Original article can be found here (source): Deep Learning on Medium
TensorFlow Quantum: an open-source library for the rapid prototyping of Quantum Machine Learning
Over the past few years, classical ML model and promise in tackling challenging scientific issues, leading to advancement in image processing for cancer detection, predicting extreme patterns and many more. The recent progress development of quantum computing, the development of new Quantum ML models could have a profound impact on the world’s biggest problem, leading to breakthroughs in the area of medicine, sensing, material, and communication.
Despite the multiple breakthroughs in multiple areas, there has been a lack of research tools to discover Quantum ML models that can process quantum data and execute Quantum computers available today.
Google has released ‘TensorFlow Quantum (TFQ)’ an open-source library for the rapid prototyping of Quantum ML models. TFQ provides the tools necessary for bringing quantum computing and ML research communities together to control and model natural or artificial quantum systems.
What is a Quantum ML model?
A Quantum ML model has the ability to represent and generalize data with a quantum mechanical origin. To understand quantum models, two concepts must be known — quantum data and hybrid-quantum classical models.
Quantum data shows superposition and entanglement, leading to the joint probability distribution that could require an exponential amount of classical computational resources to represents or store. Quantum data, which can be simulated on quantum processors/sensors/networks include quantum matter, quantum control, quantum communication networks, quantum metrology, and much more.
Quantum data generated by NISQ processors that are fairly small and noisy. By applying Quantum ML to noisy entangled data can maximize the extraction of useful classical information.
Hybrid-quantum classical models, Quantum models cannot use quantum processors alone — the NISQ processor will need to work with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, TPUs, it is a natural platform for experimenting with hybrid-quantum classical models.