Autilia Vitiello - IEEE Xplore Author Profile

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The signal settings design in urban networks requires the solution of complex optimization problems. In particular, the urban networks control when connected and electric vehicles are present has garnered significant i nterest d ue t o its notable advantages over networks with human-driven vehicles. These advantages include the potential for significant reductions in travel time, waiting time, ene...Show More
Recently, quantum computing has emerged as a new paradigm that promises to improve artificial intelligence techniques. One of the research fields that is certainly benefiting from this new computational paradigm is evolutionary optimization. In literature, efforts have been already made to run evolutionary algorithms on quantum computers using quantum effects such as superposition and entanglement...Show More
Variational Quantum Classifiers (VQCs) stand out as one of the most popular classification approaches within the Quantum Machine Learning (QML) community. They serve as an accessible entry point for newcomers to QML world, particularly those with a background in classical machine learning, due to their workflow similarity to conventional neural networks. In essence, VQCs boast a layered structure ...Show More
This paper addresses the escalating complexity of smart city environments by proposing the use of Quantum Fuzzy Inference Engine (QFIE) for enhanced control. Smart cities play a pivotal role in optimizing resource utilization and improving overall urban living. However, their intricate and interconnected nature demands advanced control algorithms. QFIE emerges as a promising solution due to its co...Show More
The Quantum Approximate Optimization Algorithm (QAOA) has become one of the most widely used components in the development of modern quantum applications. It works on the paradigm of quantum variational circuits, where a quantum circuit is trained - by repeatedly adjusting circuit parameters - to adequately solve a combinatorial optimization problem. This training process, based on classical optim...Show More
Over years, Fuzzy Logic established as a powerful tool for control systems as shown by the several applications both in the area of pure engineering and social, economical and political science. However, in spite of the success of fuzzy control systems, their design is originally affected by mainly two issues: the dependence on hardware and an expensive inference when the number of fuzzy variables...Show More
Recently, a quantum algorithm called Quantum Fuzzy Inference Engine (QFIE) has been introduced with the main goal of providing exponential speedup in the execution of a Mamdani fuzzy inference engine. This quantum algorithm achieves this result by modeling a fuzzy rule base with a quantum oracle, a black box that is widely used to estimate functions using quantum mechanical principles. Although QF...Show More
Recently, a significant interest is arising in developing techniques capable of correcting errors in noisy quantum computations without using additional quantum resources. These techniques, known as mitigation methods, are aimed at post-processing the quantum outcome without working at quantum hardware level. Among the most error-prone operations on the current quantum devices, there is surely the...Show More
The nuclear shell model is one of the most adopted many-body methods for the description of atomic nuclei whose main ingredient is the effective Hamiltonian. One of the approaches widely used to derive it is of phenomenological type, where its matrix elements are considered parameters to be fixed to reproduce the experimental data. However, the number of parameters as well as the number of experim...Show More
Quantum machine learning is a research area that explores the interplay of ideas from quantum computing and machine learning to speed up the time it takes to train or evaluate a machine learning model. Variational quantum classifiers are among the most widely used quantum models for supervised machine learning and base their operation on learning free parameters through conventional optimization a...Show More
The regulation of traffic lights in a signalised urban network requires optimizing objective functions that represent performance indicators of one or more intersections (such as delay or queue length). In this scenario, evolutionary algorithms are adopted to find suitable approximate solutions, in cases when no deterministic algorithm for finding the exact solution is known. This paper attempts t...Show More
Quantum computers can be a revolutionary tool to implement inference engines for fuzzy rule-based systems. In fact, the use of quantum mechanical principles can enable parallel execution of fuzzy rules and allow them to be used efficiently in complex contexts such as distributed and big data environments. This article introduces the very first quantum-based fuzzy inference engine that is capable o...Show More
