15 research outputs found
A Systematic Literature Review of Quantum Computing for Routing Problems
Quantum Computing is drawing a significant attention from the current scientific community. The potential advantages offered by this revolutionary paradigm has led to an upsurge of scientific production in different fields such as economics, industry, or logistics. The main purpose of this paper is to collect, organize and systematically examine the literature published so far on the application of Quantum Computing to routing problems. To do this, we embrace the well-established procedure named as Systematic Literature Review. Specifically, we provide a unified, self-contained, and end-to-end review of 18 years of research (from 2004 to 2021) in the intersection of Quantum Computing and routing problems through the analysis of 53 different papers. Several interesting conclusions have been drawn from this analysis, which has been formulated to give a comprehensive summary of the current state of the art by providing answers related to the most recurrent type of study (practical or theoretical), preferred solving approaches (dedicated or hybrid), detected open challenges or most used Quantum Computing device, among others
Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants
The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding
from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the
European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy,
Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL
(KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)
Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers
Efficient packing of items into bins is a common daily task. Known as Bin
Packing Problem, it has been intensively studied in the field of artificial
intelligence, thanks to the wide interest from industry and logistics. Since
decades, many variants have been proposed, with the three-dimensional Bin
Packing Problem as the closest one to real-world use cases. We introduce a
hybrid quantum-classical framework for solving real-world three-dimensional Bin
Packing Problems (Q4RealBPP), considering different realistic characteristics,
such as: i) package and bin dimensions, ii) overweight restrictions, iii)
affinities among item categories and iv) preferences for item ordering.
Q4RealBPP permits the solving of real-world oriented instances of 3dBPP,
contemplating restrictions well appreciated by industrial and logistics
sectors.Comment: 9 pages, 24 figure
Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem
The Bin Packing Problem (BPP) stands out as a paradigmatic combinatorial
optimization problem in logistics. Quantum and hybrid quantum-classical
algorithms are expected to show an advantage over their classical counterparts
in obtaining approximate solutions for optimization problems. We have recently
proposed a hybrid approach to the one dimensional BPP in which a quantum
annealing subroutine is employed to sample feasible solutions for single
containers. From this reduced search space, a classical optimization subroutine
can find the solution to the problem. With the aim of going a step further in
the evaluation of our subroutine, in this paper we compare the performance of
our procedure with other classical approaches. Concretely we test a random
sampling and a random-walk-based heuristic. Employing a benchmark comprising 18
instances, we show that the quantum approach lacks the stagnation behaviour
that slows down the classical algorithms. Based on this, we conclude that the
quantum strategy can be employed jointly with the random walk to obtain a full
sample of feasible solutions in fewer iterations. This work improves our
intuition about the benefits of employing the scarce quantum resources to
improve the results of a diminishingly efficient classical strategy.Comment: 8 pages, 2 figures, submitted to the IEEE Symposium Series On
Computational Intelligence 202
Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States
Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning, and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder operators, i.e., creation and annihilation operators. The high fidelity of the digitally generated coherent states is verified compared with the Poissonian distribution in Fock space. Secondly, by using Variational Quantum Algorithms, we choose different ansatzes to generate coherent states. The quantum resources—such as numbers of quantum gates, layers and iterations—are analyzed for quantum circuit learning. The simulation results show that quantum circuit learning can provide high fidelity on learning coherent states by choosing appropriate ansatzes.This research is funded by the QUANTEK project (ELKARTEK program from the Basque Government, expedient No. KK-2021/00070), the project “BRTA QUANTUM: Hacia una especialización armonizada en tecnologías cuánticas en BRTA” (expedient No. KK-2022/00041)
Digital-analog quantum computation with arbitrary two-body Hamiltonians
Digital-analog quantum computing is a computational paradigm which employs an analog Hamiltonian resource together with single-qubit gates to reach universality. Here, we design a new scheme which employs an arbitrary two-body source Hamiltonian, extending the experimental applicability of this computational paradigm to most quantum platforms. We show that the simulation of an arbitrary two-body target Hamiltonian of n qubits requires O(n2) analog blocks with guaranteed positive times, providing a polynomial advantage compared to the previous scheme. Additionally, we propose a classical strategy which combines a Bayesian optimization with a gradient descent method, improving the performance by ∼55% for small systems measured in the Frobenius norm.The Spanish Grants No. PID2019-104002GB-C21, No. PID2019-104002GB-C22, No. PID2022-136228NB-C21, and No. PID2022-136228NB-C22 funded by Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación MCIN/AEI/10.13039/501100011033, FEDER “A Way of Making Europe,” Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía, European Regional Development Fund (ERDF) under Project No. US-1380840, Grant Groups FQM-160 and FQM-177, and the project PAIDI 2020 with Reference No. P20_01247 and No. P20_00617 funded by the Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía (Spain).ERDF/MINECO Project No. UNHU-15CE-2848EU FET Open project EPIQUS (899368)HORIZON-CL4-2022-QUANTUM01-SGA Project No. 101113946 OpenSuperQPlus100 of the EU Flagship on Quantum TechnologiesSpanish Ramón y Cajal Grant No. RYC-2020-030503-I, Grant No. PID2021-125823NA-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”“ERDF Invest in your Future,” Basque Government, through Grant No. IT1470-22IKUR Strategy under the collaboration agreement between Ikerbasque Foundation and BCAM on behalf of the Department of Education of the Basque GovernmentUPV/EHU and TECNALIA 2021 PIF contract callMinistry for Digital Transformation and of Civil Service of the Spanish Government through the QUANTUM ENIA project call–Quantum Spain projectThe European Union through the Recovery, Transformation and Resilience Plan–NextGenerationEU within the framework of the “Digital Spain 2026 Agenda
An active adaptation strategy for streaming time series classification based on elastic similarity measures
In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation
strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference
patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern end detection model along with dynamic time warping-based nearest neighbor classifiers over a benchmark of ten time series classification problems. The obtained results present exciting insights into the detection accuracy and latency performance of the proposed strategy.Funding was provided by Eusko Jaurlaritza (KK-2020/00049, MATHMODE (IT1294-19), BERC 2022-2025),
Ministerio de Economı´a y Competitividad (Severo Ochoa SEV-2017-0718,TIN2016-78365-R),
Agencia Estatal de Investigacio´n (TIN2017-82626-R