Engineers at Caltech have developed an approach for quantum storage that could help pave the way for the development of large-scale optical quantum networks. 

The new system relies on nuclear spins—the angular momentum of an atom's nucleus—oscillating collectively as a spin-wave. This collective oscillation effectively chains up several atoms to store information.

The work utilizes a quantum bit (or qubit) made from an ion of ytterbium (Yb), a rare earth element also used in lasers. The team, led by Andrei Faraon (BS '04), professor of applied physics and electrical engineering, embedded the ion in a transparent crystal of yttrium orthovanadate (YVO4) and manipulated its quantum states via a combination of optical and microwave fields. The team then used the Yb qubit to control the nuclear spin states of multiple surrounding vanadium atoms in the crystal.

"Based on our previous work, single ytterbium ions were known to be excellent candidates for optical quantum networks, but we needed to link them with additional atoms. We demonstrate that in this work," says Faraon, the co-corresponding author of the Nature paper.

The device was fabricated at the Kavli Nanoscience Institute at Caltech, and then tested at very low temperatures in Faraon's lab.

A new technique to utilize entangled nuclear spins as a quantum memory was inspired by methods used in nuclear magnetic resonance (NMR).

"To store quantum information in nuclear spins, we developed new techniques similar to those employed in NMR machines used in hospitals," says Joonhee Choi, a postdoctoral fellow at Caltech and co-corresponding writer of the paper. "The main challenge was to adapt existing techniques to work in the absence of a magnetic field."

A unique feature of this system is the pre-determined placement of vanadium atoms around the ytterbium qubit as prescribed by the crystal lattice. Every qubit the team measured had an identical memory register, meaning it would store the same information.

"The ability to build a technology reproducibly and reliably is key to its success," says graduate student Andrei Ruskuc, first author of the paper. "In the scientific context, this let us gain unprecedented insight into microscopic interactions between ytterbium qubits and the vanadium atoms in their environment."

This research is part of a broader effort by Faraon's lab to lay the foundation for future quantum networks.

Quantum networks would connect quantum computers through a system that operates at a quantum, rather than classical, level. In theory, quantum computers w one day be able to perform certain functions faster than classical computers by taking advantage of the special properties of quantum mechanics, including superposition, which allows quantum bits to store information as a 1 and a 0 simultaneously.

As they can with classical computers, engineers would like to be able to connect multiple quantum computers to share data and work together—creating a "quantum internet." This would open the door to several applications, including the ability to solve computations that are too large to be handled by a single quantum computer, as well as the establishment of unbreakably secure communications using quantum cryptography.

UK scientists analyzing one of the largest genomic datasets of plants have discovered how the first plants on Earth evolved the mechanisms used to control water and ‘breathe’ on land hundreds of millions of years ago. 

The study by the University of Bristol and the University of Essex, published in New Phytologist, has important implications in understanding how plant water transport systems have evolved and how these might adapt in the future in response to climate change.

Over the last 500 million years, the evolution of land plants has supported the diversity of life on an increasingly green planet. Throughout their evolution, plants have acquired adaptations such as leaves and roots, allowing them to control water colonize the land. Some of these ‘tools’ evolved in early land plants and today are found in both tiny mosses and giant trees which form complex forest ecosystems.

Researchers from Essex’s School of Life Sciences and Bristol’s Schools of Biological Sciences and Geographical Sciences first compared the genes of 532 plant species to investigate the role of new and old genes in the genesis of these adaptations. Of these, the team focused on 218 genes which were genes related to major innovations in land plant evolution such as roots and vascular tissues.

They discovered that some early traits essential for land plants, like stomata (pores that plants use to ‘breathe’), are related to the origin of new genes. In contrast, later innovations (e.g. roots, the vascular system) recycle old genes that emerged in the ancestors of land plants and showed that different parts of plant anatomies (stomata, vascular tissue, roots) are involved in the transport of water were linked to different methods of gene evolution.

Dr. Jordi Paps, joint lead author and Senior Lecturer from Bristol's School of Biological Sciences, explained: “Our analyses shed new light on the genetic basis of the greening of the planet, highlighting the different methods of gene evolution in the diversification of the plant kingdom. Historically it has not been clear if evolutionary innovations are driven by the emergence of new genes or by the repurposing of old ones. Our findings tell us how plants have evolved at distinct moments in their history and how different modes of evolution, the origin of new genes, and the recycling of older ones, contributed to the emergence of major innovations key to the greening of the planet."

Dr. Ulrike Bechtold, joint lead author and Senior Lecturer from Essex’s School of Life Sciences explained that this study “provides insights into the mechanistic changes underpinning water uptake and transport, which are important for plant health and productivity. It allows researchers to select and investigate the function of old, repurposed, and new genes in the lab, with the aim to select genes that reduce water use and improve drought resilience in crop plants.”

Dr Alexander Bowles from Bristol’s School of Geographical Sciences, one of the study’s co-authors, added: “As well as helping us make sense of the past, this work is important for the future. By understanding how water transport systems have evolved, we can begin to understand the limiting factors for plant growth. This has particular importance when considering the growth of crops as well as their resilience to drought.”

How does a neural network work? How does it react to a failure? How can you mathematically predict when it will stop working and what will happen when it does. All these questions have now been answered by a research team led by Àlex Arenas, a professor at the Universitat Rovira i Virgili's Department of Computer Engineering and Mathematics. Arenas has found the theoretical explanation for a very complex process that will now make it possible to predict how all network systems will function. 

