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Hardware-agnostic quantum ML framework enabling gradient-based training of quantum circuits, hybrid quantum-classical models, and seamless integration with PyTorch/JAX/TensorFlow alongside device portability across IBM/Google/Rigetti/IonQ.

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

Hardware-agnostic quantum machine learning framework with automatic differentiation for training quantum circuits, building hybrid quantum-classical models, and deploying across IBM, Google, Rigetti, IonQ, and simulators.

Qiskit is a Python framework for building, simulating, and executing quantum circuits. It supports local simulation, noisy simulation, and IBM Quantum hardware execution. Users can utilize V2 primitives like Sampler and Estimator for variational algorithms and quantum machine learning tasks.

Qiskit is a leading open-source quantum computing framework designed for building, optimizing, and executing quantum circuits. It provides comprehensive tools to run on local simulators or major cloud quantum backends, including IBM Quantum, IonQ, and Amazon Braket. Use it to develop algorithms in chemistry, machine learning, and optimization.