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Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions.

, , , and . NeurIPS, (2020)

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A quantum search decoder for natural language processing., , and . Quantum Mach. Intell., 3 (1): 1-24 (2021)Spectral sparsification of matrix inputs as a preprocessing step for quantum algorithms., and . CoRR, (2019)Quantum Worst-Case to Average-Case Reductions for All Linear Problems., , , , and . SODA, page 2535-2567. SIAM, (2024)Quantum Worst-Case to Average-Case Reductions for All Linear Problems., , , , and . CoRR, (2022)Fast Quantum Algorithm for Learning with Optimized Random Features., , , and . CoRR, (2020)Sublinear quantum algorithms for estimating von Neumann entropy., , and . CoRR, (2021)Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions., , , and . NeurIPS, (2020)A Quantum Search Decoder for Natural Language Processing., , and . CoRR, (2019)Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 39008-39034. PMLR, (2023)Information-theoretic generalization bounds for learning from quantum data., , , , and . CoRR, (2023)