TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation
Shi-Xin Zhang, Yu-Qin Chen, Weitang Li, Jiace Sun, Wei-Guo Ma, Pei-Lin Zheng, Yu-Xiang Huang, Qi-Xiang Wang, Hui Yu, Zhuo Li, Xuyang Huang, Zong-Liang Li, Zhou-Quan Wan, Shuo Liu, Jiezhong Qiu, Jiaqi Miao, Zixuan Song, Yuxuan Yan, Kazuki Tsuoka, Pan Zhang, Lei Wang, Heng Fan

TL;DR
TensorCircuit-NG is a versatile, scalable quantum software platform that unifies quantum circuits, tensor networks, and neural networks within a differentiable framework, enabling advanced quantum simulations and machine learning applications.
Contribution
It introduces a unified tensor-native programming paradigm integrated with popular ML backends, supporting diverse quantum simulation methods and scalable distributed computing strategies.
Findings
Achieves near-linear speedup on GPU clusters for variational quantum algorithms.
Enables end-to-end quantum machine learning for CIFAR-100.
Facilitates differentiable optimization of tensor network states for many-body physics.
Abstract
We present TensorCircuit-NG, a next-generation quantum software platform designed to bridge the gap between quantum physics, artificial intelligence, and high-performance computing. Moving beyond the scope of traditional circuit simulators, TensorCircuit-NG establishes a unified, tensor-native programming paradigm where quantum circuits, tensor networks, and neural networks fuse into a single, end-to-end differentiable computational graph. Built upon industry-standard machine learning backends (JAX, TensorFlow, PyTorch), the framework introduces comprehensive capabilities for approximate circuit simulation, analog dynamics, fermion Gaussian states, qudit systems, and scalable noise modeling. To tackle the exponential complexity of deep quantum circuits, TensorCircuit-NG implements advanced distributed computing strategies, including automated data parallelism and model-parallel tensor…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum many-body systems · Machine Learning in Materials Science
