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Quantum Approximation on Some Classes of Multivarite Functions

Peixin Ye

Published Jan 1, 2008• 0 citations
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Abstract

We study the quantum query error of approximation of functions from anisotropic Sobolev class B(Wpr([0, 1]d)) and Holder-Nikolskii class B(Hpr([0, 1]d)) in the Lq([0, 1]d) norm for all 1 les p, q les infin. The results show that for the class B(Wpr([0, 1]d)) (r isin Nd) when ppr([0, 1]d)) and B(Wpr([0, 1]d)) (r isin R+d) when p ges q the optimal convergence rate is the same for all three settings.

DOI
10.1109/csse.2008.650

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We study the quantum query error of approximation of functions from anisotropic Sobolev class B(Wpr([0, 1]d)) and Holder-Nikolskii class B(Hpr([0, 1]d)) in the Lq([0, 1]d) norm for all 1 les p, q les infin. The results show that for the class B(Wpr([0, 1]d)) (r isin Nd) when ppr([0, 1]d)) and B(Wpr([0, 1]d)) (r isin R+d) when p ges q the optimal convergence rate is the same for all three settings.
We study the approximation of the integration of multivariate functions in the quantum model of computation. Using a new reduction approach we obtain a lower bound of the n-th minimal query error on anisotropic Sobolev classes B(Wpr([0, 1]d)), (r isin R+d). Then combining our previous results we determine the optimal bound of nth minimal query error for anisotropic Holder-Nikolskii class B(Hinfinr([0, 1]d)), (r isin R+d) and Sobolev class B(Wpr([0, 1]d)) (r isin Nd). The results show the quantum algorithms give speed up over classical algorithms.
We estimate the quantum query error of approximation to functions from the anisotropic Sobolev class B(Wpr([0,1]d)){\cal B}(W_p^{\bf r}([0,1]^d))B(Wpr​([0,1]d)) and the Hölder-Nikolskii class B(Hpr([0,1]d)){\cal B}(H_p^{\bf r}([0,1]^d))B(Hpr​([0,1]d)) in the Lq([0,1]d)L_q([0,1]^d)Lq​([0,1]d) norm for all 1≤p,q≤∞1 \leq p,q \leq \infty1≤p,q≤∞. It turns out that for the class B(Wpr([0,1]d)){\cal B}(W_p^{\bf r}([0,1]^d))B(Wpr​([0,1]d)) (r∈Nd)({\bf r}\in {\mathbb{N}^d})(r∈Nd), when p<qp \lt qp<q, the quantum algorithms can essentially improve the rate of convergence of classical deterministic and randomized algorithms; while for the class B(Hpr([0,1]d)){\cal B}(H_p^{\bf r}([0,1]^d))B(Hpr​([0,1]d)) and B(Wpr([0,1]d)){\cal B}(W_p^{\bf r}([0,1]^d))B(Wpr​([0,1]d)) (r∈R+d)({\bf r}\in {\mathbb{R}_+^d})(r∈R+d​), when p≥qp \geq qp≥q, the optimal convergence rate is the same for all three settings.
We study the approximation of functions from anisotropic and generalized Sobolev classes under Lq([0, 1]d) norm in the quantum model of computation. Based on the quantum algorithm for approximation of finite imbedding from LNPto LiNQ, we develop a quantum algorithm for approximating the imbedding from anisotropic Sobolev classes B(Wpr(|0, 1|d)) to Lq(|0, 1|d) space for all 1 les q,p les infin and prove their optimality. Our results show that for p < q the quantum model of computation can bring a speedup of roughly squaring the rate of classical deterministic and randomized settings.