1,721,158 research outputs found
Quantum machine intelligence: Launching the first journal in the area of quantum artificial intelligence
No full textQuantum Computing Meets Artificial Intelligence
No full textThe world of computing is going to shift towards new paradigms able to provide better performance in solving hard problems than classical computation. In this scenario, quantum computing is assuming a key role thanks to the recent technological enhancements achieved by several big companies in developing computational devices based on fundamental principles of quantum mechanics: superposition, entanglement and interference. These computers will be able to yield performance never seen before in several application domains, and the area of artificial intelligence may be the one most affected by this revolution. Indeed, on the one hand, the intrinsic parallelism provided by quantum computers could support the design of efficient algorithms for artificial intelligence such as, for example, the training algorithms of machine learning models, and bio-inspired optimization algorithms; on the other hand, artificial intelligence techniques could be used to reduce the effect of quantum decoherence in quantum computing, and make this type of computation more reliable. This position paper aims at introducing the readers with this new research area and pave the way towards the design of innovative computing infrastructure where both quantum computing and artificial intelligence take a key role in overcoming the performance of conventional approaches
Improving Quantum Genetic Algorithms through Recursive Search Space Exploration
No full textRecently, 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 to converge towards sub-optimal solutions of hard problems. However, the performance of these quantum evolutionary approaches is limited by the number of qubits available on current quantum devices. This limitation is more noticeable in the case of continuous optimization problems, where the search space is potentially infinite. This paper presents a recursive algorithmic structure that embodies a quantum evolutionary algorithm to overcome the limitations mentioned above. The result is an innovative and efficient approach in the context of quantum evolutionary optimization
TSSweb: A web tool for training set selection
No full textSupervised 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 classification or regression tasks. In literature, several training set selection techniques have been proposed, but, to the best of our knowledge, few software tools implement this procedure. Moreover, all of them require programming capabilities or software package installation what makes their use difficult for people without specific computer skills. This paper proposes the first web-based tool, named TSSweb, for performing an accurate selection of the training instances. Thanks to its web nature, TSSweb enables all researchers, coming from several and heterogeneous scientific backgrounds, to reduce own datasets so as to improve their analysis and reduce the execution time of their supervised learning models. As shown in the experimental session, TSSweb produces reduced datasets with a good quality as well as being user-friendly
An IEEE Std 1855 Driver for Synthetizing Quantum Fuzzy Inference Engines
No full textOver 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 is large. In 2016, the IEEE Std 1855 was released to address the first issue by introducing an XML-based language capable of modeling fuzzy systems in an abstract way independent from hardware. As for the second issue, recently, a quantum implementation of a fuzzy inference engine, denoted as QFIE, has been proposed to potentially provide an exponential computational advantage in the execution of fuzzy rules. This paper aims at integrating IEEE Std 1855 and QFIE to provide the fuzzy community with controllers transparent with respect to hardware and efficient in terms of fuzzy rule execution. This integration is achieved by introducing an IEEE Std 1855 driver capable of converting transparent fuzzy systems in controllers executable on quantum computers. As shown in a case study, this driver enables quantum implementation of a fuzzy system without knowledge of quantum hardware and technologies
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
Error Mitigation in Quantum Measurement through Fuzzy C-Means Clustering
No full textRecently, 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 mitigation approaches aimed at increasing reliability in quantum computation and making this paradigm really useful and productive in real world applications. In this paper, a fuzzy method, such as Fuzzy C-Means (FCM) clustering, has been used, for the very first time, to support the identification of matrices for error mitigation in quantum measurement. As shown in experiments, mitigation matrices identified with the support of FCM are able to strongly reduce error in computation when compared to mitigation matrices conventionally identified, like those used by IBM in its quantum library named Qiskit
A Competent Memetic Algorithm for Error Mitigation in Quantum Measurement
No full textRecently, 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 quantum measurement. Currently, the most popular mitigation method for quantum measurement error consists of computing a so-called mitigation matrix to be applied on the results outputted by a quantum processor to transform them and make them closer to the ideal ones. In this paper, a new measurement error mitigation method based on a competent memetic algorithm is proposed to generate an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method shows better results when compared with the conventional method and the state of the art among the evolutionary approaches
Thematic issue on hybrid nature-inspired algorithms: Concepts, analysis and applications
No full textMIDA: A web tool for missing data imputation based on a boosted and incremental learning algorithm
No full textOne 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 learning users in "filling-in"their data sets before inputting them to training algorithms. This paper bridges this gap by introducing a web-based tool for MIssing DAta imputation (MIDA) based on a novel supervised learning method, namely Generalized Boosted Incremental Non Parametric Imputation algorithm (G-BINPI), able to address the missing values issue in scenarios where a "missing at random"assumption occurs. The proposed approach enables machine learning users to remotely imputing their data sets by means of an intuitive graphical user interface. As highlighted in the experimental section, the proposed approach yields better performance than conventional approaches for missing data imputation on different benchmark data sets
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