TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation

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 network slicing. We validate these capabilities on GPU clusters, demonstrating a near-linear speedup in distributed variational quantum algorithms. TensorCircuit-NG enables flagship applications, including end-to-end QML for CIFAR-100 computer vision, efficient pipelines from quantum states to neural networks via classical shadows, and differentiable optimization of tensor network states for many-body physics.