Zobrazeno 1 - 10
of 45
pro vyhledávání: '"TANG, EWIN"'
Autor:
Bakshi, Ainesh, Bostanci, John, Kretschmer, William, Landau, Zeph, Li, Jerry, Liu, Allen, O'Donnell, Ryan, Tang, Ewin
We study the problem of finding a product state with optimal fidelity to an unknown $n$-qubit quantum state $\rho$, given copies of $\rho$. This is a basic instance of a fundamental question in quantum learning: is it possible to efficiently learn a
Externí odkaz:
http://arxiv.org/abs/2411.04283
We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply $e^{-\mathrm{i} Ht}$ for an unknown local Hamiltonian $H = \sum_{a = 1}^m \lambda_a E_a$ on $n$ qubits, the goal is to recover $H$. This probl
Externí odkaz:
http://arxiv.org/abs/2405.00082
We show that thermal states of local Hamiltonians are separable above a constant temperature. Specifically, for a local Hamiltonian $H$ on a graph with degree $\mathfrak{d}$, its Gibbs state at inverse temperature $\beta$, denoted by $\rho =e^{-\beta
Externí odkaz:
http://arxiv.org/abs/2403.16850
We study the problem of learning a local quantum Hamiltonian $H$ given copies of its Gibbs state $\rho = e^{-\beta H}/\textrm{tr}(e^{-\beta H})$ at a known inverse temperature $\beta>0$. Anshu, Arunachalam, Kuwahara, and Soleimanifar (arXiv:2004.0726
Externí odkaz:
http://arxiv.org/abs/2310.02243
Clustering is one of the most important tools for analysis of large datasets, and perhaps the most popular clustering algorithm is Lloyd's iteration for $k$-means. This iteration takes $N$ vectors $v_1,\dots,v_N\in\mathbb{R}^d$ and outputs $k$ centro
Externí odkaz:
http://arxiv.org/abs/2308.09701
Autor:
Bakshi, Ainesh, Tang, Ewin
We study quantum speedups in quantum machine learning (QML) by analyzing the quantum singular value transformation (QSVT) framework. QSVT, introduced by [GSLW, STOC'19, arXiv:1806.01838], unifies all major types of quantum speedup; in particular, a w
Externí odkaz:
http://arxiv.org/abs/2303.01492
Autor:
Tang, Ewin, Tian, Kevin
We present a simplified exposition of some pieces of [Gily\'en, Su, Low, and Wiebe, STOC'19, arXiv:1806.01838], which introduced a quantum singular value transformation (QSVT) framework for applying polynomial functions to block-encoded matrices. The
Externí odkaz:
http://arxiv.org/abs/2302.14324
Publikováno v:
2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS), Santa Cruz, CA, USA, 2023, pp. 363-390
We consider process tomography for unitary quantum channels. Given access to an unknown unitary channel acting on a $\textsf{d}$-dimensional qudit, we aim to output a classical description of a unitary that is $\varepsilon$-close to the unknown unita
Externí odkaz:
http://arxiv.org/abs/2302.14066
We study the problem of learning a Hamiltonian $H$ to precision $\varepsilon$, supposing we are given copies of its Gibbs state $\rho=\exp(-\beta H)/\operatorname{Tr}(\exp(-\beta H))$ at a known inverse temperature $\beta$. Anshu, Arunachalam, Kuwaha
Externí odkaz:
http://arxiv.org/abs/2108.04842
Publikováno v:
Quantum 6, 754 (2022)
We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09, arXiv:0811.3171] for low-rank matrices [Wossnig, Zhao, and Prakash, Physical Review Lett
Externí odkaz:
http://arxiv.org/abs/2009.07268