Maximum-Entropy Inference with a Programmable Annealer.
Maximum-Entropy Inference with a Programmable Annealer.
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DOI：
10.1038/srep22318
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发表时间：
2016-03-03
影响因子：
4.6
通讯作者：
Warburton PA
中科院分区：
文献类型：
--
作者：
Chancellor N;Szoke S;Vinci W;Aeppli G;Warburton PA
优化问题通常涉及找到关于许多变量的成本函数的基态（即最小能量配置）。如果变量被噪声破坏，那么这将最大化解决方案正确的可能性。另一方面，最大熵解采用在成本函数的基态和激发态上的玻尔兹曼分布的形式，以校正噪声。在这里，我们使用一个可编程退火的信息解码问题，我们模拟作为一个随机伊辛模型在一个字段。我们的实验表明，有限温度最大熵解码可以得到比最大似然方法稍好的误码率，确认有用的信息可以从退火机的激发态中提取。此外，我们介绍了一个逐位的分析方法，这是不可知的具体应用，并使用它来显示，退火样品从一个高度玻尔兹曼分布。因此，这种机器是用于利用最大熵推理的各种机器学习应用的候选者，包括语言处理和图像识别。
Optimisation problems typically involve finding the ground state (i.e. the minimum energy configuration) of a cost function with respect to many variables. If the variables are corrupted by noise then this maximises the likelihood that the solution is correct. The maximum entropy solution on the other hand takes the form of a Boltzmann distribution over the ground and excited states of the cost function to correct for noise. Here we use a programmable annealer for the information decoding problem which we simulate as a random Ising model in a field. We show experimentally that finite temperature maximum entropy decoding can give slightly better bit-error-rates than the maximum likelihood approach, confirming that useful information can be extracted from the excited states of the annealer. Furthermore we introduce a bit-by-bit analytical method which is agnostic to the specific application and use it to show that the annealer samples from a highly Boltzmann-like distribution. Machines of this kind are therefore candidates for use in a variety of machine learning applications which exploit maximum entropy inference, including language processing and image recognition.