Wednesday, September 25th, 2019

Time	Event
12:00a	Quantum sensing on a chip MIT researchers have, for the first time, fabricated a diamond-based quantum sensor on a silicon chip. The advance could pave the way toward low-cost, scalable hardware for quantum computing, sensing, and communication. “Nitrogen-vacancy (NV) centers” in diamonds are defects with electrons that can be manipulated by light and microwaves. In response, they emit colored photons that carry quantum information about surrounding magnetic and electric fields, which can be used for biosensing, neuroimaging, object detection, and other sensing applications. But traditional NV-based quantum sensors are about the size of a kitchen table, with expensive, discrete components that limit practicality and scalability. In a paper published in Nature Electronics, the researchers found a way to integrate all those bulky components — including a microwave generator, optical filter, and photodetector — onto a millimeter-scale package, using traditional semiconductor fabrication techniques. Notably, the sensor operates at room temperature with capabilities for sensing the direction and magnitude of magnetic fields. The researchers demonstrated the sensor’s use for magnetometry, meaning they were able to measure atomic-scale shifts in the frequency due to surrounding magnetic fields, which could contain information about the environment. With further refining, the sensor could have a range of applications, from mapping electrical impulses in the brain to detecting objects, even without a line of sight. “It’s very difficult to block magnetic fields, so that’s a huge advantage for quantum sensors,” says co-author Christopher Foy, a graduate student in the Department of Electrical Engineering and Computer Science (EECS). “If there’s a vehicle traveling in, say, an underground tunnel below you, you’d be able to detect it even if you don’t see it there.” Joining Foy on the paper are: Mohamed Ibrahim, a graduate student in EECS; Donggyu Kim PhD ’19; Matthew E. Trusheim, a postdoc in EECS; Ruonan Han, an associate professor in EECS and head of the Terahertz Integrated Electronics Group, which is part of MIT's Microsystems Technology Laboratories (MTL); and Dirk Englund, an MIT associate professor of electrical engineering and computer science, a researcher in Research Laboratory of Electronics (RLE), and head of the Quantum Photonics Laboratory. Shrinking and stacking NV centers in diamonds occur where carbon atoms in two adjacent places in the lattice structure are missing — one atom is replaced by a nitrogen atom, and the other space is an empty “vacancy.” That leaves missing bonds in the structure, where the electrons are extremely sensitive to tiny variations in electrical, magnetic, and optical characteristics in the surrounding environment. The NV center essentially functions as an atom, with a nucleus and surrounding electrons. It also has photoluminescent properties, meaning it absorbs and emits colored photons. Sweeping microwaves across the center can make it change states — positive, neutral, and negative — which in turn changes the spin of its electrons. Then, it emits different amounts of red photons, depending on the spin. A technique, called optically detected magnetic resonance (ODMR), measures how many photons are emitted by interacting with the surrounding magnetic field. That interaction produces further, quantifiable information about the field. For all of that to work, traditional sensors require bulky components, including a mounted laser, power supply, microwave generator, conductors to route the light and microwaves, an optical filter and sensor, and a readout component. The researchers instead developed a novel chip architecture that positions and stacks tiny, inexpensive components in a certain way using standard complementary metal-oxide-semiconductor (CMOS) technology, so they function like those components. “CMOS technologies enable very complex 3-D structures on a chip,” Ibrahim says. “We can have a complete system on the chip, and we only need  a piece of diamond and green light source on top. But that can be a regular chip-scale LED.” NV centers within a diamond slab are positioned in a “sensing area” of the chip. A small green pump laser excites the NV centers, while a nanowire placed close to the NV centers generates sweeping microwaves in response to current. Basically, the light and microwave work together to make the NV centers emit a different amount of red photons — with the difference being the target signal for readout in the researchers’ experiments. Below the NV centers is a photodiode, designed to eliminate noise and measure the photons. In between the diamond and photodiode is a metal grating that acts as a filter that absorbs the green laser photons while allowing the red photons to reach the photodiode. In short, this enables an on-chip ODMR device, which measures resonance frequency shifts with the red photons that carry information about the surrounding magnetic field. But how can one chip do the work of a large machine? A key trick is simply moving the conducting wire, which produces the microwaves, at an optimal distance from the NV centers. Even if the chip is very small, this precise distance enables the wire current to generate enough magnetic field to manipulate the electrons. The tight integration and codesign of the microwave conducting wires and generation circuitry also help. In their paper, the researchers were able to generate enough magnetic field to enable practical applications in object detection. Only the beginning In another paper presented earlier this year at the International Solid-State Circuits Conference, the researchers describe a second-generation sensor that makes various improvements on this design to achieve 100-fold greater sensitivity. Next, the researchers say they have a “roadmap” for how to increase sensitivity by 1,000 times. That basically involves scaling up the chip to increase the density of the NV centers, which determines sensitivity. If they do, the sensor could be used even in neuroimaging applications. That means putting the sensor near neurons, where it can detect the intensity and direction of firing