The team will consider three different approaches for creating entangled quantum memories that could facilitate the long-distance transmission of secure information. The five-year Multidisciplinary University Research Initiative (MURI) will be led by the Georgia Institute of Technology and include scientists from Columbia University, Harvard University, the Massachusetts Institute of Technology, the University of Michigan, Stanford University and the University of Wisconsin.

“We want to develop a set of novel and powerful approaches to quantum networking,” said Alex Kuzmich, a professor in Georgia Tech’s School of Physics and the MURI’s principal investigator.  “The three basic capabilities will be (1) storing quantum information for longer periods of time, on the order of seconds, (2) converting the information to light, and (3) transmitting the information over long distances. We aim to create large-scale systems that use entanglement for quantum communication and potentially also quantum computing.”

The MURI scientists will study three different physical platforms for designing the matter-light interaction used to generate the entangled photons.  These include neutral atom memories with electronically-excited Rydberg-level interactions, nitrogen-vacancy (NV) defect centers in diamonds, and charged quantum dots.

“A large body of work has been initiated in this area over the past 15 years by our team members and their research groups,” Kuzmich noted. “The physical approaches are different, but the goals are closely related, so there are significant opportunities for synergistic activities. Through this MURI, we will be able to interact more closely, communicate more quickly and provide new opportunities for our students and postdoctoral fellows.”

Overall, the MURI has four major goals:

• To implement efficient light-matter interfaces using three different approaches to entanglement;
• To realize entanglement lifetimes of more than one second in both the nitrogen-vacancy centers and atomic quantum memories;
• To implement two-qubit quantum states within memory nodes;
• To integrate different components and physical implementations into small units capable of significant quantum processing tasks.

Quantum memories generated from the interaction of neutral atoms and light now have maximum lifetimes of approximately 200 milliseconds.  But improvements beyond memory lifetime will be needed before practical systems can be created.

“We aim to be able to combine systems, so that instead of just one memory entangled with one photon, perhaps we could have four of them,” Kuzmich added.  “This may look like a straightforward thing to do, but this is not easy in the laboratory.  The improvements must be made at every level, so the difficulty is significant.”

Among the challenges ahead are maintaining separation between the different memory systems, and minimizing loss of light as signals propagate through the optical fiber systems that would be used to transmit entangled photons.  

“Light is easily lost, and there’s not much that can be done about that from a fundamental physics standpoint,” said Kuzmich.  “The rates of these protocols go down rapidly as you try to scale up the systems.”

Kuzmich and his Georgia Tech research team have been developing quantum memory based on the interaction of light with neutral atoms such as rubidium.  They have made substantial progress over the past decade, but he says it’s not clear which approach will ultimately be used to create large-scale quantum communication system.

The most immediate applications for the quantum memory are in secure communications, in which the entanglement of photons with matter would provide a new form of encryption.

“The immediate focus is on communication, including memories and distributed systems, which is important for sharing and transmitting information,” Kuzmich explained.  “It also has implications for quantum computation because similar techniques are often used.”

In addition to Kuzmich, collaborators in the MURI include:

• Luming Duan, professor of physics in the School of Physics at the University of Michigan, Ann Arbor, Michigan.
• Dirk Englund, assistant professor of electrical engineering and applied physics in the School of Engineering and Applied Science at Columbia University, New York, New York.
• Marko Lonkar, associate professor of electrical engineering in the School of Engineering and Applied Sciences at Harvard University, Cambridge, Massachusetts.
• Brian Kennedy, professor of physics in the School of Physics at the Georgia Institute of Technology, Atlanta, Georgia.
• Mikhail Lukin, professor of physics in the Department of Physics at Harvard University, Cambridge, Massachusetts.
• Mark Saffman, professor of physics in the Department of Physics at the University of Wisconsin, Madison, Wisconsin.
• Jelena Vuckovic, associate professor of electrical engineering in the Department of Electrical Engineering at Stanford University, Stanford, California.
• Vladan Vuletic, the Lester Wolfe Professor of Physics in the School of Physics at Massachusetts Institute of Technology, Cambridge, Massachusetts.
• Thad Walker, professor of physics in the Department of Physics at the University of Wisconsin, Madison, Wisconsin.

