TensorFlow Quantum provides an open space for developers to create programs capable of running on both classical and quantum computers.
TensorFlow Quantum (TSQ) takes quantum information, which is collected in units called “qubits,” and processes them with systems that support both traditional AI and quantum platforms. This approach allows developers to work out new uses and applications for quantum data and inform the development of quantum algorithms.
According to a blog post from Google AI: “TFQ provides the tools necessary for bringing the quantum computing and machine learning research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50 – 100 qubits.”