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As large language models (LLMs) demonstrate outstanding performance across various tasks, attention-driven models have profoundly transformed the field of machine learning.
A fast quantum mechanical algorithm for database search
Lov K Grover · 1996
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Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Peter W Shor · 1999
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Fast quantum algorithm for numerical gradient estimation
Stephen P Jordan · 2005
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Quantum algorithm for solving linear systems of equations
Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd · 2008
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Optimal halfspace range reporting in three dimensions
Peyman Afshani and Timothy M Chan · 2009
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Quantum algorithm for linear systems of equations
Aram W Harrow, Avinatan Hassidim, and Seth Lloyd · 2009
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A quantum approximate optimization algorithm, 2014
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2014
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A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien · 2014
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Quantum support vector machine for big data classification
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd · 2014
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Quantum sdp-solvers: Better upper and lower bounds
Joran van Apeldoorn, András Gilyén, Sander Gribling, and Ronald de Wolf · 2017
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Quantum speed-ups for solving semidefinite programs
Fernando GSL Brandao and Krysta M Svore · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension
András Gilyén, Seth Lloyd, and Ewin Tang · 2018
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The new legal landscape for text mining and machine learning
Matthew Sag · 2018
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Quantum-inspired classical algorithms for principal component analysis and supervised clustering
Ewin Tang · 2018
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Improvements in quantum sdp-solving with applications
Joran van Apeldoorn and András Gilyén · 2019
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Quantum sdp solvers: Large speed-ups, optimality, and applications to quantum learning
Fernando GSL Brandão, Amir Kalev, Tongyang Li, Cedric Yen-Yu Lin, Krysta M Svore, and Xiaodi Wu · 2019
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Quantum algorithm for estimating volumes of convex bodies
Shouvanik Chakrabarti, Andrew M Childs, Shih-Han Hung, Tongyang Li, Chunhao Wang, and Xiaodi Wu · 2019
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Quantum algorithms for deep convolutional neural networks
Iordanis Kerenidis, Jonas Landman, and Anupam Prakash · 2019
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A quantum-inspired classical algorithm for recommendation systems
Ewin Tang · 2019
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Quantum statistical query learning
Srinivasan Arunachalam, Alex B Grilo, and Henry Yuen · 2020
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Quantum algorithms for feedforward neural networks
Jonathan Allcock, Chang-Yu Hsieh, Iordanis Kerenidis, and Shengyu Zhang · 2020
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Quantum boosting
Srinivasan Arunachalam and Reevu Maity · 2020
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Training deep quantum neural networks
Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J Osborne, Robert Salzmann, Daniel Scheiermann, and Ramona Wolf · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Variational quantum algorithms, 2020
M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles · 2020
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Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning
Nai-Hui Chia, András Gilyén, Tongyang Li, Han-Hsuan Lin, Ewin Tang, and Chunhao Wang · 2020
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On the hardness of approximate and exact (bichromatic) maximum inner product
Lijie Chen · 2020
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2020
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Quantum speedups of optimizing approximately convex functions with applications to logarithmic regret stochastic convex bandits
Tongyang Li and Ruizhe Zhang · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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A survey on the complexity of learning quantum states
Anurag Anshu and Srinivasan Arunachalam · 2023
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Fast attention requires bounded entries
Josh Alman and Zhao Song · 2023
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Algorithm and hardness for dynamic attention maintenance in large language models
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Smyrf-efficient attention using asymmetric clustering
Giannis Daras, Nikita Kitaev, Augustus Odena, and Alexandros G Dimakis · 2020
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An improved quantum-inspired algorithm for linear regression
András Gilyén, Zhao Song, and Ewin Tang · 2020
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Small quantum computers and large classical data sets
Aram W Harrow · 2020
Cited alongside, same era.
Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya · 2020
Cited alongside, same era.
A quantum interior point method for lps and sdps
Iordanis Kerenidis and Anupam Prakash · 2020
Cited alongside, same era.
Soft threshold weight reparameterization for learnable sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Jan van den Brand, Zhao Song, and Tianyi Zhou · 2023
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Attention scheme inspired softmax regression
Yichuan Deng, Zhihang Li, and Zhao Song · 2023
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Randomized and deterministic attention sparsification algorithms for over-parameterized feature dimension
Yichuan Deng, Sridhar Mahadevan, and Zhao Song · 2023
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Differentially private continual releases of streaming frequency moment estimations
Alessandro Epasto, Jieming Mao, Andres Munoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, and Peilin Zhong · 2023
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An over-parameterized exponential regression
Yeqi Gao, Sridhar Mahadevan, and Zhao Song · 2023
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Yeqi Gao, Zhao Song, and Shenghao Xie · 2023
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Differentially private attention computation
Yeqi Gao, Zhao Song, and Xin Yang · 2023
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An iterative algorithm for rescaled hyperbolic functions regression
Yeqi Gao, Zhao Song, and Junze Yin · 2023
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A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein · 2023
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Towards provably efficient quantum algorithms for large-scale machine-learning models
Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yuri Alexeev, Jens Eisert, and Liang Jiang · 2023
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The closeness of in-context learning and weight shifting for softmax regression
Shuai Li, Zhao Song, Yu Xia, Tong Yu, and Tianyi Zhou · 2023
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Space-efficient interior point method, with applications to linear programming and maximum weight bipartite matching
S Cliff Liu, Zhao Song, Hengjie Zhang, Lichen Zhang, and Tianyi Zhou · 2023
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OpenAI · 2023
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Ritwik Sinha, Zhao Song, and Tianyi Zhou · 2023
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Provable copyright protection for generative models
Nikhil Vyas, Sham Kakade, and Boaz Barak · 2023
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Infoprompt: Information-theoretic soft prompt tuning for natural language understanding
Junda Wu, Tong Yu, Rui Wang, Zhao Song, Ruiyi Zhang, Handong Zhao, Chaochao Lu, Shuai Li, and Ricardo Henao · 2023
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Kdeformer: Accelerating transformers via kernel density estimation
Amir Zandieh, Insu Han, Majid Daliri, and Amin Karbasi · 2023
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H2o: Heavy-hitter oracle for efficient generative inference of large language models
Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark W. Barrett, Zhangyang Wang, and Beidi Chen · 2023
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Quantum linear algebra is all you need for transformer architectures
Naixu Guo, Zhan Yu, Matthew Choi, Aman Agrawal, Kouhei Nakaji, Alán Aspuru-Guzik, and Patrick Rebentrost · 2024
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Qsan: A near-term achievable quantum self-attention network
Jinjing Shi, Ren-Xin Zhao, Wenxuan Wang, Shichao Zhang, and Xuelong Li · 2024
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Quantum mixed-state self-attention network
Fu Chen, Qinglin Zhao, Li Feng, Chuangtao Chen, Yangbin Lin, and Jianhong Lin · 2025
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Sublinear time quantum algorithm for attention approximation
Zhao Song, Jianfei Xue, Jiahao Zhang, and Lichen Zhang · 2026
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