“If we are successful with this over the next five years, long-distance quantum communications may become promising for real-world implementation,” Kuzmich added.  “Integrating these advances with existing infrastructure – optical fiber that’s in the ground – will continue to be an important engineering challenge.”

This material is based upon work conducted under contract FA9550-12-1-0025.  Any opinions, findings and conclusions or recommendations expressed are those of the researchers and do not necessarily reflect the views of the Air Force Office of Scientific Research.

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An assistant professor in the School of Electrical and Computer Engineering (ECE) at Georgia Tech, Dr. Hong was honored for his paper, "Improving Prediction Accuracy of Protein-DNA Docking with GPU Computing." He shares this award with two coauthors–Jiadong Wu, his Ph.D. student, and Jun-tao Guo, a colleague from the Department of Bioinformatics and Biomedicine at the University of North Carolina at Charlotte.

Protein-DNA docking represents one of the most challenging problems in structural bioinformatics. Knowledge of how proteins interact with DNA is critical for understanding many key biological processes and for structure-based drug design. This paper describes a high performance computing method that Dr. Hong and his team have developed to tackle the protein-DNA docking problem using a GPU cluster. This protein-DNA docking algorithm integrates Monte-Carlo simulation and a simulated annealing method and has achieved 10.4 TFLOPS of sustained performance using 128 GPU cards, which represents 4× speed up over a traditional cluster with 1000 CPU cores. Such improved computation capability accelerates the conformational space sampling for the docking algorithm and increases the chance of finding near-native protein-DNA structures.

The research, which comprises three separate projects, includes work with a new mouse model of ovarian cancer to identify early detection biomarkers; an effort to characterize proteins and protein variants secreted from ovarian tumors that could serve as serum biomarkers; and work to identify metabolic changes that could help diagnose the disease.

"This grant is a program project development grant, and the idea is to bring together a number of individuals around a common theme," Martin Matzuk, a BCM professor of pathology and immunology and one of the leaders of the project, told ProteoMonitor. "We were previously funded by OCRF along with a number of investigators to focus on the role of microRNAs in ovarian cancer. That work has gone very well, so we put together another proposal in which we decided to focus on biomarkers, whether they're protein or small molecule."

Matzuk is collaborating on the work with his BCM colleague Laising Yen as well as John McDonald, a professor, the associate dean for biology program development in the school of Biology at Georgia Tech, and a chief research scientist at Atlanta's Ovarian Cancer Institute.
McDonald, who will head up the search for metabolomic biomarkers, leads a research team that published a paper in August 2010 detailing a metabolomic ovarian cancer diagnostic that identified women with ovarian cancer with 100 percent accuracy in a 94-subject trial (PM 8/20/2010).

That test used direct-analysis-in-real-time mass spectrometry to measure thousands of metabolites in subjects' blood samples, classifying them with a functional support vector machine-based machine-learning algorithm. McDonald's team is still validating their findings, McDonald told ProteoMonitor this week, but thus far "everything is looking good," and, he said, the researchers hope to finish validating the results sometime within the year.

Under the OCRF grant, the Georgia Tech team plans to use LC-MS/MS to identify specific metabolites detected by their DART-MS work in hopes of combining them with protein biomarkers identified by Matzuk's lab to build an early detection panel for ovarian cancer.
The DART analysis "gives us thousands of features, and for most of them we don't know what they are," McDonald said. "From a diagnostic point of view we don't really care as long as it's a reliable diagnostic. But at the same time we're now running LC-MS/MS to try to whittle it down to identify … the specific metabolites involved."

