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Understanding the Quantum Computational Speed-up via De-quantisation

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arxiv 1006.1419 v1 pith:ZWHSI7Z3 submitted 2010-06-08 quant-ph cs.CC

classification quant-phcs.CC
keywords algorithmsquantumclassicalalgorithmde-quantisationmethodsspeed-upwell
verification ladder T0 review T1 audit T2 compute T3 formal
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While it seems possible that quantum computers may allow for algorithms offering a computational speed-up over classical algorithms for some problems, the issue is poorly understood. We explore this computational speed-up by investigating the ability to de-quantise quantum algorithms into classical simulations of the algorithms which are as efficient in both time and space as the original quantum algorithms. The process of de-quantisation helps formulate conditions to determine if a quantum algorithm provides a real speed-up over classical algorithms. These conditions can be used to develop new quantum algorithms more effectively (by avoiding features that could allow the algorithm to be efficiently classically simulated), as well as providing the potential to create new classical algorithms (by using features which have proved valuable for quantum algorithms). Results on many different methods of de-quantisations are presented, as well as a general formal definition of de-quantisation. De-quantisations employing higher-dimensional classical bits, as well as those using matrix-simulations, put emphasis on entanglement in quantum algorithms; a key result is that any algorithm in which the entanglement is bounded is de-quantisable. These methods are contrasted with the stabiliser formalism de-quantisations due to the Gottesman-Knill Theorem, as well as those which take advantage of the topology of the circuit for a quantum algorithm. The benefits of the different methods are contrasted, and the importance of a range of techniques is emphasised. We further discuss some features of quantum algorithms which current de-quantisation methods do not cover.

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    H2S and H3S, two fuzzy-clustering plus quantum-annealing hybrids, achieve 8-19 percent optimality gaps on five small VRPLib instances, with H3S favored on corner-depot instances and H2S on center-depot instances.

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