To state that the human brain has capabilities that are, in some respects, far superior to those of all other known objects in the cosmos would be uncontroversial. The brain is the only kind of object capable of understanding that the cosmos is even there, or why there are infinitely many prime numbers, or that apples fall because of the curvature of space-time, or that obeying its own inborn instincts can be morally wrong, or that it itself exists. Nor are its unique abilities confined to such cerebral matters. The cold, physical fact is that it is the only kind of object that can propel itself into space and back without harm, or predict and prevent a meteor strike on itself, or cool objects to a billionth of a degree above absolute zero, or detect others of its kind across galactic distances. Read More
Daily Archives: August 28, 2019
Quantum radar: Experimental Microwave Quantum Illumination
Quantum illumination is a powerful sensing technique which employs entangled photons to boost the detection of low-reflectivity objects in environments with bright thermal noise. The promised advantage over classical strategies is particularly evident at low signal photon flux, a feature which makes the protocol an ideal prototype for non-invasive biomedical scanning or low-power short-range radar detection. In this work we experimentally demonstrate quantum illumination at microwave frequencies. We generate entangled fields using a Josephson parametric converter at millikelvin temperatures to illuminate a room-temperature object at a distance of 1 meter in a proof of principle bistatic radar setup. Using heterodyne detection and suitable data-processing at the receiver we observe an up to three times improved signal-to-noise ratio compared to the classical benchmark,the coherent-state transmitter, outperforming any classically-correlated radar source at the same signal powerand bandwidth. Quantum illumination is a first room-temperature application of microwave quantum circuits demonstrating quantum supremacy in detection and sensing. Read More
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully-observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning,computational game theory, and search. Read More
Rick's Cafe AI 