Figure 3: Causal hierarchy examples relevant to physics (left) and image classi cation (right).
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players.
Figure 1. Schematic of the setup. [Copyright ©2016 by the American Physical Society]
Physicists have proposed a method for entangling hundreds of atoms, and then entangling a dozen or so groups of these hundreds of atoms, resulting in a quantum network of thousands of entangled atoms. Since small bundles of these entangled groups can function as atomic clocks, this design is the first detailed proposal for a quantum network of atomic clocks.
Figure 1: Topological polarons*
[Reprinted under a Creative Commons CC-BY license]
Topological quantum phases cannot be characterized by Ginzburg–Landau type order parameters, and are instead described by non-local topological invariants. Experimental platforms capable of realizing such exotic states now include synthetic many-body systems such as ultracold atoms or photons. Unique tools available in these systems enable a new characterization of strongly correlated many-body states.
Quantum defects in diamond provide a non-invasive, high-resolution image (right) of the magnetic field produced by a single tumor cell immersed in a sample of human blood. Conventional optical imaging (left) cannot detect the tumor cell because the blood scatters and absorbs light. Magnetic fields pass unaffected through the blood, allowing the magnetically sensitive quantum defects to detect the tumor cell. (Image credit: Walsworth Group/Harvard)