论文标题
使用量子退火与经典技术对深信仰网络培训的系统比较
Systematic comparison of deep belief network training using quantum annealing vs. classical techniques
论文作者
论文摘要
在这项工作中，我们对2015年的一项研究进行了重新访问和扩展，该研究使用D-Wave Quantum Nealealer作为采样引擎来协助培训深神经网络。最初的2015年结果是使用最新的D-Wave硬件复制的。我们将这种量子辅助训练方法与更广泛的经典技术进行了系统的比较，包括：对比度差异，具有不同的优化器选择；对比差异，步骤数量增加（CD-K）；和模拟退火（SA）。我们发现，量子辅助培训仍然优于基于Gibbs采样技术的CD。但是，SA能够使用像淬灭状的时间表在高温下进行一次扫描，然后在目标温度下进行一次扫描，从而符合量子辅助训练的性能。
In this work we revisit and expand on a 2015 study that used a D-Wave quantum annealer as a sampling engine to assist in the training of a Deep Neural Network. The original 2015 results were reproduced using more recent D-Wave hardware. We systematically compare this quantum-assisted training method to a wider range of classical techniques, including: Contrastive Divergence with a different choice of optimizer; Contrastive Divergence with an increased number of steps (CD-k); and Simulated Annealing (SA). We find that quantum-assisted training still outperforms the CD with Gibbs sampling-based techniques; however, SA is able to match the performance of quantum-assisted training trivially using a quench-like schedule with a single sweep at high temperature followed by one at the target temperature.