neurons. That could help researchers map connections between neurons and see which neurons trigger each other. Other future applications including a GPS replacement for vehicles and airplanes. Because the magnetic field on Earth has been mapped so well, quantum sensors can serve as extremely precise compasses, even in GPS-denied environments. “We’re only at the beginning of what we can accomplish,” Han says. “It’s a long journey, but we already have two milestones on the track, with the first-and second-generation sensors. We plan to go from sensing to communication to computing. We know the way forward and we know how to get there.” “I am enthusiastic about this quantum sensor technology and foresee major impact in several fields,” says Ron Walsworth, a senior lecturer at Harvard University whose group develops high-resolution magnetometry tools using NV centers. “They have taken a key step in the integration of quantum-diamond sensors with CMOS technology, including on-chip microwave generation and delivery, as well as on-chip filtering and detection of the information-carrying fluorescent light from the quantum defects in diamond. The resulting unit is compact and relatively low-power. Next steps will be to further enhance the sensitivity and bandwidth of the quantum diamond sensor [and] integrate the CMOS-diamond sensor with wide-ranging applications, including chemical analysis, NMR spectroscopy, and materials characterization.” (Comment on this)
12:00a	How cities can leverage citizen data while protecting privacy India is on a path with dual — and potentially conflicting — goals related to the use of citizen data. To improve the efficiency their municipal services, many Indian cities have started enabling government-service requests, which involves collecting and sharing citizen data with government officials and, potentially, the public. But there’s also a national push to protect citizen privacy, potentially restricting data usage. Cities are now beginning to question how much citizen data, if any, they can use to track government operations. In a new study, MIT researchers find that there is, in fact, a way for Indian cities to preserve citizen privacy while using their data to improve efficiency. The researchers obtained and analyzed data from more than 380,000 government service requests by citizens across 112 cities in one Indian state for an entire year. They used the dataset to measure each city government’s efficiency based on how quickly they completed each service request. Based on field research in three of these cities, they also identified the citizen data that’s necessary, useful (but not critical), or unnecessary for improving efficiency when delivering the requested service. In doing so, they identified “model” cities that performed very well in both categories, meaning they maximized privacy and efficiency. Cities worldwide could use similar methodologies to evaluate their own government services, the researchers say. The study was presented at this past weekend’s Technology Policy Research Conference. “How do municipal governments collect citizen data to try to be transparent and efficient, and, at the same time, protect privacy? How do you find a balance?” says co-author Karen Sollins, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL), a principal investigator for the Internet Policy Research Initiative (IPRI), and a member of the Privacy, Innovation and e-Governance using Quantitative Systems (PIEQS) group. “We show there are opportunities to improve privacy and efficiency simultaneously, instead of saying you get one or the other, but not both.” Joining Sollins on the paper are: first author Nikita Kodali, a graduate student in the Department of Electrical Engineering and Computer Science; and Chintan Vaishnav, a senior lecturer in the MIT Sloan School of Management, a principal investigator for IPRI, and a member PIEQS. Intersections of privacy and efficiency In recent years, India’s eGovernment Foundation has aimed to significantly improve the transparency, accountability, and efficiency of operations in its many municipal governments. The foundation aims to move all of these governments from paper-based systems to fully digitized systems with citizen interfaces to request and interact with government service departments. In 2017, however, India’s Supreme Court ruled that its citizens have a constitutional right to data privacy and have a say in whether or not their personal data could be used by governments and the private sector. That could potentially limit the information that towns and cities could use to track the performance of their services. Around that time, the researchers had started studying privacy and efficiency issues surrounding the eGovernment Foundation’s digitization efforts. That led to a report that determined which types of citizen data could be used to track government service operations. Building on that work, the researchers were provided 383,959 anonymized citizen-government transactions from digitized modules from 112 local governments in an Indian state for all of 2018. The modules focused on three areas: new water tap tax assessment; new property tax assessment; and public grievances about sanitation, stray animals, infrastructure, schools, and other issues. Citizens send requests to those modules via mobile or web apps by entering various types of personal and property information, and then monitor the progress of the requests. The request and related data pass through various officials that each complete an individual subtask, known as a service level agreement, within a designated time limit. Then, the request passes on to another official, and so on. But much of that citizen information is also visible to the public. The software captured each step of each request, moving from initiation to completion, with time stamps, for each municipal government. The researchers then could rank each task within a town or city, or in aggregation across each town or city on two metrics: a government efficiency index and an information privacy index. The government efficiency index primarily measures a service’s timeliness, compared to the predetermined service level agreement. If a service is completed