torchquantum and quantum
These are competitors offering similar core functionality—both are hybrid quantum-classical ML frameworks—but targeting different deep learning backends (PyTorch vs TensorFlow), so practitioners typically choose one based on their existing ML infrastructure rather than using them together.
About torchquantum
mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
This framework helps quantum computing researchers and algorithm designers quickly simulate quantum circuits and quantum machine learning models on classical hardware. You can input descriptions of quantum circuits or quantum neural networks using familiar PyTorch commands, and it outputs simulated quantum states, measurement results, and enables gradient calculations for optimization. It's designed for quantum algorithm researchers, quantum machine learning practitioners, and those working on quantum neural networks.
About quantum
tensorflow/quantum
An open-source Python framework for hybrid quantum-classical machine learning.
TensorFlow Quantum helps quantum algorithm researchers and machine learning practitioners combine quantum mechanics with traditional machine learning. It takes quantum circuit definitions and classical data, processing them to produce results for advanced quantum computing research. This framework is for those exploring novel hybrid quantum-classical computing workflows, especially when leveraging Google's quantum offerings.
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