Quantum singular value transformation is a framework for designing quantum algorithms. It encompasses a variety of quantum algorithms for problems which can be solved with linear algebra, including Hamiltonian simulation, search problems, and linear system solving.[1][2][3] It was introduced in 2018 by András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe, generalizing algorithms for Hamiltonian simulation of Guang Hao Low and Isaac Chuang inspired by signal processing.[4]
^Gilyén, András; Su, Yuan; Low, Guang Hao; Wiebe, Nathan (June 2019). Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics. STOC 2019. ACM. pp. 193–204. doi:10.1145/3313276.3316366. ISBN 978-1-4503-6705-9.
^Martyn, John M.; Rossi, Zane M; Tan, Andrew K.; Chuang, Isaac L. (2021). "Grand Unification of Quantum Algorithms". PRX Quantum. 2 (4). American Physical Society: 040203. arXiv:2105.02859. Bibcode:2021PRXQ....2d0203M. doi:10.1103/PRXQuantum.2.040203.
^Arrazola, Juan Miguel (2023-05-23). "Intro to QSVT". PennyLane Demos.
^Low, Guang Hao; Chuang, Isaac (2017). "Optimal Hamiltonian Simulation by Quantum Signal Processing". Physical Review Letters. 118 (1): 010501. arXiv:1606.02685. Bibcode:2017PhRvL.118a0501L. doi:10.1103/PhysRevLett.118.010501. PMID 28106413. S2CID 1118993.
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