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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1007/115603...
Part of book or chapter of book . 2005 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2017
Data sources: DBLP
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On Counterfactual Computation

Authors: Zuliani P;

On Counterfactual Computation

Abstract

In this paper we pursue two targets. First, showing that counterfactual computation can be rigorously formalised as a quantum computation. Second, presenting a new counterfactual protocol which improve previous protocols. Counterfactual computation makes use of quantum mechanics’ peculiarities to infer the outcome of a quantum computation without running that computation. In this paper, we first cast the definition of counterfactual protocol in the quantum programming language qGCL, thereby showing that counterfactual computation is an example of quantum computation. Next, we formalise in qGCL a probabilistic extension of counterfactual protocol for decision problems (whose result is either 0 or 1). If p$_{G}^{r}$denotes for protocol G the probability of obtaining result r “for free” (i.e. without running the quantum computer), then we show that for any probabilistic protocol p$_{G}^{\rm 0}$+ p$_{G}^{\rm 1}$≤ 1 (as for non-probabilistic protocols). Finally, we present a probabilistic protocol K which satisfies p$_{K}^{\rm 0}$+p$_{K}^{\rm 1}$=1, thus being optimal. Furthermore, the result is attained with a single insertion of the quantum computer, while it has been shown that a non-probabilistic protocol would obtain the result only in the limit (i.e. with an infinite number of insertions).

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selected citations
These citations are derived from selected sources.
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!
0
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