[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118942-en":3,"doc-seo-118942-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118942,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","The Power of One Clean Qubit in Supervised Machine Learning","The paper investigates quantum coherence and quantum discord as resources within the deterministic quantum computing with one qubit (DQC1) non-universal model for supervised machine learning. It presents an efficient approach for estimating complex kernel functions using DQC1, revealing a direct relationship between coherence consumption and the kernel function. A binary classification experiment is implemented on IBM quantum hardware, with analysis of how quantum coherence and hardware noise affect performance. The proposal leverages discord because it is more noise-resilient than entanglement.","The Power of One Clean Qubit in Supervised Machine Learning  \narXiv :2210 .09275v4 [ quant-ph] 7 Nov 2023  \nMahsa Karimi, 1, 2, ∗ Ali Javadi-Abhari,3 Christoph Simon, 1, 2 and Roohollah Ghobadi 1, 2,†  \n1 Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4, Canada  \n2 Institute for Quantum Science and Technology,  \nUniversity of Calgary, Calgary, AB, T2N 1N4, Canada  \n3 IBM Quantum, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA  \n(Dated: November 8, 2023)  \nThis paper explores the potential benefits of quantum coherence and quantum discord in thenon-universal quantum computing model called deterministic quantum computing with one qubit (DQC1) in supervised machine learning. We show that the DQC1 model can be leveraged to develop an efficient method for estimating complex kernel functions. We demonstrate a simple relationship between coherence consumption and the kernel function, a crucial element in machine learning. The paper presents an implementation of a binary classification problem on IBM hardware using the DQC1 model and analyzes the impact of quantum coherence and hardware noise. The advantage of our proposal lies in its utilization of quantum discord, which is more resilient to noise than entanglement.  \nINTRODUCTION  \nRecent progress in the control and mitigation of noise and decoherence has paved the way for the development of intermediate-scale quantum devices consisting of hundreds of qubits. Although these devices are currently not fault-tolerant, there is considerable evidence that they possess superior computational capabilities compared to classical supercomputers, as a result of their ability to support quantum entanglement [1, 2] . As quantum hardware continues to evolve, it is expected to playa crucial role in various fields such as quantum simulations, quantum chemistry, and quantum machine learning (QML) [3, 4] .  \nThe use of quantum hardware for complex computations such as kernel function estimation has been proposed as a way to achieve a quantum advantage in machine learning [5, 6] . Quantum entanglement is considered a key resource for this [7–9], but it is highly susceptible to noise, thus it is important to explore other forms of quantum correlation that are less sensitive to noise or require less entanglement.  \nThe Deterministic Quantum Computing with One Qubit (DQC1) model is a non-universal quantum computing model that leverages a single qubit as a probe to interact with a highly mixed quantum state and estimate computationally expensive functions. This ability is known as the “power of one qubit” [10] . The DQC1 model generates quantum discord, a resilient type of weak quantum correlation, using the coherence of a pure qubit [11, 12] . Quantum discord is more resistant to noise than entanglement and may offer a quantum advantage in noisy conditions for quantum illumination tasks [13] . There is a limited body of literature exploring the use of DQC1 in machine learning contexts [14–16] . Reference[14] investigates the advantages of DQC1 in addressing the parity learning problem. In reference [15], DQC1 is proposed for application in kernel based su-  \npervised machine learning. Finally, reference [16] builds upon the results in reference [15], extending the concept to multiple kernel learning for supervised machine learning within a DQC1 framework.  \nThis paper studies the use of the DQC1 model in supervised machine learning for efficient estimation of complex kernel functions. The study is implemented on IBM hardware and examines the effects of coherence consumption, quantum discord, and hardware noise. The DQC1 protocol reduces measurement errors by only measuring one qubit, achieving high classification accuracy despite requiring more gates than a similar protocol in [6] .  \nThe paper is structured as follows: Section provides areview of the DQC1 algorithm, quantum coherence and quantum discord, and a brief overview of kernel-based supervised machine","cbCaikOt2HukjeOy","https://ap.wps.com/l/cbCaikOt2HukjeOy","pdf",4741441,1,9,"English","en",105,"# Introduction\n## DQC1 model and quantum correlations\n# Preliminaries\n## DQC1 algorithm and quantum state preparation\n## Circuit evolution and measurement principle","[{\"question\":\"How does the DQC1 model help with supervised machine learning in this work?\",\"answer\":\"The DQC1 model uses a single (control) qubit to interact with a highly mixed register, enabling estimation of computationally expensive kernel functions needed for supervised learning tasks.\"},{\"question\":\"What is the relationship between coherence consumption and kernel estimation?\",\"answer\":\"The study demonstrates a simple correlation between how much quantum coherence is consumed and the value of the estimated kernel function, which is essential for machine learning.\"},{\"question\":\"Why does the proposal emphasize quantum discord instead of entanglement?\",\"answer\":\"Quantum discord is used because it is more resilient to noise than entanglement, offering better robustness in noisy hardware conditions for the proposed method.\"}]","The Power of One Clean Qubit in Supervised Machine Learning | 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does the DQC1 model help with supervised machine learning in this work?","Question",{"text":75,"@type":76},"The DQC1 model uses a single (control) qubit to interact with a highly mixed register, enabling estimation of computationally expensive kernel functions needed for supervised learning tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the relationship between coherence consumption and kernel estimation?",{"text":80,"@type":76},"The study demonstrates a simple correlation between how much quantum coherence is consumed and the value of the estimated kernel function, which is essential for machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the proposal emphasize quantum discord instead of entanglement?",{"text":84,"@type":76},"Quantum discord is used because it is more resilient to noise than entanglement, offering better robustness in noisy hardware conditions for the proposed 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