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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. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing.

NOTICE OF CONSOLIDATION & PARTNERSHIP PENDING As of April 2026, the 20 pipelines of the QCAUS/PDPBioGen suites are undergoing consolidation for high-scale institutional research. Core 'Ford 2026' algorithms remain the proprietary IP of the Ford Peace and Justice Foundation. Academic users at partner institutions are currently performing validation; all other commercial inquiries must contact the author Computational Neuroscience: Large-scale neural population dynamics, brain-inspired...

...It has been utilized for developing several quantum machine learning applications. With the PaddlePaddle deep learning platform empowering QC, Paddle Quantum provides strong support for the scientific research community and developers in the field to easily develop QML applications. Moreover, it provides a learning platform for quantum computing enthusiasts.