"The idea is that we'll put it together [with Matzuk's markers] to see what an optimal diagnostic might consist of," he said.

Matzuk and the BCM researchers will be looking for protein biomarkers using a recently developed mouse model of high-grade serous ovarian cancer in which the cancer actually begins in the fallopian tube as opposed to the ovary itself. The model reflects an alternate view of ovarian cancer development "that is gaining a lot of support," Matzuk said.

Because ovarian cancer is difficult to detect early, often by the time patient samples are collected it's "too late to be trying to figure out what are the changes with regard to proteins or metabolic changes," he said. "The nice thing about having a mouse model is that these animals get cancers universally, and so you can open the animals up at a certain period and say, 'OK, at this time point what are the expression changes in these cancers? What are the earliest time points [they are visible]?"

"The goal of all three projects is to [identify] the various transcripts that are out there in these cancers," Matzuk said. "The idea is, once we catalog all of them, to go back in and then screen or develop antibodies to new variants of proteins or new secreted proteins and see whether or not those could be better markers."

The ultimate goal of the work, he said, "is to generate enough data so that we could actually go into the National Institutes of Health for a bigger project that we could start not only between our groups, but also with other groups and centers to look at various biomarkers."

Price will be a major consideration for any early detection test, Matzuk said, noting that he thinks even existing triage tests like Vermillion's OVA1 don't offer enough to justify their cost. Given the low prevalence of ovarian cancer in the general population, he said, any broad screening test for the disease would need to cost under $50 for it to be covered widely by insurers.

"I run a clinical chemistry laboratory in the county hospital, and for us to be doing this kind of screening of healthy women you need to have the cost low," he said.
However, Matzuk suggested, declining instrumentation prices could help bring costs down in the future – particularly in the case of mass spec-based tests, where multiplexing could significantly lower the price of multi-analyte assays.

"Maybe everyone will have [mass spec] analysis of their serum at some point," he said. "I think right now the instrumentation is too expensive and the testing is too expensive to go ahead and say this is for general [screening] tests, but if it turns out that these tests are extremely valuable, people are going to find a way to make them cheaper."

From 2008 to 2010, a team of bioinformatics graduatestudents, led by School of Biology Associate Professor King Jordan, worked inclose collaboration with the Centers for Disease Control and Prevention (CDC)to create an integrated suite of computational tools for the analysis ofmicrobial genome sequences.  At thattime, CDC scientists were in need of a fast and accurate system that couldautomate the analysis of sequenced genomes from disease-causing bacteria. Theyturned to the Jordan lab at Georgia Tech to help develop such a tool. TheGeorgia Tech scientists created an open source software package, the ComputationalGenomics Pipeline (CG-pipeline), to help meet CDC’s need. The softwareplatform is now used worldwide in public health research and response efforts.

“Determining the order of DNA bases for an entire genome hasbecome relatively cheap and easy in recent years because of technologicaladvancements,” said Jordan. “The hard part is figuring out what the genomesequence information means. Our software takes that next step. It analyzes the sequences,finds the genes and provides clues as to which genes are involved in makingpeople sick. Manually, this process used to take weeks, months or a year. Nowit takes us about 24 hours.”

The CG-pipeline software has been used to analyze lastsummer’s outbreak of  severe Escherichia coli(E. coli) infections that started in Germany and eventually led to illnesses in16 European countries, Canada and the United States. It was one of the largest E.coli outbreaks in history, causing 50 deaths and 4,075 confirmed worldwidecases. The bacterium was traced to sprouts. Andrey Kislyuk, a graduate of the BioinformaticsPh.D. program who helped Jordan create the software, used the CG-pipeline whileworking at Pacific Biosciences to understand why the strain of the bacteriathat caused the outbreak was so virulent.

“The software was used to determine that genetic materialfrom two previously distinct strains of E. coli  was combined in a new, hyper-virulent strain,” said Kislyuk. “Theresulting hybrid strain seems to be more lethal than either of the parentstrains.”

