



01351nas a2200193   4500008004100000245011700041210006900158260001400227300000800241490000600249520075900255100001301014700001701027700001801044700002201062700002001084700001601104856003701120       2021                        eng d00aQuantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance0 aQuantumaccelerated multilevel Monte Carlo methods for stochastic  c6/22/2021  a4810 v53 a<p>Inspired by recent progress in quantum algorithms for ordinary and partial differential equations, we study quantum algorithms for stochastic differential equations (SDEs). Firstly we provide a quantum algorithm that gives a quadratic speed-up for multilevel Monte Carlo methods in a general setting. As applications, we apply it to compute expection values determined by classical solutions of SDEs, with improved dependence on precision. We demonstrate the use of this algorithm in a variety of applications arising in mathematical finance, such as the Black-Scholes and Local Volatility models, and Greeks. We also provide a quantum algorithm based on sublinear binomial sampling for the binomial option pricing model with the same improvement.</p>1 aAn, Dong1 aLinden, Noah1 aLiu, Jin-Peng1 aMontanaro, Ashley1 aShao, Changpeng1 aWang, Jiasu  uhttps://arxiv.org/abs/2012.0628301928nas a2200121   4500008004100000245006200041210006200103260001500165520155100180100002001731700001801751856003701769       2020                        eng d00aQuantum algorithms for the polynomial eigenvalue problems0 aQuantum algorithms for the polynomial eigenvalue problems  c10/28/20203 a<p>Polynomial eigenvalue problems (PEPs) arise in a variety of science and engineering applications, and many breakthroughs in the development of classical algorithms to solve PEPs have been made in the past decades. Here we attempt to solve PEPs in a quantum computer. Firstly, for generalized eigenvalue problems (GEPs) $A\x = \lambda B\x$ with A,B symmetric, and B positive definite, we give a quantum algorithm based on block-encoding and quantum phase estimation. In a more general case when B is invertible, B&minus;1A is diagonalizable and all the eigenvalues are real, we propose a quantum algorithm based on the Fourier spectral method to solve ordinary differential equations (ODEs). The inputs of our algorithms can be any desired states, and the outputs are superpositions of the eigenpairs. The complexities are polylog in the matrix size and linear in the precision. The dependence on precision is optimal. Secondly, we show that when B is singular, any quantum algorithm uses at least Ω(n&minus;&minus;&radic;) queries to compute the eigenvalues, where n is the matrix size. Thirdly, based on the linearization method and the connection between PEPs and higher-order ODEs, we provide two quantum algorithms to solve PEPs by extending the quantum algorithm for GEPs. We also give detailed complexity analysis of the algorithm for two special types of quadratic eigenvalue problems that are important in practice. Finally, under an extra assumption, we propose a quantum algorithm to solve PEPs when the eigenvalues are complex.</p>1 aShao, Changpeng1 aLiu, Jin-Peng  uhttps://arxiv.org/abs/2010.15027