Percolation is the process by which a network system suffers a failure at a particular point that ends up affecting the whole network structure. One example of this is an electrical network that leaves a whole district without electricity when there is a problem in one tower. The process is even clearer in biological systems such as neurons. For various reasons, neurons degenerate until some of them die. These neuronal failures, caused by aging, diseases, or accidents, eventually lead to a significant loss of connectivity to the brain and the neural network stops working properly. The scientific community has been studying this percolation process for decades, along with what is known as phase transitions: the point at which a network will stop functioning completely if it is cut.

"Our research began a few years ago when I was working with the UB neuroscience team led by the researcher Jordi Soriano," explains Arenas. "We observed that, even though we directly damaged neuronal connections with lasers, the system continued to function very efficiently." This phenomenon is known as homeostatic plasticity: despite being cut, the system tries to continue doing what it was doing before the cut. It looks for alternatives, ways to continue functioning correctly.

Now, the URV research team has managed to find the answers to the phase transitions of percolation degradation: that is to say, to understand how much damage a system can undergo before it will be degraded and lose its functionality. "We have been able to find this transition and we have also been able to calculate the homeostatic response (i.e., the ability to find alternatives and continue functioning) of the network," says Arenas.

These results are important because the scientific community now has at its disposal a set of mathematical tools "that can be very useful not only in neuroscience but in any type of network," he says. The research is a step forward in our understanding of how network systems react to external damage while maintaining their functionality, compensating for the failure in one of the parts, and redirecting activity to another.

"Understanding these processes can provide solutions in many areas," says Arenas, who gives us an example of diseases such as Alzheimer's, in which many patients can remember episodes from their childhood but not more recent aspects of their lives. This is related to the degradation undergone by their neural network. Now we understand why this happens and we can apply this knowledge in research in people who begin to suffer from the disease. "For example, we will be able to know how they respond to control questions, infer to what extent their neuronal system is degraded and try medication or some other sort of intervention in an attempt to reconnect because now we know how these degradation processes act physically," says the researcher.

The results of the research can also be applied to other fields, such as road networks. If a road needs to be cut and traffic redirected to other areas, it will be possible to predict where there will be jams and what action will have to be taken to absorb the traffic there.

A recent ecosystem modeling study conducted by Iowa State University scientists shows how crop production in the United States has led to an increase in the emissions of nitrous oxide, a potent greenhouse gas, throughout the last century. 

The researchers drew on massive amounts of data on everything from weather patterns to soil conditions to land use and agricultural management practices to feed the model and quantify changes in nitrous oxide emissions from soils in the United States. The research, published in the peer-reviewed academic journal Global Change Biology, breaks soil emissions down by ecosystem types and major crops and found that the expansion of land devoted to agriculture since 1900 and intensive fertilizer inputs have predominantly driven an overall increase in nitrous oxide emissions.

The use of such ecosystem models to assess the sources of nitrous oxide emissions could help guide policymakers as they enact conservation plans and responses to climate change said Chaoqun Lu, associate professor of ecology, evolution, and organismal biology and corresponding author of the study.

“The model we are using is a process-based ecosystem model,” Lu said. “It’s similar to mimicking the patterns and processes of an ecosystem in our computer. We divide the land into thousands of pixels at a uniform size and run algorithms that simulate how ecological processes respond to changes in climate, air composition, and human activities.”

Results show emissions tripled

The study found nitrous oxide emissions from U.S. soil have more than tripled since 1900, from 133 million metric tons of carbon dioxide equivalent (MMT CO2 eq) per year at the beginning of the 20th century to 404 MMT CO2 eq per year in the 2010s. Nearly three-quarters of that rise in emissions originate from agricultural soils with corn and soybean production driving over 90% of the ag-related emissions increase, according to the study.

“Our study suggests a large [nitrous oxide] mitigation potential in cropland and the importance of exploring crop-specific mitigation strategies and prioritizing management alternatives for targeted crop types,” the study authors wrote in their paper.

The rise in emissions corresponds to an expansion of cropland in the United States, Lu said. The computer models found land devoted to agricultural production emits more nitrous oxide than natural landscapes. That’s largely due to the widespread application of nitrogen fertilizers to agricultural land and legume crop production, Lu said. The added nitrogen is partially used by crops, and the remainder either stays in soils or is lost to the environment. During this process, microorganisms living in soils consume nitrogen-containing compounds and give off nitrous oxide as a byproduct. Better understanding the dynamics of which crops lead to the greatest emissions can help shape climate mitigation policy, Lu said. Because more nitrogen fertilizer is applied in corn production on average than other crops, the study found soils, where corn is grown, tend to emit more nitrous oxide per unit of fertilizer used, Lu said.

The researchers designed mathematical models that mimic ecological processes. The models rely on mountains of data gathered and developed over years, Lu said. The researchers compiled government data on crops, land use, weather, and other variables. They also factored in historic and survey data from farmers and other landowners.

The research team also compared the results from their model with real-world data to validate their results. For instance, the scientists showed their model’s yield predictions tracked with national yield records dating back to 1925 for major crops such as corn, soybean, wheat, rice, and others. That shows the model simulation could track the long-term trajectory of nitrogen uptake that supports increasing crop yield over the past century. They also compared their model’s nitrous oxide emission predictions to real-world data collected from multiple natural and managed soils across the nation, as well as time-series measurements from a central Iowa corn-soybean rotation site over seven years.

“Our group has spent lots of time improving model performance and developing the driving force history, including natural and human disturbances, for the model simulations,” Lu said. “Behind the scenes, there are thousands of lines of algorithms to guide the computer model to make predictions. It takes decades of efforts, and more to come, to reduce modeling uncertainties and incorporate better ecological process understanding resulting from the hard work of field scientists.”