Google recently released TensorFlow Quantum, a toolset for combining state-of-the-art machine learning techniques with quantum algorithm design. This is an essential step to build tools for developers working on quantum applications. Simultaneously, they have focused on improving quantum computing hardware performance by integrating a set of quantum firmware techniques and building a TensorFlow-based toolset working from the hardware level up – from the bottom of the stack. The fundamental driver for this work is tackling the noise and error in quantum computers. Here's a small overview of the above and how the impact of noise and imperfections (critical challenges) is suppressed in quantum hardware.
Oct-5-2020, 17:05:21 GMT