providing scalable and extensible features, such as a complete set of customizable quantum gates, support for sparse matrix representations, and compatibility with GPU and TPU accelerators to optimize performance. By leveraging differentiable frameworks, QuForge accelerates simulations and facilitates quantum machine learning and algorithm development research. Through empirical demonstrations and benchmarks, we highlight the capabilities of the library to address scalability challenges and enable advances in quantum information science, establishing it as a potential tool for advancing research and applications in high-dimensional quantum computing.