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Starting from the idea of Quantum Computing which is a concept that dates back to 80s, we come to the present day where we can perform calculations on real quantum computers. This sudden development of technology opens up new scenarios that quickly lead to the desire and the real possibility of integrating this technology into current software architectures. The usage of frameworks that allow computation to be performed directly on quantum hardware poses a series of challenges. This document describes a an architectural framework that addresses the problems of integrating an API exposed Quantum provider in an existing Enterprise architecture and it provides a minimum viable product (MVP) solution that really merges classical quantum computers on a basic scenario with reusable code on GitHub repository. The solution leverages a web-based frontend where user can build and select applications/use cases and simply execute it without any further complication. Every triggered run leverages on multiple backend options, that include a scheduler managing the queuing mechanism to correctly schedule jobs and final results retrieval. The proposed solution uses the up-to-date cloud native technologies (e.g. Cloud Functions, Containers, Microservices) and serves as a general framework to develop multiple applications on the same infrastructure.
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Operating a modern power grid reliably in case of SCADA/EMS failure or amid difficult times like COVID-19 pandemic is a challenging task for grid operators. In [11], a PMU-based emergency generation dispatch scheme has been proposed to help the system operators with the supply and demand balancing; however, its realization highly relies on the control center infrastructure for computing and communication. This work, rather than using the on-premises server and dispatch communication system, proposes and implements a cloud-centric serverless architecture to ensure the operation continuity regardless of local infrastructures availability and accessibility. Through its prototype implementation and evaluation at ISO New England, the solution has demonstrated two major advantages. Firstly, the cloud infrastructure is independent and fault-tolerant, providing grid monitoring and control capability even when EMS loses the corresponding functionality or when operators need to work remotely away from the control center. Secondly, the overall design is event-driven using serverless cloud services in response to the SCADA/EMS failure event. Thanks to serverless, the burden of the server provisioning and maintenance can be avoided from the user side. The cost of using public cloud services for this solution is extremely low since it is architected and implemented based on the event-driven Function-as-a-Service (FaaS) model. This work also develops a comprehensive cyber security mechanism to comply with critical infrastructure requirements for the power grid, which can serve as an exemplary framework for other grid operators to secure their cloud services.
Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low-power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of information per element and the low energy required to write distinct states. In this paper, we first review the neuro- and neuromorphic-computing approaches that can best exploit the properties of memristor and-scale devices, and then propose a novel hybrid memristor-CMOS neuromorphic circuit which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses. We point out the differences between the use of memristors in conventional neuro-computing architectures and the hybrid memristor-CMOS circuit proposed, and argue how this circuit represents an ideal building block for implementing brain-inspired probabilistic computing paradigms that are robust to variability and fault-tolerant by design.
Serverless computing has grown in popularity in recent years, with an increasing number of applications being built on Functions-as-a-Service (FaaS) platforms. By default, FaaS platforms support retry-based fault tolerance, but this is insufficient for programs that modify shared state, as they can unwittingly persist partial sets of updates in case of failures. To address this challenge, we would like atomic visibility of the updates made by a FaaS application. In this paper, we present AFT, an atomic fault tolerance shim for serverless applications. AFT interposes between a commodity FaaS platform and storage engine and ensures atomic visibility of updates by enforcing the read atomic isolation guarantee. AFT supports new protocols to guarantee read atomic isolation in the serverless setting. We demonstrate that aft introduces minimal overhead relative to existing storage engines and scales smoothly to thousands of requests per second, while preventing a significant number of consistency anomalies.
We present an algorithm for compiling arbitrary unitaries into a sequence of gates native to a quantum processor. As accurate CNOT gates are hard for the foreseeable Noisy- Intermediate-Scale Quantum devices era, our A* inspired algorithm attempts to minimize their count, while accounting for connectivity. We discuss the search strategy together with metrics to expand the solution frontier. For a workload of circuits with complexity appropriate for the NISQ era, we produce solutions well within the best upper bounds published in literature and match or exceed hand tuned implementations, as well as other existing synthesis alternatives. In particular, when comparing against state-of-the-art available synthesis packages we show 2.4x average (up to 5.3x) reduction in CNOT count. We also show how to re-target the algorithm for a different chip topology and native gate set, while obtaining similar quality results. We believe that empirical tools like ours can facilitate algorithmic exploration, gate set discovery for quantum processor designers, as well as providing useful optimization blocks within the quantum compilation tool-chain.
Neuromorphic computing takes inspiration from the brain to create energy efficient hardware for information processing, capable of highly sophisticated tasks. In this article, we make the case that building this new hardware necessitates reinventing electronics. We show that research in physics and material science will be key to create artificial nano-neurons and synapses, to connect them together in huge numbers, to organize them in complex systems, and to compute with them efficiently. We describe how some researchers choose to take inspiration from artificial intelligence to move forward in this direction, whereas others prefer taking inspiration from neuroscience, and we highlight recent striking results obtained with these two approaches. Finally, we discuss the challenges and perspectives in neuromorphic physics, which include developing the algorithms and the hardware hand in hand, making significant advances with small toy systems, as well as building large scale networks.
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