2 research outputs found

    Efficient discrete-time simulations of continuous-time quantum query algorithms

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    The continuous-time query model is a variant of the discrete query model in which queries can be interleaved with known operations (called "driving operations") continuously in time. Interesting algorithms have been discovered in this model, such as an algorithm for evaluating nand trees more efficiently than any classical algorithm. Subsequent work has shown that there also exists an efficient algorithm for nand trees in the discrete query model; however, there is no efficient conversion known for continuous-time query algorithms for arbitrary problems. We show that any quantum algorithm in the continuous-time query model whose total query time is T can be simulated by a quantum algorithm in the discrete query model that makes O[T log(T) / log(log(T))] queries. This is the first upper bound that is independent of the driving operations (i.e., it holds even if the norm of the driving Hamiltonian is very large). A corollary is that any lower bound of T queries for a problem in the discrete-time query model immediately carries over to a lower bound of \Omega[T log(log(T))/log (T)] in the continuous-time query model.Comment: 12 pages, 6 fig

    Approximating Fractional Time Quantum Evolution

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    An algorithm is presented for approximating arbitrary powers of a black box unitary operation, Ut\mathcal{U}^t, where tt is a real number, and U\mathcal{U} is a black box implementing an unknown unitary. The complexity of this algorithm is calculated in terms of the number of calls to the black box, the errors in the approximation, and a certain `gap' parameter. For general U\mathcal{U} and large tt, one should apply U\mathcal{U} a total of ⌊t⌋\lfloor t \rfloor times followed by our procedure for approximating the fractional power Ut−⌊t⌋\mathcal{U}^{t-\lfloor t \rfloor}. An example is also given where for large integers tt this method is more efficient than direct application of tt copies of U\mathcal{U}. Further applications and related algorithms are also discussed.Comment: 13 pages, 2 figure
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