[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118919-en":3,"doc-seo-118919-105":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},118919,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Towards Provably Efficient Quantum Algorithms for Large-Scale Machine-Learning Models - Research Article Summary","Large-scale machine learning models require substantial computation, energy, and time during both pre-training and fine-tuning, creating pressure to improve efficiency and reduce environmental impact. The work shows that fault-tolerant quantum computing can achieve provably efficient resolutions for generic (stochastic) gradient descent under conditions such as sufficient dissipation, sparsity, and small learning rates. Using related algorithms for dissipative differential equations, the authors prove scaling with model size and training iterations, and benchmark sparse-training regimes to motivate a practical sparse download and re-upload scheme.","Article [https://doi.org/10.1038/s41467-023-43957-x](https://doi.org/10.1038/s41467-023-43957-x)  \nTowards provably efficient quantum algorithms for large-scale machinelearning models  \nReceived: 25 April 2023  \n\n| Accepted: 24 November 2023 |\n| --- |\n|  |\n| Check for updates |\n\nJunyu Liu 1,2,3,4,5,6, Minzhao Liu 7,8, Jin-Peng Liu 9,10,11, Ziyu Ye2, Yunfei Wang12, Yuri Alexeev2,3,8, Jens Eisert 13  & Liang Jiang 1,3  \nLarge machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as OðT2 × polylogðnÞÞ, where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible atthe early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.  \nIt is widely believed that large-scale machine learning might be one of the most revolutionary technologies benefiting society1, including already important breakthroughs in digital arts2, conversation like GPT-33,4, and mathematical problem solving5. However, training such models with considerable parameters is costly and has high carbon emissions. For instance, twelve million dollars and over five-hundred tons of CO2 equivalent emissions have been produced to train GPT-36. Thus, on the one hand, it is important to make large-scale machinelearning models (like large language models, LLM) sustainable andefficient.  \nOn the other hand, machine learning might possibly be one of the flag applications of quantum technology. Running machine learning algorithms on quantum devices, implementing readings of so-called quantum machine learning, is widely seen as a potentially very fruitful application of quantum algorithms7. Specifically, many quantum approaches are proposed to enhance the capability of classical machine learning and hopefully find some useful applications, like8,9. Despite rapid development and significant progress, current quantum machine learning algorithms feature substantial limitations both in theory and practice. First, practical applications of quantum machine  \n1Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, USA. 2Department of Computer Science, The University of Chicago, Chicago, IL60637, USA. 3Chicago Quantum Exchange, Chicago, IL60637, USA. 4Kadanoff Center for Theoretical Physics, The University of Chicago, Chicago, IL 60637, USA. 5qBraid Co., Chicago, IL 60615, USA. 6SeQure, Chicago, IL 60615, USA. 7Department of Physics, The University of Chicago, Chicago, IL 60637, USA. 8Computational Science Division, Argonne National Laboratory, Lemont, IL 60439, USA. 9Simons Institute for the Theory of Computing, University of California, Berkeley, CA 94720, USA. 10Department of Mathematics, University of California, Berkeley, CA 94720, USA. 11Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. 12Martin A. Fisher School of Physics, Brandeis University, Waltham, MA 02453, USA. 13Dahlem Center for Complex Quantum Systems, Free University Berlin, Berlin 14195, Germany. [e-mail: jense@zedat.fu-","cbCaim4U6fMJ0mKQ","https://ap.wps.com/l/cbCaim4U6fMJ0mKQ","pdf",777041,1,6,"English","en",105,"# Introduction\n# Quantum speedups for (stochastic) gradient descent\n## Conditions for provable efficiency\n## Relationship to quantum algorithms for dissipative dynamics\n# Benchmarking on large model instances\n## Sparse training and early-stage enhancement\n# Proposed sparse parameter download and re-upload scheme\n# Outlook for state-of-the-art large-scale machine learning","[{\"question\":\"What computational bottlenecks motivate this research on large-scale machine learning models?\",\"answer\":\"The paper highlights high computational expenses, energy, and time costs in both pre-training and fine-tuning, along with substantial carbon emissions.\"},{\"question\":\"What quantum setting and assumptions are used to obtain provably efficient performance?\",\"answer\":\"It relies on fault-tolerant quantum computing and requires models to be sufficiently dissipative and sparse, together with small learning rates.\"},{\"question\":\"How does the proposed approach relate to earlier efficient quantum algorithms?\",\"answer\":\"The work builds on quantum algorithms for dissipative differential equations and adapts similar methods to (stochastic) gradient descent, the core learning algorithm in machine learning.\"}]","Towards Provably Efficient Quantum Algorithms for Large-Scale Machine-Learning Models - 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