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Feedback-Optimized Operations with Linear Ion Crystals

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 Added by Stefan Ulm
 Publication date 2009
  fields Physics
and research's language is English




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We report on transport operations with linear crystals of 40Ca+ ions by applying complex electric time-dependent potentials. For their control we use the information obtained from the ions fluorescence. We demonstrate that by means of this feedback technique, we can transport a predefined number of ions and also split and unify ion crystals. The feedback control allows for a robust scheme, compensating for experimental errors as it does not rely on a precisely known electrical modeling of the electric potentials in the ion trap beforehand. Our method allows us to generate a self-learning voltage ramp for the required process. With an experimental demonstration of a transport with more than 99.8 % success probability, this technique may facilitate the operation of a future ion based quantum processor.



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The solving of linear systems provides a rich area to investigate the use of nearer-term, noisy, intermediate-scale quantum computers. In this work, we discuss hybrid quantum-classical algorithms for skewed linear systems for over-determined and under-determined cases. Our input model is such that the columns or rows of the matrix defining the linear system are given via quantum circuits of poly-logarithmic depth and the number of circuits is much smaller than their Hilbert space dimension. Our algorithms have poly-logarithmic dependence on the dimension and polynomial dependence in other natural quantities. In addition, we present an algorithm for the special case of a factorized linear system with run time poly-logarithmic in the respective dimensions. At the core of these algorithms is the Hadamard test and in the second part of this paper we consider the optimization of the circuit depth of this test. Given an $n$-qubit and $d$-depth quantum circuit $mathcal{C}$, we can approximate $langle 0|mathcal{C}|0rangle$ using $(n + s)$ qubits and $Oleft(log s + dlog (n/s) + dright)$-depth quantum circuits, where $sleq n$. In comparison, the standard implementation requires $n+1$ qubits and $O(dn)$ depth. Lattice geometries underlie recent quantum supremacy experiments with superconducting devices. We also optimize the Hadamard test for an $(l_1times l_2)$ lattice with $l_1 times l_2 = n$, and can approximate $langle 0|mathcal{C} |0rangle$ with $(n + 1)$ qubits and $Oleft(d left(l_1 + l_2right)right)$-depth circuits. In comparison, the standard depth is $Oleft(d n^2right)$ in this setting. Both of our optimization methods are asymptotically tight in the case of one-depth quantum circuits $mathcal{C}$.
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