01362nas a2200157 4500008004100000245010600041210006900147260001500216300001400231490000700245520083800252100002301090700001901113700002301132856004901155 2017 eng d00aQuantum algorithm for systems of linear equations with exponentially improved dependence on precision0 aQuantum algorithm for systems of linear equations with exponenti c2017/12/21 a1920-19500 v463 a
Harrow, Hassidim, and Lloyd showed that for a suitably specified N×N matrix A and N-dimensional vector b⃗ , there is a quantum algorithm that outputs a quantum state proportional to the solution of the linear system of equations Ax⃗ =b⃗ . If A is sparse and well-conditioned, their algorithm runs in time poly(logN,1/ϵ), where ϵ is the desired precision in the output state. We improve this to an algorithm whose running time is polynomial in log(1/ϵ), exponentially improving the dependence on precision while keeping essentially the same dependence on other parameters. Our algorithm is based on a general technique for implementing any operator with a suitable Fourier or Chebyshev series representation. This allows us to bypass the quantum phase estimation algorithm, whose dependence on ϵ is prohibitive.
1 aChilds, Andrew, M.1 aKothari, Robin1 aSomma, Rolando, D. uhttp://epubs.siam.org/doi/10.1137/16M108707201120nas a2200181 4500008004100000245006700041210006700108260001500175300001100190490000800201520058500209100002300794700002300817700001900840700001900859700002300878856003700901 2015 eng d00aSimulating Hamiltonian dynamics with a truncated Taylor series0 aSimulating Hamiltonian dynamics with a truncated Taylor series c2015/03/03 a0905020 v1143 a We describe a simple, efficient method for simulating Hamiltonian dynamics on a quantum computer by approximating the truncated Taylor series of the evolution operator. Our method can simulate the time evolution of a wide variety of physical systems. As in another recent algorithm, the cost of our method depends only logarithmically on the inverse of the desired precision, which is optimal. However, we simplify the algorithm and its analysis by using a method for implementing linear combinations of unitary operations to directly apply the truncated Taylor series. 1 aBerry, Dominic, W.1 aChilds, Andrew, M.1 aCleve, Richard1 aKothari, Robin1 aSomma, Rolando, D. uhttp://arxiv.org/abs/1412.4687v102001nas a2200181 4500008004100000020002200041245007600063210006900139260001500208300001200223520144000235100002301675700002301698700001901721700001901740700002301759856003701782 2014 eng d a978-1-4503-2710-700aExponential improvement in precision for simulating sparse Hamiltonians0 aExponential improvement in precision for simulating sparse Hamil c2014/05/31 a283-2923 a We provide a quantum algorithm for simulating the dynamics of sparse Hamiltonians with complexity sublogarithmic in the inverse error, an exponential improvement over previous methods. Specifically, we show that a $d$-sparse Hamiltonian $H$ acting on $n$ qubits can be simulated for time $t$ with precision $\epsilon$ using $O\big(\tau \frac{\log(\tau/\epsilon)}{\log\log(\tau/\epsilon)}\big)$ queries and $O\big(\tau \frac{\log^2(\tau/\epsilon)}{\log\log(\tau/\epsilon)}n\big)$ additional 2-qubit gates, where $\tau = d^2 \|{H}\|_{\max} t$. Unlike previous approaches based on product formulas, the query complexity is independent of the number of qubits acted on, and for time-varying Hamiltonians, the gate complexity is logarithmic in the norm of the derivative of the Hamiltonian. Our algorithm is based on a significantly improved simulation of the continuous- and fractional-query models using discrete quantum queries, showing that the former models are not much more powerful than the discrete model even for very small error. We also simplify the analysis of this conversion, avoiding the need for a complex fault correction procedure. Our simplification relies on a new form of "oblivious amplitude amplification" that can be applied even though the reflection about the input state is unavailable. Finally, we prove new lower bounds showing that our algorithms are optimal as a function of the error. 1 aBerry, Dominic, W.1 aChilds, Andrew, M.1 aCleve, Richard1 aKothari, Robin1 aSomma, Rolando, D. uhttp://arxiv.org/abs/1312.1414v2