Hybrid Quantum Computing - Tabu Search Algorithm for Partitioning Problems: Preliminary Study on the Traveling Salesman Problem

Osaba, Eneko; Villar-Rodríguez, Esther; Oregi, Izaskun; Moreno-Fernandez-de-Leceta, Aitor

doi:10.1109/cec45853.2021.9504923

Cited by 25 publications

(11 citation statements)

References 32 publications

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“…The performance mechanisms in resolving important industrial problems are highlighted by the distinctions between classical computing and QC. The QC mechanics use fundamental quantum properties like superposition, entanglement, and the measurement paradox to find the best answers to challenging issues [43], [44].…”

Section: Quantum Computingmentioning

confidence: 99%

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

et al. 2023

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Prognostics and Health Management (PHM) concerns predicting machines' behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines' state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models' speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.

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Section: Quantum Computingmentioning

confidence: 99%

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

et al. 2023

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“…However, researchers are forced to devise problems tailored to hardware capacity, which often fall short from advanced or realistic formulations [61], [63], [70]. Furthermore, the necessity of taking a step forward and advance in the formulation of more realistic problems is a widely accepted open challenge [31], [49], [65], [67]. Within this frame of reference, the following research lines have been identified by the community as future work: i) the efficient modeling of advanced routing problems, focusing on building mathematical formulations which optimize the problem embedding [60], [65], ii) the solving of real-world problems oriented to realistic applications (such as traffic optimization or detecting routing anomalies), even if the size of these problems is restricted [49], [68], [69] and iii) the development of hybrid solvers using classical optimization mechanisms to tackle complex problems [33], [71].…”

Section: • Researchers With Background In Traditional Artificialmentioning

confidence: 99%

A Systematic Literature Review of Quantum Computing for Routing Problems

2022

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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.

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“…Besides, as can read in [16], current D'WAVE computers cannot solve TSP instances with a size greater than 9 nodes [20], because of the current qubit amount limitation. For this reason, in order to cope with Burma'12, Burma'13 and Burma'14 instances, the quantum-classical hybrid method described in [21] has been adopted. This procedure relies on the partition of the whole instance into similar-sized sub-problems to be solved independently by the quantum computer.…”

Section: Testing the Efficiency Of The H-qubo On Additional Tsp Insta...mentioning

confidence: 99%

Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations

Villar-Rodríguez¹,

Osaba²,

Oregi³

2022

Preprint

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Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development. Indeed, current commercial quantum computers suffer from some critical restraints, such as noisy processes and a limited amount of qubits, among others, that affect the performance of quantum algorithms. Despite these limitations, researchers have devoted much effort to propose different frameworks for efficiently using these Noisy Intermediate-Scale Quantum (NISQ) devices. One of these procedures is D'WAVE Systems' quantum-annealer, which can be use to solve optimization problems by translating them into an energy minimization problem. In this context, this work is focused on providing useful insights and information into the behaviour of the quantum-annealer when addressing real-world combinatorial optimization problems. Our main motivation with this study is to open some quantum computing frontiers to non-expert stakeholders. To this end, we perform an extensive experimentation, in the form of a parameter sensitive analysis. This experimentation has been conducted using the Traveling Salesman Problem as benchmarking problem, and adopting two QUBOs: state-of-the-art and a heuristically generated. Our analysis has been performed on a single 7-noded instance, and it is based on more than 200 different parameter configurations, comprising more than 3700 unitary runs and 7 million of quantum reads. Thanks to this study, findings related to the energy distribution and most appropriate parameter settings have been obtained. Finally, an additional study has been performed, aiming to determine the efficiency of the heuristically built QUBO in further TSP instances.

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Hybrid Quantum Computing - Tabu Search Algorithm for Partitioning Problems: Preliminary Study on the Traveling Salesman Problem

Cited by 25 publications

References 32 publications

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

A Systematic Literature Review of Quantum Computing for Routing Problems

Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations

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