quantum

Why we included this project

TensorFlow Quantum is the practical route when you want to train machine learning models directly on quantum circuits while staying inside the TensorFlow ecosystem. Quantum circuits and Pauli operators become first-class tensors, so you can batch heterogeneous circuits and compute expectation values, samples, or state simulations with gradients that flow cleanly back through Keras layers. Because circuit definition happens in Cirq and simulation runs through qsim, researchers can prototype a hybrid quantum-classical pipeline on a classical simulator and carry the same code path onto real hardware later. The framework targets people studying quantum data, parameterized circuits, and the variational models behind much of near-term NISQ research, though it expects you to know both quantum computing basics and TensorFlow before you get far.