before its timeframe, it’s more efficient; if it’s completed after, it’s less efficient. The information privacy index measures how responsible is a government in collecting, using, and disclosing citizen data that may be privacy sensitive, such as personally identifiable information. The more the city collects and shares inessential data, the lower its privacy rating. Phone numbers and home addresses, for instance, aren’t needed for many of the services or grievances, yet are collected — and publicly disclosed — by many of the modules. In fact, the researchers found that some modules historically collected detailed personal and property information across dozens of data fields, yet the governments only needed about half of those fields to get the job done. Model behavior By analyzing the two indices, they found eight “model” municipal governments that performed in the top 25 percent for all services in both the efficiency and privacy indices. In short, they used only the essential data — and passed that essential data through fewer officials — to complete a service in a timely manner. The researchers now plan to study how the model cities are able to get services done so quickly. They also hope to study why some cities performed so poorly, in the bottom 25 percent, for any given service. “First, we’re showing India that this is what your best cities look like and what other cities should become,” Vaishnav says. “Then we want to look at why a city becomes a model city.” Similar studies can be conducted in places where similar citizen and government data are available and which have equivalents to India’s service level agreements — which serve as a baseline for measuring efficiency. That information isn’t common worldwide yet, but could be in the near future, especially in cities like Boston and Cambridge, Vaishnav says. “We gather a large amount of data and there’s an urge to do something with the data to improve governments and engage citizens better,” he says. “That may soon be a requirement in democracies around the globe.” Next, the researchers want to create an innovation-based matrix, which will determine which citizen data can and cannot be made public to private parties to help develop new technologies. They’re also working on a model that provides information on a city’s government efficiency and information privacy scores in real time, as citizen requests are being processed. (Comment on this)
12:00p	Greener and fairer: Balancing pollution, energy prices, and household income Governments that impose taxes on carbon dioxide and other greenhouse gas emissions can benefit from a cleaner, more climate-friendly environment and a revenue stream that can be tapped to lower other taxes and create jobs. But environmental taxes may also exact an excessive financial burden on low-income households, which spend a much greater fraction of their budgets than richer households do on heating oil, natural gas, and electricity. This concern has limited the use of green taxes in Spain, where emissions are taxed at levels far below average for the European Union, which seeks to lower emissions across the continent to fulfill its 2015 Paris Agreement climate pledge. Now a new study by researchers at the MIT Joint Program on the Science and Policy of Global Change, the University of Oldenburg in Germany, and the Basque Center for Climate Change in Spain shows that low-income households in Spain can actually benefit from environmental taxes if revenues are redistributed to all taxpayers. Using a computational model to assess the environmental and economic impacts of a green tax reform policy in which revenues are recycled in equal amounts to households in annual lump-sum payments, the researchers found that the policy significantly reduces emissions without imposing economic hardship on any segment of the population. The study appears in the journal Economics of Energy and Environmental Policy. “There may be a tradeoff between efficiency and equity in climate policy design,” says Xaquin Garcia-Muros, a co-author of the study and postdoctoral associate at the MIT Joint Program. Noting the perfect can be the enemy of the good, as indicated by the November 2018 Yellow Vest protests against fuel tax hikes in France, he adds, “Governments that seek to introduce environmental policies need to show they can cut emissions equitably in order for the public to support them. Otherwise, climate mitigation measures will be rejected by public opinion, and attempts to tackle climate change will be unsuccessful.” The proposed policy includes a tax on carbon dioxide (CO2) of 40 euros per metric ton in all sectors (except transportation) not covered by the EU emissions trading system, tax increases on fossil fuels to match the EU average of 1.5 percent of GDP, and economy-wide taxes on air pollutants — nitrogen oxides, (NOx) and sulfur dioxide (SO2) emissions at 1,000 euros/metric ton. In addition, it provides annual lump-sum rebates to private households based on household income. Combining a “computable general equilibrium” model of the Spanish economy with a “micro-simulation” sub-model that characterizes households of different income levels, the researchers determined the tax reform policy’s impact on pollution levels, energy prices, and household net income. They found that the policy would significantly reduce emissions of CO2 (10 percent), NOx (13 percent) and SO2 (20 percent); produce an estimated 7.3 billion euros in annual revenues; and enable annual lump-sum rebates of 400 euros. Most importantly, the rebates would offset the cost of the green taxes for the bottom half of income levels, with the poorest households receiving an average annual net benefit of 203 euros and the richest paying a net cost of 599 euros. “We expect similar results in other southern European and public transit-oriented countries,” says Garcia-Muros. “But while results will differ for each country, all can benefit by ensuring that green tax policies accommodate economic inequality.” An earlier MIT Joint Program study showed how this principle can be applied in the design of carbon pricing policies in the United States. (Comment on this)

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