no code implementations • 3 Oct 2023 • Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang
Anshu, Arunachalam, Kuwahara, and Soleimanifar (arXiv:2004. 07266) gave an algorithm to learn a Hamiltonian on $n$ qubits to precision $\epsilon$ with only polynomially many copies of the Gibbs state, but which takes exponential time.
no code implementations • 18 Aug 2023 • João F. Doriguello, Alessandro Luongo, Ewin Tang
The time complexity is $O\big(\frac{k^{2}}{\varepsilon^2}(\sqrt{k}d + \log(Nd))\big)$ and maintains the polylogarithmic dependence on $N$ while improving the dependence on most of the other parameters.
no code implementations • 10 Aug 2021 • Jeongwan Haah, Robin Kothari, Ewin Tang
In the appendix, we show that virtually the same algorithm can be used to learn $H$ from a real-time evolution unitary $e^{-it H}$ in a small $t$ regime with similar sample and time complexity.
no code implementations • 14 Oct 2019 • Nai-Hui Chia, András Gilyén, Tongyang Li, Han-Hsuan Lin, Ewin Tang, Chunhao Wang
Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gily\'en, Su, Low, and Wiebe [STOC'19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions.
no code implementations • 31 Oct 2018 • Ewin Tang
A central roadblock to analyzing quantum algorithms on quantum states is the lack of a comparable input model for classical algorithms.
1 code implementation • 10 Jul 2018 • Ewin Tang
We give a classical analogue to Kerenidis and Prakash's quantum recommendation system, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning.