At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry and high-energy physics. Nevertheless, challenges remain regarding the trainability of quantum machine learning models. Here we review current methods and applications for quantum machine learning. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with quantum machine learning. Quantum machine learning has become an essential tool to process and analyze the increased amount of quantum data. Despite recent progress, there are still many challenges to be addressed and myriad future avenues of research.

Paris – The bundle of instruments known as SuperCam on board the Perseverance Mars rover has collected its first samples in the hunt for past life on the planet, mission scientists said Wednesday. The return to Earth years from now of the rocks and soil it retrieves "will give scientists the Holy Grail of planetary exploration," Jean-Yves le Gall, president of France's National Center for Space Studies (CNES), which mostly built the mobile observatory, commented via a YouTube broadcast. These "pieces of Mars," he said, may "finally answer this fascinating and fundamental question: was there ever life elsewhere than Earth?" After seven months in space, NASA's Perseverance rover gently set down on Martian soil last month and sent back black-and-white images revealing the rocky fields of Jezero Crater, just north of the Mars equator. "The critical component of this astrobiology mission is SuperCam," said Thomas Zurbuchen, deputy head of NASA's Science Mission Directorate.

We're long past the point of questioning whether machines can learn. The question now is how do they learn? Machine learning, a subset of artificial intelligence, is the study of computer algorithms that improve automatically through experience. That means a machine can learn, independent of human programming. Los Alamos National Laboratory staff scientist Nga Thi Thuy Nguyen-Fotiadis is an expert on machine learning, and at 5:30 p.m. on Monday, Dec. 14, she hosts the virtual presentation "Deep focus: Techniques for image recognition in machine learning," as part of the Bradbury Science Museum's (1350 Central Ave., Los Alamos, 505-667-4444, lanl.gov/museum)

Want to know where all the wind and solar power supplies in the US are for some brilliant renewable-energy project? Or plot a round-the-world trip hitting every major soccer stadium along the way? It should be possible with a new tool that lets anyone scan the globe through AI "eyes" to instantly find satellite images of matching objects. Descartes Labs, a New Mexico startup that provides AI-driven analysis of satellite images to governments, academics and industry, on Tuesday released a public demo of its GeoVisual Search, a new type of search engine that combines satellite images of Earth with machine learning on a massive scale. The idea behind GeoVisual is pretty simple.

Rethinking conventional computer designs, which are decades old, the U.S. Department of Energy has set its sights on creating systems that could supplant today's PCs and servers. The Los Alamos National Laboratory--best known for its work with nuclear weapons--is developing and acquiring new types of computers as it looks to replace conventional computers. Its newest toy is a D-Wave 2X quantum computer, which the lab purchased from D-Wave Systems for an undisclosed price. The acquisition of the D-Wave quantum computer fits LANL's goal to understand new forms of computing and how they apply to different applications, said John Sarrao, associate director for Theory, Simulation, and Computation at LANL. LANL is also researching neuromorphic chips inspired by the functioning of the brain.

The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.

The emerging Web of Data utilizes the web infrastructure to represent and interrelate data. The foundational standards of the Web of Data include the Uniform Resource Identifier (URI) and the Resource Description Framework (RDF). URIs are used to identify resources and RDF is used to relate resources. While RDF has been posited as a logic language designed specifically for knowledge representation and reasoning, it is more generally useful if it can conveniently support other models of computing. In order to realize the Web of Data as a general-purpose medium for storing and processing the world's data, it is necessary to separate RDF from its logic language legacy and frame it simply as a data model. Moreover, there is significant advantage in seeing the Semantic Web as a particular interpretation of the Web of Data that is focused specifically on knowledge representation and reasoning. By doing so, other interpretations of the Web of Data are exposed that realize RDF in different capacities and in support of different computing models.

Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most "central" in a semantic network is difficult because one relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics has focused primarily on context-based rankings (i.e. user prescribed contexts). Moreover, many of the current semantic network metrics rank semantic associations (i.e. directed paths between two vertices) and not the vertices themselves. This article presents a framework for calculating semantically meaningful primary eigenvector-based metrics such as eigenvector centrality and PageRank in semantic networks using a modified version of the random walker model of Markov chain analysis. Random walkers, in the context of this article, are constrained by a grammar, where the grammar is a user defined data structure that determines the meaning of the final vertex ranking. The ideas in this article are presented within the context of the Resource Description Framework (RDF) of the Semantic Web initiative.

Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of $10^9$ edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.