A team of researchers led by the Department of Energy's Oak Ridge National Laboratory has demonstrated a new method for splitting light beams into their frequency modes. The scientists can then choose the frequencies they want to work with and encode photons with quantum information. Their work could spur advancements in quantum information processing and distributed quantum computing.

The frequency of light determines its color. When the frequencies are separated, as in a rainbow, each color photon can be encoded with quantum information, delivered in units known as qubits. Qubits are analogous to but different from classical bits, which have a value of either 0 or 1, because qubits are encoded with values of both 0 and 1 at the same time.

The researchers liken quantum information processing to stepping into a hallway and being able to go both ways, whereas in classical computing just one path is possible.

The team's novel approach - featuring the first demonstration of a frequency tritter, an instrument that splits light into three frequencies - returned experimental results that matched their predictions and showed that many quantum information processing operations can be run simultaneously without increasing error. The quantum system performed as expected under increasingly complex conditions without degrading the encoded information.

"Under our experimental conditions, we got a factor 10 better than typical error rates," said Nicholas Peters, Quantum Communications team lead for ORNL's Quantum Information Science Group. "This establishes our method as a frontrunner for high-dimensional frequency-based quantum information processing."

Photons can carry quantum information in superpositions - where photons simultaneously have multiple bit values - and the presence of two quantum systems in superposition can lead to entanglement, a key resource in quantum computing.

Entanglement boosts the number of calculations a quantum computer could run, and the team's focus on creating more complex frequency states aims to make quantum simulations more powerful and efficient. The researchers' method is also notable because it demonstrates the Hadamard gate, one of the elemental circuits required for universal quantum computing.

"We were able to demonstrate extremely high-fidelity results right off the bat, which is very impressive for the optics approach," said Pavel Lougovski, the project's principal investigator. "We are carving out a subfield here at ORNL with our frequency-based encoding work."

The method leverages widely available telecommunications technology with off-the-shelf components while yielding high-fidelity results. Efforts to develop quantum repeaters, which extend the distance quantum information can be transmitted between physically separated computers, will benefit from this work.

"The fact that our method is telecom network-compatible is a big advantage," Lougovski said. "We could perform quantum operations on telecom networks if needed."

Peters added that their project demonstrates that unused fiber-optic bandwidth could be harnessed to reduce computational time by running operations in parallel.

"Our work uses frequency's main advantage - stability - to get very high fidelity and then do controlled frequency jumping when we want it," said Wigner Fellow Joseph Lukens, who led the ORNL experiment. The researchers have experimentally shown that quantum systems can be transformed to yield desired outputs.

The researchers suggest their method could be paired with existing beam-splitting technology, taking advantage of the strengths of both and bringing the scientific community closer to full use of frequency-based photonic quantum information processing.

The team's findings were published in Physical Review Letters.

An unbiased approach for sifting through big data
Sapporo, Japan (SPX) Feb 03 A new method could help researchers develop unbiased indicators for assessing complex systems such as population health.

Researchers have developed a complex system model to evaluate the health of populations in some U.S. cities based only on the most significant variables expressed in available data. Their unbiased network-based probabilistic approach to mine big data could be used to assess other complex systems, such as ranking universities or evaluating ocean sustainability.

Societies today are data-rich, which can both empower and overwhelm. Sifting through this data to determine which variables to use for the assessment of something like the health of a city's population is challenging.

Researchers often choose these variables based on their personal experience. They might decide that adult obesity rates, mortality rates, and life expectancy are important variables for calculating a generalized metric of the residents' overall health. But are these the best variables to use? Are there other more important ones to consider?

Matteo Convertino of Hokkaido University in Japan and Joseph Servadio of the University of Minnesota in the U.S. have introduced a novel probabilistic method that allows the visualization of the relationships between variables in big data for complex systems.

The approach is based on "maximum transfer entropy," which probabilistically measures the strength of relationships between multiple variables over time.

Using this method, Convertino and Servadio mined through a large amount of health data in the U.S. to build a "maximum entropy network" (MENet): a model composed of nodes representing health-related variables, and lines connecting the variables.

The lines are darker the stronger the interdependence between two variables. This allowed the researchers to build an "Optimal Information Network" (OIN) by choosing the variables that had the most practical relevance for assessing the health status of populations in 26 U.S. cities from 2011 to 2014. By combining the data from each selected variable, the researchers were able to compute an "integrated health value" for each city. The higher the number, the less healthy a city's population.

They found that some cities, such as Detroit, had poor overall health during that timeframe. Others, such as San Francisco, had low values, indicating more favorable health outcomes. Some cities showed high variability over the four year period, such as Philadelphia. Cross-sectional comparisons showed tendencies for California cities to score better than other parts of the country. Also, Midwestern cities, including Denver, Minneapolis, and Chicago, appeared to perform poorly compared to other regions, contrary to national city rankings.

Convertino believes that methods like this, fed by large data sets and analysed via automated stochastic computer models, could be used to optimize research and practice; for example for guiding optimal decisions about health.

"These tools can be used by any country, at any administrative level, to process data in real-time and help personalize medical efforts," says Convertino.

But it is not just for health - "The model can be applied to any complex system to determine their Optimal Information Network, in fields from ecology and biology to finance and technology. Untangling their complexities and developing unbiased systemic indicators can help improve decision-making processes," Convertino added.

Research paper

Related Links
Oak Ridge National Laboratory
Space Technology News - Applications and Research

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Quantum cocktail provides insights on memory control
Zurich, Switzerland (SPX) Feb 05, 2018
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