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Quantum Information Processing
Article . 2002 . Peer-reviewed
License: Springer Nature TDM
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2001
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Path Integration on a Quantum Computer

Authors: Joseph F. Traub; Henryk Woźniakowski;

Path Integration on a Quantum Computer

Abstract

We study path integration on a quantum computer that performs quantum summation. We assume that the measure of path integration is Gaussian, with the eigenvalues of its covariance operator of order j^{-k} with k>1. For the Wiener measure occurring in many applications we have k=2. We want to compute an $\e$-approximation to path integrals whose integrands are at least Lipschitz. We prove: 1. Path integration on a quantum computer is tractable. 2. Path integration on a quantum computer can be solved roughly $\e^{-1}$ times faster than on a classical computer using randomization, and exponentially faster than on a classical computer with a worst case assurance. 3.The number of quantum queries is the square root of the number of function values needed on a classical computer using randomization. More precisely, the number of quantum queries is at most $4.22 \e^{-1}$. Furthermore, a lower bound is obtained for the minimal number of quantum queries which shows that this bound cannot be significantly improved. 4.The number of qubits is polynomial in $\e^{-1}$. Furthermore, for the Wiener measure the degree is 2 for Lipschitz functions, and the degree is 1 for smoother integrands.

24 pages; Revision of 9/2/02 includes a query lower bound and the upper bound of $4.22 \e^{-1}$ to compute an $\e$-approximation to a path integral

Keywords

Quantum Physics, FOS: Physical sciences, Quantum Physics (quant-ph)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Average
Top 10%
Top 10%
Green