Search results for "hardware"
showing 10 items of 1372 documents
Supervised learning of time-independent Hamiltonians for gate design
2018
We present a general framework to tackle the problem of finding time-independent dynamics generating target unitary evolutions. We show that this problem is equivalently stated as a set of conditions over the spectrum of the time-independent gate generator, thus transforming the task to an inverse eigenvalue problem. We illustrate our methodology by identifying suitable time-independent generators implementing Toffoli and Fredkin gates without the need for ancillae or effective evolutions. We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques. In turn, this allows us to solve problems that are not amenable, in general, to direct ana…
On the Influence of Technology on Learning Processes
2014
Probabilistic computations and frequency computations were invented for the same purpose, namely, to study possible advantages of technology involving random choices. Recently several authors have discovered close relationships of these generalizations of deterministic computations to computations taking advice. Various forms of computation taking advice were studied by Karp and Lipton [1], Damm and Holzer [2], and Freivalds [3]. In the present paper, we apply the nonconstructive, probabilistic, and frequency methods to an inductive inference paradigm originally due to Gold [4] and investigate their impact on the resulting learning models. Several trade-offs with respect to the resulting l…
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions
2018
Part 10: Learning - Intelligence; International audience; Although the field of Learning Automata (LA) has made significant progress in the last four decades, the LA-based methods to tackle problems involving environments with a large number of actions are, in reality, relatively unresolved. The extension of the traditional LA (fixed structure, variable structure, discretized, and pursuit) to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and consequently, most components of the vector will, after a relatively short time, have values that are smal…
Approximate supervised learning of quantum gates via ancillary qubits
2018
We present strategies for the training of a qubit network aimed at the ancilla-assisted synthesis of multi-qubit gates based on a set of restricted resources. By assuming the availability of only time-independent single and two-qubit interactions, we introduce and describe a supervised learning strategy implemented through momentum-stochastic gradient descent with automatic differentiation methods. We demonstrate the effectiveness of the scheme by discussing the implementation of non-trivial three qubit operations, including a Quantum Fourier Transform (QFT) and a half-adder gate.
Adjusting Software Revenue and Pricing Strategies in the Era of Cloud Computing
2016
Competitive forces shape software revenue and pricing models in cloud computing.Different revenue and pricing models lead to different competitive strategies.Software firms apply mixed revenue models, or a hybrid pricing mechanism.Software renting provides flexibility for software providers against competition.Software architecture may either limit possibilities for different revenue models. Recent research has recognized cloud computing as a new paradigm of servitization in which software products are offered based on service contracts. Thus, instead of selling software licenses, software vendors can rent software as a service to customers. However, it is still unclear how software provide…
Spatial joins
2019
The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. This paper reviews research and recent trends on spatial join evaluation. The complexity of different data types, the consideration of different join predicates, the use of modern commodity hardware, and support for parallel processing open the road to a number of interesting directions for future research, some of which we outline in the paper.
Notice of Violation of IEEE Publication Principles: Robust Delay-Dependent $H_{\infty}$ Control of Uncertain Time-Delay Systems With Mixed Neutral, D…
2011
The problem of robust mode-dependent delayed state feedback H∞ control is investigated for a class of uncertain time-delay systems with Markovian switching parameters and mixed discrete, neutral, and distributed delays. Based on the Lyapunov-Krasovskii functional theory, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities for the stochastic stability and stabilization of the considered system using some free matrices. The desired control is derived based on a convex optimization method such that the resulting closed-loop system is stochastically stable and satisfies a prescribed level of H∞ performance, simultaneously. Finally, two numer…
A simple timestamping data acquisition system for ToF-ERDA
2015
A new data acquisition system, ToF-DAQ, has been developed for a ToF-ERDA telescope and other ToF-E and ToF-ToF measurement systems. ToF-DAQ combines an analogue electronics front-end to asynchronous time stamped data acquisition by means of a FPGA device. Coincidences are sought solely in software based on the timestamps. Timestamping offers more options for data analysis as coincidence events can be built also in offline analysis. The system utilises a National Instruments R-series FPGA device and a Windows PC as a host computer. Both the FPGA code and the host software were developed using the National Instruments LabVIEW graphical programming environment. Up to eight NIM ADCs can be han…
Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity
2018
Nonparametric topic models based on hierarchical Dirichlet processes (HDPs) allow for the number of topics to be automatically discovered from the data. The computational complexity of standard Gibbs sampling techniques for model training is linear in the number of topics. Recently, it was reduced to be linear in the number of topics per word using a technique called alias sampling combined with Metropolis Hastings (MH) sampling. We propose a different proposal distribution for the MH step based on the observation that distributions on the upper hierarchy level change slower than the document-specific distributions at the lower level. This reduces the sampling complexity, making it linear i…
A Lightweight Network Discovery Algorithm for Resource-constrained IoT Devices
2019
Although quite simple, existing protocols for the IoT suffer from the inflexibility of centralized infrastructures and require several configuration stages. The implementation of these protocols is often prohibitive on resource-constrained devices. In this work, we propose a distributed lightweight implementation of network discovery for simple IoT devices. Our approach is based on the exchange of symbolic executable code among nodes. Based on this abstraction, we propose an algorithm that makes even IoT resource-constrained nodes able to construct the network topology graph incrementally and without any a priori information about device positioning and presence. The minimal set of executab…