1,721,274 research outputs found
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
Response to the Letter to the Editor regarding the Revisional Surgery After One Anastomosis/Minigastric Bypass: an Italian Multi-institutional Survey
No full textTSSweb: 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
The Effect of Strain-Hardening on a 8% Carbon Steel: an Industrial Drawing Process
No full textSalerno, Ital
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
The Effect of Strain-Hardening on a 8% Carbon Steel: an Industrial Drawing Process
No full textSalerno, Ital
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
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