Compact quantum kernel-based binary classifier

Blank, Carsten; Silva, Adenilton J. da; Albuquerque, Lucas P. de; Petruccione, Francesco; Park, Daniel K.

doi:10.1088/2058-9565/ac7ba3

Cited by 14 publications

(8 citation statements)

References 49 publications

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“…Applying quantum machine learning to classical data requires an initial step that encodes the classical data into a quantum state. For this, various approaches exist, such as the amplitude encoding, angle encoding, Hamiltonian encoding, and trainable quantum encoding [8,[44][45][46][47][48][49][50]. In this simulation, we modify the instantaneous quantum polynomial encoding that utilizes Ising Hamiltoinan, motivated by Havlíc ˇek et al [49], to map a four-dimensional data point into a two-qubit state.…”

Section: 150} Each Class Has the Same Number Of Data Points (50 Per C...mentioning

confidence: 99%

Variational quantum state discriminator for supervised machine learning

Lee,

Baek,

Huh

et al. 2023

Quantum Sci. Technol.

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Quantum state discrimination (QSD) is a fundamental task in quantum information processing with numerous applications. We present a variational quantum algorithm that performs the minimum-error QSD, called the variational quantum state discriminator (VQSD). The VQSD uses a parameterized quantum circuit that is trained by minimizing a cost function derived from the QSD, and finds the optimal positive-operator valued measure (POVM) for distinguishing target quantum states. The VQSD is capable of discriminating even unknown states, eliminating the need for expensive quantum state tomography. Our numerical simulations and comparisons with semidefinite programming demonstrate the effectiveness of the VQSD in finding optimal POVMs for minimum-error QSD of both pure and mixed states. In addition, the VQSD can be utilized as a supervised machine learning algorithm for multi-class classification. The area under the receiver operating characteristic curve obtained in numerical simulations with the Iris flower dataset ranges from 0.97 to 1 with an average of 0.985, demonstrating excellent performance of the VQSD classifier.

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Section: 150} Each Class Has the Same Number Of Data Points (50 Per C...mentioning

confidence: 99%

Variational quantum state discriminator for supervised machine learning

Lee,

Baek,

Huh

et al. 2023

Quantum Sci. Technol.

Self Cite

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“…The ability of quantum computing to efficiently manipulate exponentially large quantum space enables the fast evaluation of the kernel function more efficiently than classical computers. Blank et al [ 169 ] presented a compact quantum circuit for constructing a kernel-based binary classifier. Their model incorporated compact amplitude encoding of real-valued data, which reduced the number of qubits by two and linearly reduced the number of training steps.…”

Section: Machine Learning Researchmentioning

confidence: 99%

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Ezugwu

Oyelade

Ikotun

et al. 2023

Arch Computat Methods Eng

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The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa’s most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

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“…[1][2][3][4] One of the foundations for such quantum advantages is the ability to efficiently form and manipulate data in a large quantum feature space, especially with kernel functions used in classification and other classes of machine learning. [5][6][7][8][9][10][11][12][13][14] A recent study has realized a quantum kernel-based classifier model simply with a SWAP test algorithm, 10,11 which opened up a new class of quantum machine learning with a non-linear feature-space mapping. In this article, we enhance a quantum SWAP test classifier (STC) to realize the mathematical model for the maximum-margin property of SVM, which is achieved by a variational classical-quantum hybrid algorithm to render an approximate quantum state of support vectors.…”

Section: Introductionmentioning

confidence: 99%

Variational Quantum Approximate Support Vector Machine With Inference Transfer

Park

Rhee

2022

Preprint

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A kernel-based quantum classifier is the most interesting and powerful quantum machine learning technique for hyperlinear classification of complex data, which can be easily realized in shallow-depth quantum circuits such as a SWAP test classifier. A variational quantum approximate support vector machine (VQASVM) can be realized inherently and explicitly on these circuits by introduction of a variational scheme to map the quadratic optimization problem of the support vector machine theory to a quantum-classical variational optimization problem. Probability weight modulation in index qubits of a classifier can designate support vectors among training vectors, which can be achieved with a parameterized quantum circuit (PQC). The classical parameters of PQC is then transferred to many copies of other decision inference circuits. Our VQASVM algorithm is experimented with toy example data sets on cloud-based quantum machines for feasibility evaluation, and numerically investigated to evaluate its performance on a standard iris flower and MNIST data set. The empirical run-time complexity of VQASVM is estimated to be sub-quadratic on the training data set size, while that of the classical solver is quadratic.

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Compact quantum kernel-based binary classifier

Cited by 14 publications

References 49 publications

Variational quantum state discriminator for supervised machine learning

Variational quantum state discriminator for supervised machine learning

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Variational Quantum Approximate Support Vector Machine With Inference Transfer

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