Quantum Version of the k‐NN Classifier Based on a Quantum Sorting Algorithm

Quezada, L. F.; Sun, Guo-Hua; Dong, Shi‐Hai

doi:10.1002/andp.202100449

Cited by 10 publications

(10 citation statements)

References 45 publications

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“…Our results are comparable to recent state-of-the-art quantum algorithms, see, for example, [38] with an accuracy of 95.82% and [29] which found an accuracy of 94.66%. The notable points of this work are as follows:…”

Section: Our Contributionsupporting

confidence: 85%

“…Another quantum version of the K-NN classifier is proposed in [29]. In this paper, the entire dataset was incorporated into a quantum circuit.…”

Section: Related Workmentioning

confidence: 99%

“…Our method computes the required distance metric based on the SWAP-Test, which offers a similarity metric for comparing two quantum states. We use Grover's algorithm [26,[29][30][31][32] to efficiently identify states with high similarity scores by utilizing an oracle. Quantum states from the labeled dataset are analyzed using the SWAP-Test [33,34] via a mid-circuit measurement.…”

Section: Introductionmentioning

confidence: 99%

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Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance

Maldonado-Romo,

Montiel-Pérez,

Onofre

et al. 2024

Mathematics

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This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with O(log(n)) space complexity. It incorporates Grover’s algorithm and the quantum SWAP-Test to identify similar states and determine the nearest neighbors with high probability, achieving Om search complexity, where m is the qubit address. We implement a simulation of the algorithm using IBM’s Qiskit with GPU support, applying it to the Iris and MNIST datasets with two different angle encodings. The experiments employ multiple QRAM cell sizes (8, 16, 32, 64, 128) and perform ten trials per size. According to the performance, accuracy values in the Iris dataset range from 89.3 ± 5.78% to 94.0 ± 1.56%. The MNIST dataset’s mean binary accuracy values range from 79.45 ± 18.84% to 94.00 ± 2.11% for classes 0 and 1. Additionally, a comparison of the results of this proposed approach with different state-of-the-art versions of QK-NN and the classical K-NN using Scikit-learn. This method achieves a 96.4 ± 2.22% accuracy in the Iris dataset. Finally, this proposal contributes an experimental result to the state of the art for the MNIST dataset, achieving an accuracy of 96.55 ± 2.00%. This work presents a new implementation proposal for QK-NN and conducts multiple experiments that yield more robust results than previous implementations. Although our average performance approaches still need to surpass the classic results, an experimental increase in the size of QRAM or the amount of data to encode is not achieved due to limitations. However, our results show promising improvement when considering working with more feature numbers and accommodating more data in the QRAM.

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Section: Our Contributionsupporting

confidence: 85%

“…Another quantum version of the K-NN classifier is proposed in [29]. In this paper, the entire dataset was incorporated into a quantum circuit.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance

Maldonado-Romo,

Montiel-Pérez,

Onofre

et al. 2024

Mathematics

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show abstract

“…The basic mechanism of the K-NN algorithm determines the data pattern by selecting the prevailing label within its set of knearest neighbors. 37,38 Since the K-NN algorithm is simple, self-adaptive, and highly accurate, it has been widely used in various sensors such as e-nose and colorimetric arrays. 7,39 We selected it to support the analysis of ammonia concentration to improve detection accuracy.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…The basic mechanism of the K -NN algorithm determines the data pattern by selecting the prevailing label within its set of k-nearest neighbors. , Since the K -NN algorithm is simple, self-adaptive, and highly accurate, it has been widely used in various sensors such as e-nose and colorimetric arrays. , We selected it to support the analysis of ammonia concentration to improve detection accuracy. First, 30 ammonia colorimetric images of six concentration categories were obtained and extracted by Color Grab as input data based on RGB color features, forming the data set for machine learning (Figure a).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis

Wang,

Zhou,

Luo

et al. 2023

Anal. Chem.

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Artificial olfactory systems have been widely used in medical fields such as in the analysis of volatile organic compounds (VOCs) in human exhaled breath. However, there is still an urgent demand for a portable, accurate breath VOC analysis system for the healthcare industry. In this work, we proposed a Janus colorimetric face mask (JCFM) for the comfortable evaluation of breath ammonia levels by combining the machine learning K-nearest neighbor (K-NN) algorithm. Such a Janus fabric is designed for the unidirectional penetration of exhaled moisture, which can reduce stickiness and ensure facial dryness and comfort. Four different pH indicators on the colorimetric array serve as recognition elements that cross-react with ammonia, capturing the optical fingerprint information on breath ammonia by mimicking the sophisticated olfactory structure of mammals. The Euclidean distance (ED) is used to quantitatively describe the ammonia concentration between 1 ppm and 10 ppm, indicating that there is a linear relationship between the ammonia concentration and the ED response (R 2 = 0.988). The K-NN algorithm based on RGB response features aids in the analysis of the target ammonia level and achieves a prediction accuracy of 96%. This study integrates colorimetry, Janus design, and machine learning to present a wearable and portable sensing system for breath ammonia analysis.

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Quantum K‐Nearest Neighbor Classification Algorithm via a Divide‐and‐Conquer Strategy

Gong,

Ding,

et al. 2024

Adv Quantum Tech

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The K‐nearest neighbor algorithm is one of the most frequently applied supervised machine learning algorithms. Similarity computing is considered to be the most crucial and time‐consuming step among the classical K‐nearest neighbor algorithm. A quantum K‐nearest neighbor algorithm is proposed based on the divide‐and‐conquer strategy. A quantum circuit is designed to calculate the fidelity between the test sample and each feature vector of the training dataset. The quantum K‐nearest neighbor algorithm has higher classification efficiency in high‐dimensional data processing. The classification accuracy of the proposed algorithm is equivalent to that of the classical K‐nearest neighbor algorithm under the IRIS dataset. In addition, compared with the typical quantum K‐nearest neighbor algorithms, the proposed classification method possesses higher classification accuracy with less calculation time, which has wide applications in the industrial field.

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Quantum Version of the k‐NN Classifier Based on a Quantum Sorting Algorithm

Cited by 10 publications

References 45 publications

Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance

Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance

Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis

Quantum K‐Nearest Neighbor Classification Algorithm via a Divide‐and‐Conquer Strategy

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