qiskit-machine-learning and quantum
These are complementary frameworks that can work together—Qiskit Machine Learning provides quantum circuits and algorithms optimized for Qiskit's execution stack, while TensorFlow Quantum integrates quantum operations as differentiable layers within TensorFlow's deep learning ecosystem, allowing practitioners to choose based on whether they prioritize Qiskit's native quantum tools or TensorFlow's broader ML infrastructure.
About qiskit-machine-learning
qiskit-community/qiskit-machine-learning
An open-source library built on Qiskit for quantum machine learning tasks at scale on quantum hardware and classical simulators
This library helps quantum machine learning researchers and practitioners design and experiment with machine learning models that leverage quantum computing principles. It takes classical datasets as input and produces classification or regression models that can run on quantum hardware or simulators. Users are typically quantum algorithm developers or scientists exploring the cutting edge of quantum AI.
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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