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
About quantum
tensorflow/quantum
An open-source Python framework for hybrid quantum-classical machine learning.
Integrates Cirq for quantum circuit design and qsim for high-performance simulation, while implementing quantum operations as native C++ TensorFlow Ops for seamless integration in the compute graph. Provides automatic differentiation of quantum circuits through multiple gradient methods (parameter shift, adjoint) and leverages Keras for defining quantum machine learning models, enabling researchers to scale quantum algorithm exploration across millions of circuit simulations.
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