[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118877-en":3,"doc-seo-118877-105":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},118877,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Blind Quantum Machine Learning with Quantum Bipartite Correlator","Blind Quantum Machine Learning for distributed quantum computing privacy is addressed by introducing new protocols built on the quantum bipartite correlator algorithm. The work targets confidentiality against untrusted computing nodes by reducing communication overhead while maintaining data privacy. Algorithm-specific privacy-preserving mechanisms are provided with low computational overhead, avoiding complex cryptographic techniques. Effectiveness is verified through detailed complexity and privacy analyses, supporting privacy-aware quantum machine learning with resource-efficient untrusted devices.","arXiv :2310 . 12893v1 [ quant-ph] 19 Oct 2023  \nBlind Quantum Machine Learning with Quantum Bipartite Correlator  \nChanghao Li, 1, 2, 3, ∗ Boning Li,2, 4 Omar Amer, 1 Ruslan Shaydulin, 1 Shouvanik Chakrabarti, 1 Guoqing Wang,2, 3 Haowei Xu,3 Hao Tang,5 Isidor Schoch,3 Niraj Kumar, 1 Charles Lim, 1 Ju Li,3, 5,† Paola Cappellaro,2, 3, 4,‡ and Marco Pistoia 1, §  \n1 Global Technology Applied Research, JPMorgan Chase, New York, NY 10017 USA  \n2 Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  \n3 Department of Nuclear Science and Engineering,  \nMassachusetts Institute of Technology, Cambridge, MA 02139, USA  \n4 Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  \n5 Department of Materials Science and Engineering,  \nMassachusetts Institute of Technology, Cambridge, MA 02139, USA  \nDistributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence ofuntrusted computing nodes. In this work, we introduce novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm. Our protocols have reduced communication overhead while preserving the privacy of data from untrusted parties. We introduce robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques. We then validate the effectiveness of the proposed protocols through complexity and privacy analysis. Our findings pave the way for advancements in distributed quantum computing, opening up new possibilities for privacy-aware machine learning applications in the era of quantum technologies.  \nI. INTRODUCTION  \nQuantum computation that leverages the principles of quantum mechanics has the potential to tackle problems that are beyond the reach of classical computers, revolutionizing fields ranging from cryptography [1] to finance [2] and drug discovery [3] . Distributed quantum computing has attracted a lot of attention in recent years [4–10] due to the rapid progress in quantum communication technologies. In distributed quantum computing, multiple quantum processors are connected over a network, enabling collaborative computation and resource sharing. This approach is crucial for scaling up quantum computing power and overcoming the limitations of individual quantum systems. Exploiting distributed quantum resources enables tackling larger and more computationally complex problems in domains such as optimization, simulation and quantum machine learning (QML) . QML is especially suitable for distributed computation due to the need to process large datasets.  \nPrivacy in distributed computing plays a vital role in ensuring the confidentiality and security of sensitive information processed by multiple parties. Distributed quantum computation involves sharing and transmitting of quantum states across multiple nodes, making it paramount to protect the privacy of data and prevent unauthorized access. Furthermore, in practice, addressing privacy concerns in distributed quantum computing  \n∗  \n†  \n‡  \n§  \n[changhao.li@jpmchase.com](changhao.li@jpmchase.com)[ ](changhao.li@jpmchase.com)[liju@mit.edu](liju@mit.edu)[ ](liju@mit.edu)[pcappell@mit.edu](pcappell@mit.edu)[ ](pcappell@mit.edu)[marco.pistoia@jpmchase.com](marco.pistoia@jpmchase.com)  \nis essential for facilitating applications in fields such as finance and healthcare, where preserving the privacy of sensitive data is of utmost importance.  \nA number of protocols have been proposed in recent years that aim to implement private distributed quantum computing. For example, blind quantum computing [11– 13] enables the client to execute a quantum computation using one or more remote quantum servers while keeping the structure of the computa","cbCaio5QhhRXMWac","https://ap.wps.com/l/cbCaio5QhhRXMWac","pdf",745169,1,11,"English","en",105,"# Introduction\n# Formalism","[{\"question\":\"What problem does the paper target in distributed quantum computing?\",\"answer\":\"It targets maintaining privacy of data when distributed computation uses untrusted quantum computing nodes, where sensitive quantum-state information must remain confidential.\"},{\"question\":\"How do the proposed blind quantum machine learning protocols work?\",\"answer\":\"They introduce blind distributed quantum machine learning protocols based on the quantum bipartite correlator algorithm, enabling communication-efficient inner product estimation while concealing data between client and server.\"},{\"question\":\"How is privacy and efficiency validated?\",\"answer\":\"The paper validates the protocols through complexity analysis and privacy analysis, including resource estimates for communication and computation costs and demonstrating reduced communication overhead.\"}]","Blind Quantum Machine Learning with Quantum Bipartite Correlator | 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problem does the paper target in distributed quantum computing?","Question",{"text":75,"@type":76},"It targets maintaining privacy of data when distributed computation uses untrusted quantum computing nodes, where sensitive quantum-state information must remain confidential.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the proposed blind quantum machine learning protocols work?",{"text":80,"@type":76},"They introduce blind distributed quantum machine learning protocols based on the quantum bipartite correlator algorithm, enabling communication-efficient inner product estimation while concealing data between client and server.",{"name":82,"@type":73,"acceptedAnswer":83},"How is privacy and efficiency validated?",{"text":84,"@type":76},"The paper validates the protocols through complexity analysis and privacy analysis, including resource estimates for communication and computation costs and demonstrating reduced communication 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