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Quantum computers promise a new paradigm of computation where information is processed in a way that has no classical analogue. However, the known problems for which quantum computers offer a computational advantage, require long gate sequences and large number of qubits. Error-correction codes and fault-tolerant gate implementation require the encoding of logical qubits on a large number of physical qubits, and given the overheads, it is expected that general purpose quantum computers will have millions of physical qubits, thus requiring an underlying qubit technology that can be manufactured at scale. Photons make great qubits, they are cheap to produce, resilient to noise and the only known option for quantum networks. Most crucially, they can be efficiently manipulated with silicon photonics, an intrinsically scalable and manufacturable platform in which all the fundamental quantum gates can be implemented. In this talk, I will describe an architecture for universal fault-tolerant quantum computing based on linear optics, in the process I will explain how measurement-induced non-linearity can overcome the challenge of creating entanglement and how loss can be effectively tackled with error correcting codes.
In the last decade, we have experienced a significant leap forward in computer vision for tasks such as object recognition, reconstruction, 3D vision, detection, and tracking, where artificial systems have reached human and sometimes even "superhuman" performance. These advances result from the combination of novel machine learning algorithms, more powerful computers and large-enough curated datasets, which allow for off-line learning. This success has not yet transferred to robotics. In robotics, despite significant progress, machines are still far behind humans when it comes to physical and purposeful interaction with unstructured environments. Humans largely exploit physical interactions with the surroundings to solve complex long-horizon tasks (cooking, cleaning, tidying, assembling...) in "perceptually dirty", cluttered, uncontrolled, and often unknown environments such as homes, and offices. On the contrary, most of the state-of-the-art robotic solutions are restricted to short-horizon tasks in "perceptually clean" environments and try to minimize the interaction with the surroundings.
Despite all the challenges of physical interaction, in my research at SVL I advocate to consider interactions with the environment as part of the solution instead of the problem. In my talk I will present work in our group exploiting physical interaction to solve robotic tasks. I will present our work on Interactive Navigation, tasks where the robotic agent needs to interact with the environment (e.g. open doors, push away obstacles) to achieve a desired location. Solving this type of navigation is necessary to move in common human uncontrolled environments such as our homes. I will also present our work on Mechanical Search, where we equip robots with skills to search efficiently for target objects in piles of cluttered objects using physical interaction (pushing other objects, grasping them), a problem that is faced frequently not only in home robotics but also in logistic domains. And finally, I will present iGibson, SVL's large effort to provide interactive agents with a simulation environment to train and test interactive AI solutions, and demonstrate that our approach outperforms state-of-the-art learning-based and classical methods on real-world data while maintaining efficiency.
Over the next few years, food making robots are expected to be prevalent in our lives. The speaker describes this trend, shows video demonstrations of some commercially available food making robots and indicates what we could expect in the future.
Quantum computers exploit the bizarre features of quantum physics -- uncertainty, entanglement, and measurement -- to perform tasks that are impossible using conventional means, such as computing over ungodly amounts of data, and communicating via teleportation. I will describe the architecture of a quantum computer based on individual atomic clock qubits, suspended and isolated with electric fields, perfectly replicable with no idle errors, and individually addressed with laser beams. This leading physical representation of a quantum computer has allowed unmatched demonstrations of small algorithms and emulations of hard quantum problems with more than 50 quantum bits. While this system can solve some esoteric tasks that cannot be accomplished in conventional devices, it remains a great engineering challenge to build a quantum computer big enough to be generally useful for society. But the good news is that this is not a scientific challenge, as we know the technology needed and it's not quantum.
Water contamination can harm human health and alter aquatic ecosystems. Current treatment only addresses collected wastewater, although most pollution occurs from uncollected water (e.g., agricultural runoff). Water quality is typically monitored at treatment plants with time-intensive, costly, analytical laboratory techniques that limit monitoring frequency and spatiotemporal resolution; exhibit dangerous lag times between sampling and measurement; and constrain monitoring to treatment plants. Presently, real-time water monitoring is restricted to indirect measures of pollution (e.g., pH, dissolved oxygen); we design modular sensors that directly measure pollutants such as ammonium, which contributes to harmful algal blooms. Our first step toward that vision is designing selective, durable, deployable ammonia sensors that achieve laboratory method detection (1 mg/L) at field sensor prices (<$30) and breakthrough selectivity (>99%).
Through the transformative impact of quantum computing, powerful software and algorithms for the next generation of high-performance computing are being developed. These quantum algorithms will provide solutions to some of the world's toughest computational problems, including simulation, optimization, and machine learning applications in chemistry, finance, logistics, pharmaceuticals, engineering, and materials - in short, solve massive, real-world problems that will revolutionize industry and society.
Especially in recent years, we have witnessed a dramatic increase in the amount of investment and progress made in both quantum computing hardware and software architectures. This talk will look at the dramatic developments of the last decade and discuss the various approaches to the application of quantum computing. The goal is to suggest trends of how commercial quantum computing technology will evolve in the coming years and describe how this tremendous processing power could unleash exponential advances for humanity.
CMOS scaling and its power-performance-density-cost benefits have been the driving force behind the semiconductor industry. I will first review CMOS evolution over the last half century with a focus on recent progress. Due to CMOS leakage current constraints, supply voltage reduction has been stalled for a while which makes energy efficiency gain more difficult. Major efforts were undertaken to search for low voltage switches such as tunnel FET, negative capacitance FET and non CMOS logic but practical implementation appears far away. For high performance computing applications, cryogenic operation provides a straightforward way to reduce supply voltage due to steepened subthreshold slope and sharp turn-off behavior. In this talk, I will discuss various benefits of cryogenic operation, some of them were recognized long ago but have not been discussed in the context of advanced CMOS technologies and some of them are not obvious. I will also highlight the stringent requirement on the energy efficiency of refrigerators for overall power benefit.