The Support Vector Machine (SVM) is a well-known supervised machine learning approach aimed at facing classification problems. Thanks to the exploitation of kernel functions, SVM succeeds to address also complex classification tasks involving non-linearly separable data. However, there are limitations to its success when the feature space becomes large, and the kernel functions become computationa...Show More
Genetic Algorithms (GAs) are optimization methods that search near-optimal solutions by applying well-known operations such as selection, crossover and mutation. In particular, crossover and mutation are aimed at creating new solutions from selected parents with the goal of discovering better and better solutions in the search space. In literature, several approaches have been defined to create ne...Show More
Recently, Variational Quantum Circuits (VQCs) are attracting considerable attention among quantum algorithms thanks to their robustness to the noise characterizing the current quantum devices. In detail, VQCs involve parameterized quan-tum circuits to be trained by means of a classical optimizer that makes queries to the quantum device. VQCs play a key role in several applications including quantu...Show More
Bloodstain Pattern Analysis (BPA) is used by forensic officers to analyse bloodstains left at crime scenes. As a consequence, it has a crucial role in the investigations of bloody crimes. Currently, BPA activities are performed manually by leading to a slow and potentially imprecise analysis of the crime scenes. In order to overcome these issues, recently, some software tools have been developed i...Show More
Quantum computing is a fascinating research area which promises a revolution in computing performance. Since the launch of the IBM Quantum Experience project in 2016, the research activities in this area are strongly increased. This project provides the public access to quantum processors composed of superconducting physical computing elements known as qubits. Unfortunately, qubits are sensitive t...Show More
Noisy Intermediate Scale Quantum (NISQ) devices are expected to demonstrate the real potential of quantum computing in solving hard problems. However, quantum noise that characterizes this kind of devices still remains an obstacle for their practical exploitation in real world scenarios. As a consequence, there is a strong emergence for error correction techniques aimed at making NISQ devices stab...Show More
Recently, Quantum Computing is entered in the so-called Noisy Intermediate-Scale Quantum (NISQ) era, where devices characterized by a few number of qubits are potentially able to overcome classical computers in performing specific tasks. However, noise in quantum operators still limits the size of quantum circuits that can be run in a reliable way. Consequently, there is a strong need for error mi...Show More
This paper introduces a study on fuzzy-based approaches aimed at addressing a crucial task in quantum computation: the evaluation of the similarity between quantum states. A quantum state is a mathematical entity that provides a probability distribution for the outcomes of each possible measurement of a quantum algorithm. Because quantum computers are still characterized by high noise in computati...Show More
Electroencephalography (EEG) headsets are wearable computing devices capable of recording electrical activity of the brain. These devices play a key role in the Brain-Computer Interfaces (BCIs) systems, i.e., systems capable of acquiring, processing and classifying EEG signals in order to control external devices such as wireless prosthetics. In spite of their crucial role, the current EEG headset...Show More
One of the main issues in machine learning is related to the quality of data used to efficiently train statistical models for classification/regression tasks. Among these issues, the presence of missing values in data sets is particularly prone in affecting the accuracy performance of learning methods. As a consequence there is a strong emergence of software tools aimed at supporting machine learn...Show More
Supervised learning methods aimed at performing precise predictions by learning from labeled training data. Unfortunately, training data can contain noisy or wrong information, specially when they come from real-world applications. In this scenario, applying a so-called training set selection procedure on data can lead to improve the performance of the supervised learning methods used for classifi...Show More
Twitter is currently one of the most popular platforms for disseminating information about events happening around the world. Especially but not only for emergency events, it is crucial to know when and where the events are taking place. Unfortunately, identifying the geo-location of an event discussed in Twitter is a very challenging task mainly due to the brevity of the messages (i.e., tweets) a...Show More
Steady-State Visual Evoked Potentials (SSVEPs) are electroencephalography (EEG) signals which, recently, have attracted a notable interest in the field of Brain Computer Interfaces (BCIs) due to their little training requirement. Similar to other EEG signals, SSVEPs are captured by means of EEG devices characterized by multiple wet electrodes. Unfortunately, these EEG devices are very uncomfortabl...Show More