New research provides a Rosetta Stone that translates the language of reinforcement learning to the quantum realm. It tackles sticky questions like what it means for a quantum agent to learn and how the history of a quantum agent's interaction with its environment can be captured in a meaningful way. It also shows how a standard algorithm in the quantum toolkit can help agents learn faster in settings where an early stroke of luck can make a big difference--like when learning how to navigate a maze. Future research could investigate whether a quantum computer, with the added help of a quantum agent, could learn about its own noisy environment fast enough to change the way it reacts to errors. The work may also shed light on one of the deepest questions in physics: How does the everyday world arise from interactions that are, at the microscopic level, described by quantum mechanics?
Google has revealed it is bringing together its machine learning and quantum computing initiatives with the launch of TensorFlow Quantum. The machine learning framework has the ability to construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators and train both discriminative and generative quantum models. According to a Google AI blog, TensorFlow Quantum is able to create quantum models with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs. The release of TensorFlow Quantum comes after Microsoft's launch of Azure Quantum and the recent news that Honeywell is developing a quantum computer with a quantum volume of at least 64 which will be available in the next three months. In an abstract for a paper, authored by members of Alphabet's X unit, The Institute for Quantum Computing at the University of Waterloo, NASA's Quantum AI Lab, Volkswagen and Google Research, submitted to the preprint repository arXiv, the authors explain what they believe TensorFlow Quantum can achieve, saying: "We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage."
At Xanadu we are developing a photonic quantum computer: a device that processes information stored in quantum states of light. We are very excited by the possibilities that this approach brings. Photonic quantum computers naturally use continuous degrees of freedom -- like the amplitude and phase of light -- to encode information. This continuous, or analog, structure makes photonic devices a natural platform for quantum versions of neural networks. How do we mimic a neural network using a photonic system?
A century ago, the quantum revolution quietly began to change our lives. A deeper understanding of the behavior of matter and light at atomic and subatomic scales sparked a new field of science that would vastly change the world's technology landscape. Today, we rely upon the science of quantum mechanics for applications ranging from the Global Positioning System to magnetic resonance imaging to the transistor. The advent of quantum computers presages yet another new chapter in this story that will enable us to not only predict and improve chemical reactions and new materials and their properties, for example, but also to provide insights into the emergence of spacetime and our universe. Remarkably, these advances may begin to be realized in a few years.
Google today announced the launch of TensorFlow Quantum, bringing together machine learning and quantum computing initiatives at the company. The framework can construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models. Last fall, Google said it achieved quantum supremacy with the debut of a newly engineered solution. The release of TensorFlow Quantum follows the launch of Azure Quantum and progress by companies like Honeywell. Creating quantum models is made possible with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs, according to a Google AI blog.