{"title":"量子核化二进制分类的二次方提速","authors":"Jungyun Lee, Daniel K. Park","doi":"10.1002/qute.202400126","DOIUrl":null,"url":null,"abstract":"<p>Classification is at the core of data-driven prediction and decision-making, representing a fundamental task in supervised machine learning. Recently, several quantum machine learning algorithms that use quantum kernels as a measure of similarities between data have emerged to perform binary classification on datasets encoded as quantum states. The potential advantages of quantum kernels arise from the ability of quantum computers to construct kernels that are more effective than their classical counterparts in capturing patterns in data or computing kernels more efficiently. However, existing quantum kernel-based classification algorithms do not harness the capability of having data samples in quantum superposition for additional enhancements. This work demonstrates how such capability can be leveraged in quantum kernelized binary classifiers (QKCs) through Quantum Amplitude Estimation (QAE) for quadratic speed-up. Additionally, new quantum circuits are proposed for the QKCs in which the number of qubits is reduced by one, and the circuit depth is reduced linearly with respect to the number of sample data. The quadratic speed-up over previous methods is verified through numerical simulations on the Iris dataset.</p>","PeriodicalId":72073,"journal":{"name":"Advanced quantum technologies","volume":"7 8","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/qute.202400126","citationCount":"0","resultStr":"{\"title\":\"Quadratic Speed-ups in Quantum Kernelized Binary Classification\",\"authors\":\"Jungyun Lee, Daniel K. Park\",\"doi\":\"10.1002/qute.202400126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Classification is at the core of data-driven prediction and decision-making, representing a fundamental task in supervised machine learning. Recently, several quantum machine learning algorithms that use quantum kernels as a measure of similarities between data have emerged to perform binary classification on datasets encoded as quantum states. The potential advantages of quantum kernels arise from the ability of quantum computers to construct kernels that are more effective than their classical counterparts in capturing patterns in data or computing kernels more efficiently. However, existing quantum kernel-based classification algorithms do not harness the capability of having data samples in quantum superposition for additional enhancements. This work demonstrates how such capability can be leveraged in quantum kernelized binary classifiers (QKCs) through Quantum Amplitude Estimation (QAE) for quadratic speed-up. Additionally, new quantum circuits are proposed for the QKCs in which the number of qubits is reduced by one, and the circuit depth is reduced linearly with respect to the number of sample data. The quadratic speed-up over previous methods is verified through numerical simulations on the Iris dataset.</p>\",\"PeriodicalId\":72073,\"journal\":{\"name\":\"Advanced quantum technologies\",\"volume\":\"7 8\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/qute.202400126\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced quantum technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/qute.202400126\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced quantum technologies","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/qute.202400126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
Quadratic Speed-ups in Quantum Kernelized Binary Classification
Classification is at the core of data-driven prediction and decision-making, representing a fundamental task in supervised machine learning. Recently, several quantum machine learning algorithms that use quantum kernels as a measure of similarities between data have emerged to perform binary classification on datasets encoded as quantum states. The potential advantages of quantum kernels arise from the ability of quantum computers to construct kernels that are more effective than their classical counterparts in capturing patterns in data or computing kernels more efficiently. However, existing quantum kernel-based classification algorithms do not harness the capability of having data samples in quantum superposition for additional enhancements. This work demonstrates how such capability can be leveraged in quantum kernelized binary classifiers (QKCs) through Quantum Amplitude Estimation (QAE) for quadratic speed-up. Additionally, new quantum circuits are proposed for the QKCs in which the number of qubits is reduced by one, and the circuit depth is reduced linearly with respect to the number of sample data. The quadratic speed-up over previous methods is verified through numerical simulations on the Iris dataset.