NapkinML: Pocket-Sized Implementation of Machine Learning Models in NumPy

Machine Learning (ML), while unable to precisely simulate natural systems, excels in learning system models and predicting their behavior. Classic ML models have, in recent years, addressed numerous scientific challenges, playing a significant role in cancer detection, earthquake aftershock prediction, extreme weather forecasting, and extrasolar planet detection. The burgeoning discoveries in quantum computing technology are poised to have a profound impact on quantum machine learning models, promising breakthroughs in medicine, materials, sensors, and communication domains. However, the bottleneck encountered so far lies in the absence of research tools to discover useful quantum machine learning models capable of handling quantum data and usable on computers. Addressing this, Google's secretive division, Google X, collaborates with the University of Waterloo and Volkswagen to introduce TensorFlow Quantum (TFQ), an open-source library for rapidly prototyping quantum ML models. TFQ empowers the fusion of quantum computing and machine learning research communities by providing essential tools to control/model natural or artificial quantum systems, such as noise intermediate-scale quantum processors (NISQ) with approximately 50-100 qubits. At its core, TFQ integrates the open-source framework Cirq for NISQ algorithm development. Cirq, tailored by Google for NISQ algorithms, allows developers to craft quantum algorithms for specific quantum processors.