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首页> 外文会议>IEEE International Conference on Quantum Computing and Engineering >Advanced anneal paths for improved quantum annealing
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Advanced anneal paths for improved quantum annealing

机译：先进的退火路径可改善量子退火

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Advances in quantum annealing technology make it possible to obtain high quality approximate solutions of important NP-hard problems. With the newer generations of the D-Wave annealer, more advanced features are available which allow the user to have greater control of the anneal process. In this contribution, we study how such features can help in improving the quality of the solutions returned by the annealer. Specifically, we focus on two of these features: reverse annealing and h-gain. Reverse annealing (RA) was designed to allow refining a known solution by backward annealing from a classical state representing the solution to a mid-anneal point where a transverse field is present, followed by an ordinary forward anneal, which is hoped to improve on the previous solution. The h-gain (HG) feature stands for time-dependent gain in Hamiltonian linear (h) biases and was originally developed to help study freezeout times and phase transitions in spin glasses. Here we apply HG to bias the quantum state in the beginning of the annealing process towards the known solution as in the RA case, but using a different apparatus. We also investigate a hybrid reverse annealing/h-gain schedule, which has a backward phase resembling an RA step and whose forward phase uses the HG idea. To optimize the parameters of the schedules, we employ a Bayesian optimization framework. We test all techniques on a variety of input problems including the weighted Maximum Cut problem and the weighted Maximum Clique problem. Our results show that each technique may dominate the others depending on the input instance, and that the HG technique is a viable alternative to RA for some problems.
机译：量子退火技术的进步使得有可能获得重要的NP难题的高质量近似解。随着新一代D-Wave退火炉的推出，可以使用更多高级功能，这些功能使用户可以更好地控制退火过程。在这项贡献中，我们研究了这些功能如何帮助提高退火炉返回的解决方案的质量。具体来说，我们重点关注以下两个功能：反向退火和h增益。反向退火（RA）旨在通过从代表溶液的经典状态向存在横向磁场的中间退火点进行反向退火，然后进行普通的正向退火，从而对已知的溶液进行精炼，以期改善这种情况。先前的解决方案。 h增益（HG）功能代表哈密顿线性（h）偏差中随时间变化的增益，最初是为了帮助研究旋转玻璃中的冻结时间和相变而开发的。在这里，我们采用HG来将退火过程开始时的量子态偏向已知的解，如在RA情况下一样，但是使用的是不同的设备。我们还研究了混合反向退火/ h增益计划，该计划具有类似于RA步骤的后向阶段，其前向阶段使用HG思想。为了优化计划表的参数，我们采用了贝叶斯优化框架。我们在各种输入问题上测试了所有技术，包括加权的最大割问题和加权的最大集团问题。我们的结果表明，根据输入实例的不同，每种技术可能会主导其他技术，并且对于某些问题，HG技术是RA的可行替代方案。

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