PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

python machine-learning deep-learning neural-network qml tensorflow optimization quantum differentiable-computing automatic-differentiation pytorch autograd quantum-computing quantum-chemistry quantum-machine-learning qiskit jax cirq

• Updated Nov 12, 2026
• Python

Automatic differentiation in C++; infinite differentiability of conditionals, loops, recursion and all things C++

machine-learning differentiable-computing automatic-differentiation

• Updated Apr 5, 2019
• C++

GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.

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• Updated Feb 13, 2024
• Python

A library for soft differentiable relaxations of common JAX functions.

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• Updated Jul 1, 2026
• Python

A library for soft differentiable relaxations of common PyTorch functions.

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• Updated Jun 28, 2026
• Python

Fast Differentiable Forest lib with the advantages of both decision trees and neural networks

differentiable-computing decision-trees attention-mechanism end-to-end-learning quantum-learning

• Updated Nov 21, 2021
• Python

The PennyLane-Cirq plugin integrates Google's Cirq software library with with PennyLane's quantum machine learning capabilities.

python machine-learning qml tensorflow quantum differentiable-computing pytorch quantum-computing quantum-chemistry quantum-algorithms quantum-machine-learning jax cirq

• Updated Nov 11, 2026
• Python

Differentiable Neural Computer in TensorFlow

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• Updated Mar 27, 2017
• Jupyter Notebook

A simple differentiable 2D physics simulator written in jax and pytorch

deep-learning differentiable-computing pytorch physics-2d differentiable-physics-engine jax

• Updated Jul 25, 2024
• Python

Differentiable model selection for quantum theory

differentiable-computing quantum-foundations

• Updated Jun 13, 2023
• Jupyter Notebook

JAX-native differentiable multigrid solver for steady-state diffusion–absorption problems.

differentiable-computing scientific-computing multigrid diffusion pde-solver jax differentiable-physics

• Updated Nov 2, 2026
• Python

Explore quantum computing with notebooks, guides, and code for core concepts, algorithms, and hands-on learning

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• Updated Oct 13, 2026
• Jupyter Notebook

Differentiable stochastic-computing primitives for PyTorch: train neural networks natively SC-aware.

python machine-learning fpga deep-learning neural-network differentiable-computing pytorch embedded-systems stochastic quantization low-power bitstream stochastic-optimization neuromorphic-computing chip-design differentiable-optimization hardware-aware-training

• Updated Jun 14, 2026
• Python