IARPA in the News
IBM claims advance in effort to build reliable, large-scale quantum computer
The race to build a universal quantum computer is gaining steam, with IBM claiming a breakthrough that paves the way to large-scale systems that can operate reliably.
IBM researchers have developed error-correction techniques that could maintain the integrity of computations performed using qubits, or quantum bits -- the basis of quantum computing. As with conventional computing, isolating and resolving data errors is a key step to building a fully functional quantum computer, said Jay Gambetta, a manager of IBM's quantum computing and information group....
A paper on the research will appear in the April 29 issue of Nature Communications. The research was partly funded by the U.S. government's Intelligence Advanced Research Projects Activity. IARPA also funds research to develop a new superconductor semiconductor, which is an important component for quantum computers.
IBM Brings Quantum Computing a Step Closer
Researchers at IBM have stitched together a prototype circuit that could become the basis of quantum computers a decade hence.
The circuit, an assemblage of four supercooled, superconducting devices known as qubits, checks for the critical errors that make quantum chips so difficult to build. The IBM research is set to be described Wednesday in a paper published in the scientific journal Nature Communications.
Demonstration of a quantum error detection code using a square lattice of four superconducting qubits
The ability to detect and deal with errors when manipulating quantum systems is a fundamental requirement for fault-tolerant quantum computing. Unlike classical bits that are subject to only digital bit-flip errors, quantum bits are susceptible to a much larger spectrum of errors, for which any complete quantum error-correcting code must account. Whilst classical bit-flip detection can be realized via a linear array of qubits, a general fault-tolerant quantum error-correcting code requires extending into a higher-dimensional lattice. Here we present a quantum error detection protocol on a two-by-two planar lattice of superconducting qubits. The protocol detects an arbitrary quantum error on an encoded two-qubit entangled state via quantum non-demolition parity measurements on another pair of error syndrome qubits. This result represents a building block towards larger lattices amenable to fault-tolerant quantum error correction architectures such as the surface code....
We thank M. B. Rothwell and G. A. Keefe for fabricating devices. We thank J. R. Rozen, J. Rohrs and K. Fung for experimental contributions. We thank S. Bravyi and J. A. Smolin for engaging discussions. We thank I. Siddiqi for providing the JPAs. We acknowledge Caltech for HEMT amplifiers. We acknowledge support from IARPA under contract W911NF-10-1-0324. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the US Government.
SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources
Rapidly increasing volumes of news feeds from diverse data sources, such as online newspapers, Twitter and online blogs are proving to be extremely valuable resources in helping anticipate, detect, and forecast outbreaks of rare diseases. This paper presents SourceSeer, a novel algorithmic framework that combines spatio-temporal topic models with sourcebased anomaly detection techniques to effectively forecast the emergence and progression of infectious rare diseases. SourceSeer is capable of discovering the location focus of each source allowing sources to be used as experts with varying degrees of authoritativeness. To fuse the individual source predictions into a final outbreak prediction we employ a multiplicative weights algorithm taking into account the accuracy of each source. We evaluate the performance of SourceSeer using incidence data for hantavirus syndromes in multiple countries of Latin America provided by HealthMap over a timespan of fifteen months. We demonstrate that SourceSeer makes predictions of increased accuracy compared to several baselines and is capable of forecasting disease outbreaks in a timely manner even when no outbreaks were previously reported....
This work is supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC000337, the US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Brain Projects Get Researchers Thinking Big
The human brain consists of about 100 billion neurons, yet no one knows how many distinct types or shapes of those neurons exist. Creating a framework for answering that basic question and many other, much more complex questions is among the goals of a new multi-institutional research effort.
The project, called BigNeuron, aims to create reliable high-throughput and quantitative 3D reconstructions of the thousands of branches that make up individual neurons. It’s a crucial step, say researchers, to ultimately understanding how the brain encodes information....
BigNeuron’s launch comes as interest in researching the human brain has grown enough in recent years to spawn two large-scale, public-private research efforts....
NIH hopes to step up activity within and across four partner federal agencies in BRAIN—FDA, NSF, the Defense Advanced Research Projects Agency (DARPA), and Intelligence Advanced Research Projects Activity (IARPA). Cross-agency efforts can be expected along the lines of the Collaborative Research in Computational Neuroscience (CRCNS) program. NIH and NSF have joined with the German and French funding agencies and the United States-Israel Binational Science Foundation to review and fund proposals intended to add to “understanding of nervous system structure and function, mechanisms underlying nervous system disorders, and computational strategies used by the nervous system.”
Can the Military Make a Prediction Machine?
What could the military do if it could better understand the massive amounts of data that humanity creates, an estimated 2.5 quintillion bytes every day? Could it predict aspects of the future?...
The ways in which humans interact with government, with one another, with medical facilities, transit systems and brands, etc. can predict events of national security significance. They can indicate, for instance, if a deadly disease outbreak is taking hold in a small rural community or if civil unrest is on the rise.
One example of that is the Open Source Indicators Program, launched in 2011 by from the Intelligence Advanced Research Programs agency. Led by program manager Jason Matheny, Open Source Indicators funds projects to predict events of national security relevance by monitoring tens of thousands of blogs, RSS feeds, news reports, social network chatter from sites like Twitter and Facebook, and other open sources...
Join Patrick Tucker online for a video discussion with DARPA’S Paul Cohen and IARPA’s Jason Matheny at 11 a.m. EDT on Monday, April 13. Find out more below, or sign up here for the Defense One viewcast.
IARPA Wants A Magical All-In-One Chemical Detection Tool
America’s Intelligence Advanced Research Projects Agency is filled with a bunch of nerds. At the very least, they’ve got a Lord of the Rings fan in their acronym-making department. Their latest project, open for solicitations last week, is called the “Standoff ILluminator for Measuring Absorbance and Reflectance Infrared Light Signatures,” or SILMARILS. Named for the fictional crystal jewels of Silmarillion fame, SILMARILS is a device that will illuminate and discover trace chemicals from almost 100 feet away.