Discrete optimization using quantum annealing on sparse Ising models
Discrete optimization using quantum annealing on sparse Ising models
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DOI：
10.3389/fphy.2014.00056
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发表时间：
2014-01-01
影响因子：
3.1
通讯作者：
Roy, Aidan
中科院分区：
文献类型：
--
作者：
Bian, Zhengbing;Chudak, Fabian;Roy, Aidan
本文讨论使用量子退火解决离散优化问题的技术。可能影响计算的实际问题包括精度限制、有限温度、有限能量范围、稀疏连接性和少量量子位。为了解决这些问题，我们提出了一种寻找基态和第一激发态之间具有大经典间隙的能量表示的方法，将不兼容的模型映射到硬件的有效算法，以及对太大而无法适应硬件的问题使用分解方法。我们通过描述使用 D-Wave 量子硬件进行多达 1000 个变量的低密度奇偶校验解码的实验来验证该方法。
This paper discusses techniques for solving discrete optimization problems using quantum annealing. Practical issues likely to affect the computation include precision limitations, finite temperature, bounded energy range, sparse connectivity, and small numbers of qubits. To address these concerns we propose a way of finding energy representations with large classical gaps between ground and first excited states, efficient algorithms for mapping non-compatible (sing models into the hardware, and the use of decomposition methods for problems that are too large to fit in hardware. We validate the approach by describing experiments with D-Wave quantum hardware for low density parity check decoding with up to 1000 variables.