Another Bioinformatics Ph.D. graduate who helped design andimplement the pipeline, Lee Katz, analyzed the bacteria that caused last year’soutbreak of listeriosis in the United States while working at the CDC.  That outbreak was traced back to cantaloupesfrom a single farm in Colorado that were tainted with Listeria. Over the spanof several months, there were 146 confirmed cases of listeriosis and 30 deaths,making it the deadliest outbreak of foodborne illness in the U.S. in 25 years.Using the CG-pipeline, Katz was able to identify an important epidemiological genomicmarker, which will help track invasive strains of Listeria.

The CG-pipeline software platform can be used to analyze anymicrobial genome sequence. It has already been applied to bacteria that cause avariety of infectious diseases, including cholera, salmonella and bacterialmeningitis.

Katz continues to work closely with the Jordan lab toimprove the software. This collaboration is important in CDC’s efforts to minegenome sequence information in the service of public health using softwaredeveloped at Georgia Tech.

“During Dr. Yuan’s time on the ISyE faculty, he has made valuable contributionsin research, teaching, and service. Dr. Yuan’s exceptional teaching ability isevident in the excellent teaching evaluations and student praise hereceives.  We are fortunate to have him as a colleague and now as theISyE’s newest Coca-Cola Junior Professor,” said Jane C. Ammons, H. Milton andCarolyn J. Stewart School Chair.

The Coca-Cola Junior Professorship is supported by a gift from Coca-Cola, inorder to support research and development in ISyE. Endowed professorships, suchas this one, are awarded to outstanding faculty, ensuring them the resourcesthey need to remain at the forefront of their fields and to lead teaching andresearch efforts in their key areas.

In addition to this recent honor, Yuan was the recipient of the NationalScience Foundation Career Award in 2009 for his exemplary work in sparsemodeling and estimation with high-dimensional data.  He was also named asa Distinguished Cancer Scholar from the Georgia Cancer Coalition in 2007, andwas the recipient of the John van Ryzin Award in 2004.

Yuan received his PhD in statistics from the University of Wisconsin atMadison. He also holds a master’s in computer science from the University ofWisconsin, and a bachelor’s in electrical engineering and information sciencefrom the University of Science & Technology of China. Yuan's currentresearch interests include statistical learning, bioinformatics, and methods ofregularization.

The following awards were presented to IAC faculty:

Big Data Award
Alan L. Porter, Professor Emeritus, Schools of Public Policy and Industrial & Systems Engineering

The Big Data Award recognizes a Georgia Tech researcher or research group that has successfully established a strong research relationship with industry and commercialized technology for the management and application of large complex datasets to solve problems in society and industry and, in so doing, developed new tools and methods for capture, storage, analysis, searching and visualization of information.

People and Technology Award
Michael L. Best, Associate Professor, The Sam Nunn School of International Affairs and School of Interactive Computing; Editor-in- Chief, Information Technologies & International Development

The People and Technology Award honors the efforts of Georgia Tech researchers whose pursuit of research in the interaction between humans and technology demonstrates a transformative societal impact in improving the human condition.

Public Service, Leadership, and Policy Award
Dan Breznitz, Associate Professor, The Sam Nunn School of International Affairs

The Public Service, Leadership, and Policy Award recognized a Georgia Tech researcher or research group that has made a significant contribution to the study of innovation and policies that promote and sustain research for public benefit.

Innovation in Literature and Communication Award
Thomas N. Lux, Professor, School of Literature, Communication, and Culture; Founder and Director of Poetry@Tech

The Innovation in Literature and Communication Award is given in recognition for innovation in communication and literature in science, engineering, and technology.

The Gala Celebration Honoring Innovators and Inventors & GTRC for 75 years of Service to Georgia Tech Faculty took place December 12, 2011 at the Georgia Tech Hotel and Conference Center Ballroom.