1,720,972 research outputs found
Design of an LSTM cell on a quantum hardware
No full textThe present paper proposes a methodology to implement a Long Short-Term Memory cell in the quantum framework, where inference is computed by replicating the internal structure of the cell using quantum circuits. A suitable encoding is proposed and the design of each quantum operation is detailed. A complexity analysis of the circuit is hence conducted and finally, the quantum architecture is experimentally validated both in an IBM Q simulator and with a numerical simulation on a classical device. The proposed approach leads the way for a completely quantum implementation of a Long Short-Term Memory network
A General Approach to Dropout in Quantum Neural Networks
Full text linkIn classical Machine Learning, "overfitting" is the phenomenon occurring when
a given model learns the training data excessively well, and it thus performs
poorly on unseen data. A commonly employed technique in Machine Learning is the
so called "dropout", which prevents computational units from becoming too
specialized, hence reducing the risk of overfitting. With the advent of Quantum
Neural Networks as learning models, overfitting might soon become an issue,
owing to the increasing depth of quantum circuits as well as multiple embedding
of classical features, which are employed to give the computational
nonlinearity. Here we present a generalized approach to apply the dropout
technique in Quantum Neural Network models, defining and analysing different
quantum dropout strategies to avoid overfitting and achieve a high level of
generalization. Our study allows to envision the power of quantum dropout in
enabling generalization, providing useful guidelines on determining the maximal
dropout probability for a given model, based on overparametrization theory. It
also highlights how quantum dropout does not impact the features of the Quantum
Neural Networks model, such as expressibility and entanglement. All these
conclusions are supported by extensive numerical simulations, and may pave the
way to efficiently employing deep Quantum Machine Learning models based on
state-of-the-art Quantum Neural Networks
All-optical and logic gate based on semiconductor optical amplifiers for implementing deep recurrent neural networks
No full textThe development of optical logic gates is a key
factor for enabling next generation of computations in the
context of Deep Learning and Quantum Computing. In this
work, we introduce a scheme for the implementation of an alloptical
AND logic gate, which makes use of semiconductor
optical amplifiers (SOA) in cross-phase modulation
configuration combined with an all-optical XOR gate. Our
analysis includes a realistic model of SOA, which considers
also the phase and the delay of the signals. We prove that our
scheme allows us to obtain almost ideal transitions in 3 out of
the 4 situations in a 2-bit logic, with any SOA. The remaining
combination shows a reduction of extinction ratio, which can
still be improved with better tuning of SOAs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Machine learning forecast of surface solar irradiance from meteo satellite data
Full text linkIn order to facilitate the shift towards sustainable practices and to support the transition to renewable energy,
there is a requirement for faster and more accurate predictions of solar irradiance. Surface solar energy
predictions are essential for the establishment of solar farms and the enhancement of energy grid management.
This paper presents a novel approach to forecast surface solar irradiance up to 24 h in advance, utilizing various
machine and deep learning architectures. Our proposed Machine Learning (ML) models include both pointbased
(1D) and grid-based (3D) solutions, offering a comprehensive exploration of different methodologies.
Our forecasts leverage two days of input data to predict the next day of solar exposure at country scale.
To assess the models’ performance, extensive testing is conducted across three distinct geographical areas
of interest: Austria (where models were trained and validated), Switzerland and Italy (where we tested our
models under a transfer learning regime), and sensitivity to the season is also discussed. The study incorporates
comparisons with established benchmarks, including state-of-the-art numerical weather predictions, as well as
fundamental predictors such as climatology and persistence. Our findings reveal that the ML-based methods
clearly outperform traditional forecasting techniques, demonstrating high accuracy and reliability in predicting
surface solar irradiance. This research not only contributes to the advancement of solar energy forecasting
but also highlights the effectiveness of machine learning and deep learning models in being competitive to
conventional methods for short-term solar irradiance predictions
A Review on Quantum Approximate Optimization Algorithm and its Variants
No full textThe Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization
problems that are classically intractable. This comprehensive review offers an
overview of the current state of QAOA, encompassing its performance analysis in
diverse scenarios, its applicability across various problem instances, and
considerations of hardware-specific challenges such as error susceptibility and
noise resilience. Additionally, we conduct a comparative study of selected QAOA
extensions and variants, while exploring future prospects and directions for
the algorithm. We aim to provide insights into key questions about the
algorithm, such as whether it can outperform classical algorithms and under
what circumstances it should be used. Towards this goal, we offer specific
practical points in a form of a short guide. Keywords: Quantum Approximate
Optimization Algorithm (QAOA), Variational Quantum Algorithms (VQAs), Quantum
Optimization, Combinatorial Optimization Problems, NISQ AlgorithmsComment: 67 pages, 9 figures, 9 tables; version 2 -- added more discussions
and practical guide
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Time series prediction with autoencoding LSTM networks
No full textNowadays, solving prediction problems in green computing is an open and challenging task, for which solutions based on deep learning are studied. In this work, we present a forecasting algorithm based on Long Short-Term Memory networks applied to renewable energy sources time series prediction. We make use of an encoder-decoder structure to extract useful representative sequence data, employing a stacked LSTM architecture for data embedding and successive prediction. By comparing the performance of the proposed forecasting scheme with a classical twolayer LSTM structure, we are able to asses the performance of the former as a robust tool for solving prediction problems in the green computing framework
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