  TeD-Q is an open-source software framework for quantum machine learning,
variational quantum algorithm (VQA), and simulation of quantum computing. It
seamlessly integrates classical machine learning libraries with quantum
simulators, giving users the ability to leverage the power of classical machine
learning while training quantum machine learning models. TeD-Q supports
auto-differentiation that provides backpropagation, parameters shift, and
finite difference methods to obtain gradients. With tensor contraction,
simulation of quantum circuits with large number of qubits is possible. TeD-Q
also provides a graphical mode in which the quantum circuit and the training
progress can be visualized in real-time.
