1,721,023 research outputs found
A double network approach to international production
No full texthttp://ideas.repec.org/s/urb/wpaper.htm
Deep neural networks for quantum circuit mapping
No full textQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy
Implementing defuzzification operators on quantum annealers
Full text linkDue to the built-in parallelism of quantum computing, there is an unexplored potential for some complex fuzzy logic computations to take the advantage of the future quantum computers. Recently, it has been introduced a novel representation of fuzzy sets and implementations of some basic fuzzy logic operators (union, intersection, alpha-cut and maximum) based on solving a Quadratic Unconstrained Binary Optimization (QUBO) problems, on a type of quantum computers known as quantum annealers. In this paper, this work is extended by presenting an implementation of centroid defuzzification on quantum annealer machines, based on binary quadratic model (BQM) but this time using Ising model. Having the basic operations and defuzzification implemented on quantum computers, this paper paves the way towards the implementation of a whole fuzzy inference engine on enhanced devices, such as quantum annealers
Atomic decomposition for preduals of some Banach spaces
No full textGiven a Banach space E with a supremum type norm induced by a sequence L = (Lj) of linear forms Lj : X → R on the Banach space X, we prove that if the unit ball BX is σ(X, L)compact then E has a predual E? with an atomic decomposition. We extend results from [7] where X is assumed a reflexive Banach space
Fuzzy logic on quantum annealers
Full text linkQuantum computation is going to revolutionize the world of computing by enabling the design of massive parallel algorithms that solve hard problems in an efficient way, thanks to the exploitation of quantum mechanics effects, such as superposition, entanglement and interference. These computational improvements could strongly influence the way how fuzzy systems are designed and used in contexts, such as big data, where computational efficiency represents a non-negligible constraint to be taken into account. In order to pave the way towards this innovative scenario, this paper introduces a novel representation of fuzzy sets and operators based on Quadratic Unconstrained Binary Optimization (QUBO) problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers
A BMO-Type Characterization of Higher Order Sobolev Spaces
Full text linkWe obtain a new characterization of the higher Sobolev space Wm,p(Rn), m ∈ N and
p ∈ (1,+∞) and of the space BV m, the space of functions of higher order bounded variation.
The characterizations are in term of BMO-type seminorms. The results unify and
substantially extend previous results in Fusco et al. (ESAIM Control Optim. Calc Var.,
24(2), 835–847 2018) and Farroni et al. (J. Funct. Anal., 278(9), 108451 2020)
Quantum genetic selection: Using a quantum computer to select individuals in genetic algorithms
No full textThis paper proposes an innovative selection operator based on concepts from quantum mechanics. In particular, a quantum state is used to embody genetic individuals and their fitness values, and a quantum algorithm known as amplitude amplification is used to modify this state in order to create a quantum superposition in which the probability to measure an individual is related to its quality. The main peculiarity of this approach is related to the non-zero probability of selecting individuals do not belonging to the current population so as to create new genetic material and reduce the likelihood that genetic evolution will converge to local optima. The suitability of the proposed operator has been proved by an experimental session where a comparison with well-known selection methods has been carried out on a set of benchmark problems
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