The probabilistic nature of quantum particles, state space, and the superposition principle are among the important concepts in quantum mechanics. A framework was previously developed by the authors that allowed to ta...
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The probabilistic nature of quantum particles, state space, and the superposition principle are among the important concepts in quantum mechanics. A framework was previously developed by the authors that allowed to take advantage of these quantum aspects in the field of image processing. This was done by modeling each image's pixel by a two-state quantum system which allowed efficient single-object segmentation. However, the extension of the framework to multi-object segmentation would be highly complex and computationally expensive. In this paper, we propose a classical image segmentation algorithm inspired by the continuous-variable quantum theory that overcomes the challenges in extending the framework to multi-object segmentation. By associating each pixel with a quantum harmonic oscillator, the space of coherent states becomes continuous. Thus, each pixel can evolve from an initial state to any of the continuous coherent states under the influence of an external resonant force. The Hamiltonian operator is designed to account for this force and is derived from the features extracted at the pixel. Therefore, the system evolves from an initial ground state to a final coherent state depending on the image features. Finally by calculating the fidelity between the final state and a set of reference states representing the objects in the image, the state with the highest fidelity is selected. The collective states of all pixels produce the final segmentation. The proposed method is tested on a database of synthetic and natural images, and compared with other methods. Average sensitivity and specificity of 97.86% and 99.61% were obtained respectively indicating the high segmentation accuracy of the algorithm.
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate ...
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ISBN:
(纸本)9781450392686
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with non-elitist selection outperform benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality.
Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between ...
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ISBN:
(纸本)9783031059360;9783031059353
Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called quantum Entanglement inspired the Classification Algorithm (QECA). Particularly, we use the joint probability derived from entangled systems to model correlation between features and categories, that is, quantum Correlation (QC), and leverage it to develop a novel QC-induced Multi-layer Perceptron framework for classification tasks. Experimental results on four datasets from diverse domains show that QECA is significantly better than the baseline methods, which demonstrates that QC revealed by entangled systems can improve the classification performance of traditional algorithms.
Massive Multiple-Input Multiple-Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). However, having a massive numbe...
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ISBN:
(纸本)9781538617342
Massive Multiple-Input Multiple-Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). However, having a massive number of radio-frequency (RF) chains at the BS can be costly and energy inefficient. One way to achieve the diversity gain of massive MIMO systems is to employ a massive number of antennas with limited number of RF chains. Thus, antenna selection techniques can be applied to reduce the system complexity and hardware cost. In this paper, a quantum-inspired Tabu Search (QTS) algorithm is applied to antenna selection in Massive MIMO systems and compared with two well known algorithms;namely, a Classical Tabu Search (CTS) algorithm and a Genetic Algorithm (GA). The QTS algorithm has a great advantage over CTS, since it only requires finding the optimum rotation angle to evolve the system towards a better solution. In contrast, in CTS, the dimensions of the tabu matrix are dynamic and need to be optimized. Moreover, to achieve maximum performance, these dimensions need to be reconfigured when changing the number of antennas or the number of iterations, while no such a problem occurs in the QTS. The QTS algorithm also shows better results in terms of the system capacity compared to CTS and GA. Furthermore, the classical and quantuminspired TS algorithms require much lower complexity than the GA.
We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang's breakthrough quantum-inspired algorithm for recomme...
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ISBN:
(纸本)9781450369794
We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang's breakthrough quantum-inspired algorithm for recommendation systems [STOC'19]. Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gilyen et al. [STOC'19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions. Our results give compelling evidence that in the corresponding QRAM data structure input model, quantum SVT does not yield exponential quantum speedups. Since the quantum SVT framework generalizes essentially all known techniques for quantum linear algebra, our results, combined with sampling lemmas from previous work, suffices to generalize all recent results about dequantizing quantum machine learning algorithms. In particular, our classical SVT framework recovers and often improves the dequantization results on recommendation systems, principal component analysis, supervised clustering, support vector machines, low-rank regression, and semidefinite program solving. We also give additional dequantization results on low-rank Hamiltonian simulation and discriminant analysis. Our improvements come from identifying the key feature of the quantum-inspired input model that is at the core of all prior quantum-inspired results: l(2)-norm sampling can approximate matrix products in time independent of their dimension. We reduce all our main results to this fact, making our exposition concise, self-contained, and intuitive.
The success of machine learning models over the last few years is mostly related to the significant progress of deep neural networks. These powerful and flexible models can even surpass human-level performance in task...
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The success of machine learning models over the last few years is mostly related to the significant progress of deep neural networks. These powerful and flexible models can even surpass human-level performance in tasks such as image recognition and strategy games. However, experts need to spend considerable time and resources to design the network structure. The demand for new architectures drives interest in automating this design process. Researchers have proposed new algorithms to address the neural architecture search (NAS) problem, including efforts to reduce the high computational cost of such methods. A common approach to improve efficiency is to reduce the search space with the help of expert knowledge, searching for cells rather than entire networks. Motivated by the faster convergence promoted by quantum-inspired evolutionary methods, the Q-NAS algorithm was proposed to address the NAS problem without relying on cell search. In this work, we consolidate Q-NAS, adding a new penalization feature, enhancing its retraining scheme, and also investigating more challenging search spaces than before. In CIFAR-10, we reached 93.85% of test accuracy in 67 GPU days, considering the addition of an early-stopping mechanism. We also applied Q-NAS to CIFAR-100, without modifying the parameters, and our best accuracy was 74.23%, which is comparable to ResNet164. The enhancements and results presented in this work show that Q-NAS can automatically generate network architectures that outperform hand-designed models for CIFAR-10 and CIFAR-100. Also, compared to other NAS methods, Q-NAS results are promising regarding the balance between performance, runtime efficiency, and automation. We believe that our results enrich the discussion on this balance, considering alternatives to the cell search approach. (C) 2022 Elsevier B.V. All rights reserved.
Concepts and formalism from acoustics are often used to exemplify quantum mechanics. Conversely, quantum mechanics could be used to achieve a new perspective on acoustics, as shown by Gabor studies. Here, we focus in ...
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Concepts and formalism from acoustics are often used to exemplify quantum mechanics. Conversely, quantum mechanics could be used to achieve a new perspective on acoustics, as shown by Gabor studies. Here, we focus in particular on the study of human voice, considered as a probe to investigate the world of sounds. We present a theoretical framework that is based on observables of vocal production, and on some measurement apparati that can be used both for analysis and synthesis. In analogy to the description of spin states of a particle, the quantum-mechanical formalism is used to describe the relations between the fundamental states associated with phonetic labels such as phonation, turbulence, and supraglottal myoelastic vibrations. The intermingling of these states, and their temporal evolution, can still be interpreted in the Fourier/Gabor plane, and effective extractors can be implemented. The bases for a quantum vocal theory of sound, with implications in sound analysis and design, are presented.
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